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Protocols and Criteria for Acoustic Emission Monitoring of Fracture

1. 0 0 00 000 0 oo Table 2 July 2014 criteria tabulation for South system oooo 0 0c 0 0 000 oo 0 0 00 000 0 0 8 1 2014 8 2 2014 8 3 2014 8 4 2014 8 5 2014 8 6 2014 8 7 2014 8 8 2014 8 9 2014 8 10 2014 8 11 2014 8 12 2014 8 13 2014 8 14 2014 8 15 2014 8 16 2014 8 17 2014 8 18 2014 8 19 2014 8 20 2014 8 21 2014 8 22 2014 8 23 2014 8 24 2014 8 25 2014 8 26 2014 8 27 2014 8 28 2014 8 29 2014 8 30 2014 8 31 2014 141108002147_0 141108104016_0 141110003421_0 141111010550_0 141111235610_0 141112001917_0 141112005625_0 141113001344_0 141114002853_0 141114074359 0 141115003701_0 140810034849_0 140810093534_0 140810143326_0 140810170612_0 140810195435_0 140811123459 0 140812085632_0 140814171111 0 140814181547 0 140814195811 0 140814202709 0 140814210535 0 140814220221 0 140815132803 0 140816134326 0 140817133423 0 140818134248 0 140819152355 0 140820154249 0 140821144344 0 140822140000 0 140822143326 0 140822150712 0 140822201740 0 140823131419 0 140824135030 0 140824152803 0 140824160540 0 140824180708 0 140825173142 0 140825181443 0 140825194936 0 1408271
2. 32 Figure 6 5 Hand pump connected to the jack just out of view to the top of picture 34 Figure 6 6 Fracture test results key aeo ie rod eed orb c um seed 35 Figure 7 1 South system data collection efficiency 41 Figure 7 2 North system data collection efficiency de eek 43 Figure 9 1 Cumulative number of hits versus time for the first data set a Low activity day b High activity day c Anomalous day qne n 52 Figure 9 2 Number of hits versus frequency centroid for the first data set a Low activity day b High activity day c Anomalous day ies ota ar nasus 54 Figure 9 3 Duration versus amplitude for the first data set a Low activity day b High activity day 1008 0 20 56 Figure 9 4 Maximum absolute energy versus amplitude for the first data set Low activity day b High activity day Anomalous day uide etr reete torta eee 58 Figure 9 5 Cumulative number hits versus time for the second data set in the north system a Low activity day b High activity day c Anomalous 62 Figure 9 6 Cumulative number of hits versus time for the second data set in the south system a Low acti
3. Hits vs Time sec 7 Hits vs Time sec 2 40 60 80 100 Figure A 41 Cumulative hits versus time 5 individual sensors A 22 Hits vs Freg Centroid kHz lt All Channels l I l 1 1 i i I 1 l l I I l I l l 100 110 120 130 140 150 160 170 180 190 200 210 220 230 240 250 260 270 280 Duration us vs Amplitude dB 6 3 Figure A 43 Duration us versus amplitude dB sensors 6 7 8 9 23 Absolute vs Amplitude dB lt 9 gt 1 0 006 9 5 005 9 0 005 8 5 005 8 0 005 7 5E 005 1 7 0 005 6 5 005 6 0 005 5 5E 005 1 5 0E 005 1 4 5E 005 1 4 0E 005 1 3 5 005 3 0 005 2 5E 005 2 0 005 1 5E 005 1 0 005 5 0E 004 1 0 0E 000 0 Figure A 44 Maximum absolute energy aJ versus amplitude dB sensors 6 7 8 9 Absolute vs Time sec 5 9 100000 95000 90000 85000 80000 75000 70000 65000 60000 55000 50000 45000 40000 35000 30000 25000 20000 15000 10000 5000 0 1 l l l 1 l 150 200 250 300 350 400 450 500 550 Figure A 45 Absolute energy rate aJ s during 86 second period including fracture sensors 6 7 8 9 A 24 Absolute Energy aJ vs lt 8 gt Absolute Energy aJ vs Time sec 5 Absolute Energy aJ vs Time sec 1
4. 5 19 w 2 S 500 Sw 00 8 nen sedes EAT ba Figure 4 4 Timing parameters used to define an individual hit 4 5 Characteristics of Acoustic Emission from Fracture Previous investigations performed with AE have revealed qualitative characteristics of acoustic emission AE data from fracture events The exact magnitudes of AE parameters from fracture data results will vary significantly depending on the geometry of the test specimen size of the fracture and placement of the sensors Therefore findings in the literature cannot be used to directly characterize fracture in the Cedar Avenue Bridge However the trends and concepts 14 discovered in previous experiments be used to determine the parameters that work best for characterizing AE data from fracture One of the most commonly reported AE characteristics of fracture is the presence of a high count rate Counts is probably the most basic parameter and has been used since the beginning of AE testing It has remained popular even with the development of more sophisticated signal processing Tests have shown that the AE count rate is proportional to the rate of crack growth Miller amp McIntire 1987 Sinclair Connors amp Formby 1977 A high number of counts is produced by failure modes including crack extension plastic deformation and fracture events within the plastic zone ahead
5. 1 10 0 1 I 1 I I 0 I 1 I 200 220 240 260 280 200 220 240 260 Hits vs Time sec 8 Hits vs Time sec 11 Hits vs Time sec lt 16 gt GFit 10 10 200 984 180 8 84 160 if 74 140 6 64 120 5 54 100 4 4 80 3 34 60 2 24 40 1 1 4 20 0 1 I I I 07 I 1 0 1 1 1 200 220 240 260 280 200 220 240 260 280 200 220 240 260 280 Figure A 33 Cumulative hits versus time s individual sensors A 18 Hits vs Freq Centroid kHz lt All Channels l 120 140 160 190 200 220 240 260 280 Duration us vs Amplitude dB 8 11 Absolute Eneray aJ vs 8 11 200000 1300005 18000043 1700005 1600005 150000 140000 130000 120000 110000 100000 90000 800004 700004 600004 500004 40000 30000 20000 10000 0 1 0 Absolute Energy aJ vs lt 8 11 gt 1 1 1 l l l l I 1 l I l I 250 300 350 400 450 500 550 600 650 700 750 800 850 900 950 1000 1050 Figure A 37 Absolute energy rate aJ s during 86 second period including fracture sensors 8 9 10 11 A 20 Absolute Energy al vs Time sec 8 Absolute Energy al vs 10 Absolute Energy al vs Time sec 15 10000 500 100000 9000 450 90000 8000 400 80000 7000 350 70000 6000 300 60000 5000 250 50000 4000 200 40000 3000 150 30000 2000 100 20000 1000 50 10000 0 4 0 4 0 1 l I 1 1 1 200 400 600 8
6. 50000 2000 200000 45000 1800 180000 40000 1600 160000 35000 1400 140000 30000 1200 120000 25000 1000 100000 20000 800 80000 15000 600 60000 10000 400 40000 5000 200 20000 0 1 1 1 I I I I I 0 1 D I I 1 1 1 0 1 100 20 300 400 500 600 700 800 100 20 300 400 500 600 700 800 100 200 300 400 500 600 700 800 Absolute Eneray aJ vs Time sec 8 Absolute Energy aJ vs 7 Absolute vs Time sec 2 50000 10000 500000 45000 9000 450000 40000 8000 400000 35000 7000 350000 30000 6000 300000 25000 5000 250000 20000 4000 200000 15000 3000 150000 10000 2000 100000 5000 1000 50000 0 1 I I I I 0 1 I 1 I I 0 l I 1 100 200 300 400 500 600 700 800 100 200 300 400 500 600 700 800 100 200 300 400 500 600 700 800 Figure A 46 Absolute energy rate aJ s during 86 second period including fracture individual sensors Counts vs Time sec 8 Counts vs Time sec 6 Counts vs Time sec 1 100 10 200 30 95 180 80 84 160 70 2 140 60 54 120 50 54 100 40 44 80 30 34 20 2 40 10 1 20 0 1 I 1 I I 1 0 1 1 1 1 1 I I 0 1 1 I I 1 1 100 200 0 400 500 600 700 800 100 20 300 400 50 600 700 800 10 20 0 400 500 600 700 800 Counts vs Time sec lt 9 Counts vs Time sec 7 Counts vs Time sec 2 100 20 500 30 18 450 80 16 400 70 14 350 60 12 300 50 10 250 40 8 200 30 150 20 4 100 10 2 50 0 1 0 0 100
7. ine eye 47 Table 8 5 Third fracture Criterion Set s eie e eate dus 49 Table 8 6 Third criterion Set exceedances ied euet 49 Table 9 1 Frequency of exceedance for individual criteria using the first fracture criterion set and thefirst data Se Ds 59 Table 9 2 Number of days a given number of criteria are exceeded using the first fracture criterion set and the first data set en esee eee eme 60 Table 9 3 Frequency of exceedance for individual criteria using the second fracture criterion set and the second data set in the North system du egnen 71 Table 9 4 Frequency of exceedance for individual criteria using the second fracture criterion set and second data set in the South system Eq 71 Table 9 5 Number of days a given number of criteria were exceeded using the second fracture criterion set and the second data set in the North system 72 Table 9 6 Number of days a given number of criteria were exceeded using the second fracture criterion set and the second data set in the South system seen 72 Table 9 7 Frequency of exceedance for individual criteria using the third fracture criterion set and the third data set in the Nort
8. A filtering algorithm has been used for monitoring in service bridge members comprising a ringdown count range number of counts after the peak high event rate and tight location tolerance to filter out non fracture AE events Hopwood II amp Prine 1987 The remaining AE events after filtering where considered fracture AE events if they had a high frequency bias Criteria sets 1 and 2 in Chapter 8 also consider high frequency bias by analyzing the frequency centroid of all the hits In the absence of AE noise and reflected waveforms AE hits from fracture will have peak amplitudes close to the wave fronts Yu et al 2011 Therefore hits will generally have high peak amplitudes with short rise times This may be true in an ideal geometry but waves propagating through a structure like the Cedar Avenue Bridge will undergo many reflections and interferences This is an example of how some characteristics which may work well in a controlled setting breakdown when implemented in a real structure with complex geometry 16 CHAPTER 5 CEDAR AVENUE BRIDGE MONITORING METHODOLOGY 5 1 System Overview Acoustic emission AE sensing technology was chosen for the monitoring of the Cedar Avenue Bridge because it is the only proven commercially available technology that has the ability to detect the formation of a crack at the moment the crack occurs Schultz amp Thompson 2010 An AE monitoring system has the potential to continuously monitor
9. UR PEE qoses Dre e 39 e 2 4613 1820 658 se 2 23 4797 4796 1624 112 696 5 8 2 3 49816 49818 1233 112 90835 1 16 2 3 399 39 2423 14 492 16 2 3 545 548 2433 104 476 k 3 i 2 3 6833 194 14 1 24 12 3 25564 25566 1887 96 504 2 24 2 3 2672 2673 1290 96 749 3 024 2 3 2808 2809 1048 96 963 4 2 12 3 2990 997 1024 96 970 5 24 2 3 aso 1877 96 6 24 12 3 3395 3390 124 96 8 32 1650 1650 2806 112 39914 2 2 18493 1846 2508 112 4657 3 8 3 2 2088 2080 2514 112 445581 8 2 226 2329 1395 112 8087 f 5 8 2 2607 26088 128 112 9205 5 16 3 2 52 342 52 344 1307 104 79572 16 2 54568 54572 3842 104 206 1 2 6 3 2 959 951 2664 96 3606 2 24 13 2 11398 1139 1558 96 667 3 13 2 1344 149 2207 96 448 _ Wave Velocity Average 572 MIRO A jwn Ha la l 1 l a a la l 1 l a 1 Table 2 Group 2 pencil break velocity results Dt ms DD Gn Wave Velocity ins 10 Group Test u 1 7
10. 88 Table 10 8 Non AE signal rejection effectiveness for third criterion set in South system 89 EXECUTIVE SUMMARY As the inventory of bridges in Minnesota ages the probability that a bridge will experience structural damage increases Over time factors such as environment fatigue loading and salt treatment take their toll on bridge health Hence the desire to monitor bridges arises in order to discover structural distress before it escalates into costly bridge damage The subject of continuous bridge monitoring includes many proposed solutions each with advantages and disadvantages Part of the difficulty of developing a universal bridge monitoring solution is that every bridge is different and therefore requires a different solution Acoustic emission AE technology was selected for the monitoring of fracture critical steel bridges because it shows promise of being able to monitor a large region of a structure with relatively few sensors for the purpose of fracture detection During this project an AE monitoring system was configured and implemented for monitoring one of the tie girders supporting the northbound lanes of the Cedar Avenue tied arch bridge MN Bridge 9600N to test the ability of AE systems for the monitoring of a large bridge structure The overall goal of this project was to investigate the use of a sparse AE system with sensors spaced along the bridge using large spacing for the purpose of sensing fracture in st
11. 006 4 0 006 4 0 006 2 0 006 0 006 3 0E 006 1 5 006 2 0 006 2 0 006 1 0 006 1 0 006 1 0 006 5 0 005 0008 i 0 0E 000 AM i i 0 0E 000 200 400 600 800 1000 200 400 600 800 1000 200 400 600 800 1000 Absolute Energy aJ vs Time sec 9 Absolute Eneray aJ vs lt 7 Absolute Energy aJ vs Time sec 2 GFlt 1 0E 007 100000 5 0E 006 0 006 90000 4 5 006 8 0 006 80000 4 0 006 7 0 006 70000 3 5E 006 0 006 60000 3 0E 006 5 0E 006 50000 2 5 006 4 0 006 40000 2 0 006 3 0E 006 30000 1 5E 006 2 0E 006 20000 1 0E 006 1 0 006 10000 5 0E 005 0 0E 000 5 1 1 1 1 1 1 1 1 0 0E 000 999 1 1 1 I 200 400 600 800 1000 200 400 600 800 1000 200 400 600 800 1000 Figure A 64 Absolute energy rate aJ s during 86 second period including fracture individual sensors Counts vs Time sec 8 Counts vs Time sec lt 6 gt Counts vs Time sec 1 GFlt 500 200 200 450 180 180 400 160 160 350 140 140 300 120 120 250 100 100 200 80 80 150 60 60 100 40 40 50 20 20 ral I m I I I I I I I 200 400 600 800 1000 200 400 600 800 1000 200 400 600 800 1000 Counts vs Time sec 8 Counts vs Time sec 7 Counts vs Time sec 2 500 50 200 450 45 180 400 40 160 350 35 140 300 30 120 250 25 100 200 20 80 150 15 100 10 40 50 5 20 Sy 1 Dm 1 200 4
12. 2 De DOO PCT 2 DS IDI CCU VE m TS 3 Chapter 3 Literat re Ses buta ecu GO eee E 4 3 1 Acoustic Emission Monitoring and Fatigue Life Prediction in Axially Loaded Notched Steel SpB ecHYWenSu us oia aset tau i ed ees denti e la A ted dde 4 3 2 Acoustic Emission Monitoring of In Service Bridges 4 3 3 Acoustic Emission Monitoring of Fatigue Cracks in Steel Bridge Girders 5 3 4 Prediction of Fatigue Crack Growth in Steel Bridge Components using Acoustic Emission C 6 3 5 Acoustic Emission Analysis during Fatigue Crack Growth in Steel 6 3 6 Acoustic Emission Monitoring of Bridges Review and Case Studies 7 Chapter 4 Acoustic Emission Back roud a 9 4 1 Acoustic EMISSION SOUDCES ot 9 4 2 Acoustic Emission Wave Propagation 9 4 2 1 Wave Propagation Modes 9 4 2 2 Wave Atten ation E 11 4 3 Acoustic Emission Monitoring 11 4 4 Acoustic Emission
13. 58 For each day during the first data set the first fracture criterion set defined in Section 8 2 was used to evaluate the data Table 9 1 shows the number of times each criterion was exceeded during each month of data analysis Criterion exceedances can be fairly common in the case of criteria one and three which occur once about every three days Criterion exceedances can also be very rare for criterion four which was only exceeded four times in 193 days Table 9 2 shows how many days a given number of criteria were exceeded The number of days exceeding a given number of criteria decreases as the number of criteria increases This illustrates the variability of the non fracture AE noise data and demonstrates the importance of having multiple criteria of diverse nature to define fracture events None of the days saw the exceedance of all five criteria For example the anomalous files from 5 24 13 and 7 2 13 did not have high enough frequency centroid peaks to exceed criterion number two The cause for such great activity is still unknown and the girder was inspected after the 7 2 13 instance of anomalous data was recorded but no signs of fracture where found Thus these anomalous data sets were deemed to not have been produced by fracture a fracture event and the first fracture criterion set was effective in excluding them as possible fracture events Table 9 1 Frequency of exceedance for individual criteria using the first fracture criter
14. 6476 5 3 16 15 14 13 650 13651 1164 104 8934 4 16 15 14 15 870 15 871 1115 104 924 1 24 15 14 4739 4 743 379 96 _ 2540 2 24 15 14 8 503 8505 1202 96 796 24 15 14 13 017 13018 1361 96 70536 1 Wave Velocity Average 6020 4 S I gt C 4 APPENDIX TROUBLESHOOTING AND MAINTENANCE TIMELINE The majority of the troubleshooting procedures were required for the North system The items in this timeline refer to the north system unless otherwise specified Table D 1 System timeline of troubleshooting events Date System Status Troubleshooting Action Taken SH II is believed to be working Asked sprint to mirror the plan from the 9 6 13 Modem account is not properly set MnDOT account to the UMN account up They were successful in doing so SH II is believed to be working Activated modem using initial MDN MSL Modem has been activated for new MSID 10 9 13 account A remote login of the new system is System Current 9 0 amps successful proving the modem and SH II are functioning E After restarting the system stays on for a 14 13 few minutes then loses power Current 1 6 amps SH II has switched off After Trouble shooting diagnosis provided by restarting the system switches to an Mistras Key points are low curre
15. Minnesota Department of Transportation Protocols and Criteria for Acoustic Emission Monitoring RESEARCH of Fracture Critical Steel Bridges m LIBRARY Office of Transportation System Management Arturo E Schultz Principal Investigator Department of Civil Environmental and Geo Engineering University of Minnesota June 2015 Research Project Final Report 2015 36 request this document in an alternative format call 651 366 4718 or 1 800 657 3774 Greater Minnesota or email your request to ADArequest dot state mn us Please request at least one week in advance Technical Report Documentation Page 1 Report No 2 3 Recipients Accession MN RC 2015 36 4 Title and Subtitle 5 Report Date June 2015 Protocols and Criteria for Acoustic Emission Monitoring of 7 Author s 8 Performing Organization Report No Anton S Tillmann Arturo E Schultz Javier E Campos S 9 Performing Organization Name and Address 10 Project Task Work Unit No Department of Civil Environmental and Geo Engineering CTS Project 2013016 University of Minnesota 11 Contract C or Grant G No 500 Pillsbury Drive SE 99008 49 Minneapolis MN 55455 0220 o 12 Sponsoring Organization Name and Address 13 Type of Report and Period Covered Minnesota Department of Transportation Final Report Research Services amp Library 14 Sponsoring Agency Code 395 John Ireland Boulevard MS 330 St Paul Minnesota
16. N a ZSSS Figure 5 4 Walking bridge adjacent to monitored tie girder plan view 5 4 System Power Each of the two monitoring systems is powered by a solar panel array of four 130W 26 x59 solar panels The maximum current output for each solar panel under direct sunlight is 7 5 amps However it has been observed that even slight obstructions to solar incidence will reduce the output current noticeably Current produced by the solar panels is stored in four 12V 104Ah batteries connected in parallel The batteries are protected from overcharging by a charge controller unit The 5 central computer is connected to the DC output of the battery array A low battery protector cuts off the power to the SH II when battery voltage drops below 10 1V to prevent batteries from complete discharge Physical Acoustics Corporation 2010 The power 21 system also is equipped with a power inverter that allows AC devices to be used simultaneously with the monitoring system The power inverter is necessary for accessing the SH I computer user interface from inside the bridge and this operation requires an external monitor A schematic of the power system is shown in Figure 5 5 Physical Acoustics Corporation 2010 AC OUTLET DC OUTLET CHASSIS GROUND LOW BATTERY PROTECTOR COPPER BAR FUSE 200A
17. 008 8054006 4 5E 006 4 5E 006 8 0E 006 8 0E 006 4 0E 006 4 0E 006 7 0E4006 7 0E4006 3 5E4006 3 5E4006 amp 0E 006 amp 0E 006 3 0E 006 3 0E 006 5 0E 006 5 0E 006 2 5E 006 2 5E 006 4 0E 006 4 0E 006 2 0E4006 2 0E4006 3 0E 006 3 0E 006 1 5E 006 1 5E 006 2 0E 006 2 0E 006 1 0 006 1 0 006 1 0 006 1 0 006 5 0E 005 5 0E 005 0 0E 000 j 0 0 000 j i 0 0 000 j 0 0E 000 i 200 400 600 80 200 40 600 80 200 40 600 80 200 40 60 80 Figure A 8 Absolute energy rate aJ s during 86 second period including fracture individual sensors Counts vs 1 Counts vs 3 Counts vs Time sec 5 GFit Counts vs Time sec 7 5000 5000 10000 1000 4500 4500 3000 300 4000 4000 8000 800 3500 3500 7000 700 3000 3000 6000 600 2500 2500 5000 500 2000 2000 4000 400 1500 1500 3000 300 1000 1000 2000 200 500 500 1000 100 0 1 I 1 I 0 1 1 0 1 1 I 0 1 1 200 400 500 800 200 400 600 800 200 400 600 800 200 400 600 800 Counts vs Time sec 2 Counts vs 4 Counts vs Time sec lt 6 gt GFlt Counts vs Time sec 8 5000 5000 1000 1000 4500 4500 900 900 4000 4000 800 800 3500 3500 700 700 3000 3000 600 600 2500 2500 500 500 2000 2000 400 400 1500 1500 300 300 1000 1000 200 200 500 500 100 100 0 T 1 0 T I 1 1 0 I I 0 1 1 1 200 400 600
18. 1 008 1 008 1 4 008 1 2 008 1 0 008 0 007 60 007 4 0 007 2 06 007 0 0E4000 1 1 400 50 800 1000 Absolute Energy aJ vs Time sec 2 2 0E 008 1 8E 008 1 6E 008 1 4E 008 1 2E 008 1 0 008 8 06 007 6 0E 007 4 0E 007 2 0E 007 005 000 i i 400 600 l 800 1000 Absolute Energy a vs Time sec lt 3 gt Absolute Energy aJ vs Time sec 5 2 0 008 1 8 008 1 008 1 4 008 1 2 008 1 0 008 8 0 007 0 007 4 0 007 2 0 007 2 0 008 1 8 008 1 008 1 4 008 1 2 008 1 0 008 8 0 007 0 007 4 0 007 2 0E 007 0 0 000 0 0E 000 i i i i i i 400 60 80 1000 400 600 800 1000 Absolute Energy a vs Time sec 4 Absolute Energy aJ vs Time sec lt 6 gt 5 0E 008 4 5E 008 4 0 008 008 3 0E 008 2 5E 008 2 0E 008 1 5 008 1 0 008 5 0 007 5 0E 008 4 5E 008 4 0E4008 3 5E4008 3 0E 008 2 5E 008 2 0E 008 1 008 1 0 008 5 0E 007 0 0 000 0 0E 000 i i i i 1 400 600 800 1000 1 400 600 800 1000 Absolute Energy a vs Time sec 7 0E 008 4 5E 008 4 0 008 3 5 008 3 0 008 2 5 008 2 0 008 1 5 008 1 0 008 5 0 007 0 0 000 Absolute Energy a vs Time sec 8 2 06 008 1 8 008 1 008
19. 1 4 008 1 2 008 1 0E 008 8 0E4007 60 007 4 0E 007 20 007 0 0E4000 400 1 1 800 1000 i 1 I 600 800 1000 Figure A 18 Absolute energy rate aJ s during 86 second period including fracture Counts vs Time sec 1 individual sensors Counts vs Time sec 3 Counts vs Time sec 5b GFit Counts vs Time sec 7 5000 4500 4000 3500 3000 2500 2000 1500 1000 500 0 l 1 1 1 400 600 800 1000 5000 5000 4500 4500 4000 4000 3500 3500 3000 3000 2500 2500 2000 2000 1500 1500 1000 1000 500 500 0 0 l 1 I 400 600 800 1000 400 600 800 1000 5000 4500 4000 3500 3000 2500 2000 1500 1000 500 0 I 1 400 600 800 1000 Counts vs Time sec 2 Counts vs Time sec 4 Counts vs Time sec b GFlt Counts vs Time sec 8 5000 4500 4000 3500 3000 2500 2000 1500 1000 500 0 1 1 1 400 600 800 1000 5000 5000 4500 4500 4000 4000 3500 3500 3000 3000 2500 2500 2000 2000 1500 1500 1000 1000 500 500 0 0 1 1 I 1 400 600 800 1000 400 600 800 1000 5000 4500 4000 3500 3000 2500 2000 1500 1000 500 0 1 I I 1 400 600 800 1000 Figure 19 Count rate counts s during 86 second period including fracture individual sensors 11 300 Time sec vs X Position lt All Channels Loc 2 280
20. 7 5E 008 7 0E 008 6 5 009 6 0 008 5 5 009 5 0 008 4 5 009 4 0 009 3 5 009 3 0E 009 2 5 009 2 0 009 1 5 009 1 0 009 5 0E 008 0 0 000 l I 1 l 110 120 130 140 150 Figure 26 Maximum absolute energy aJ versus amplitude dB sensors 7 8 Absolute Eneray aJ vs Time sec 7 8 5 0E 007 4 5E 007 4 D0E 007 3 5E 007 3 0 007 2 5E 007 2 0 007 1 5E 007 1 0 007 5 0E 006 0 0 000 I I I I I I 400 500 600 700 800 300 1000 1100 1200 Figure A 27 Absolute energy rate aJ s during 86 second period including fracture sensors 7 8 Absolute Energy aJ vs Time sec 1 2 0 007 1 8 007 1 6 007 1 4 007 1 2 007 1 0 007 8 0 006 0 006 4 0 006 2 0 006 0 0 000 400 1 000 1200 Absolute Energy aJ vs Time sec 3 2 0E 007 1 8E 007 1 6E 007 1 4E 007 1 2E 007 1 0E 007 8 0E 006 0 006 4 0 006 2 0 006 0 0 000 1 800 1000 U 1200 Absolute Energy aJ vs Time sec lt 5 gt 2 0E 007 1 8E 007 1 6E 007 1 4E 007 1 2E 007 1 0 007 8 0 006 5 0E 006 4 0E 006 2 0E 006 0 0 000 i 000 1 1200 Absolute Energy aJ vs Time sec 7 GFIt 2 0E 007 1 8E 007 1 007 1 4 007 1 2E4007 1 0 007 8 0 006 5 0E 006 4 0 006 2 0 006 0
21. System current 10 6 amps displayed when signal strength is checked 8 8 14 Determined that only three of the Plan for future trip to replace south system four solar panels of the south solar panel system are producing power 8 24 14 5 ceases to acquire data Purchased new antenna to replace old Modem is not working antenna that may have deteriorated SH II is inoperative New antenna is installed 9 25 14 Modem is assumed to be SH II is rebooted and begins working but inoperative only acquires data for 4 hours before D 2 becoming inoperative Modem is removed for testing in office South system is operating on three 10 17 14 The bad solar panel is replaced of four solar panels Set up modem on office computer with appropriate software and contacted sprint 2 for troubleshooting diagnosis No signal 10 21 14 SH II is believed to be inoperative was registered Sprint could not provide problem or solution other than to purchase a new modem SH II is inoperative SH II is rebooted but quickly becomes 10 24 14 System current 3 5 amps inoperative 11 7 14 SH II is inoperative SH II is rebooted but quickly becomes System current 0 amps inoperative 11 14 14 SH II is inoperative SH II is rebooted but quickly becomes System current 1 3 amps inoperative Indicates a day where a site visit was made to the Cedar Avenue Bridge D 3 APPENDIX E CRITERIA EXCEE
22. recorded on the bridge at times other than those for the fracture beam tests exceeded all of the criteria in any of the three fracture criteria sets This observation is in agreement with the Cedar Avenue Bridge s history of excellent performance as determined by periodic visual inspections 11 2 Conclusions The monitoring of the Cedar Avenue Bridge using AE technology demonstrated that although AE processing protocols may be complex AE technology holds promise for identifying fracture in steel bridges particularly those bridges that are fracture critical To properly use sparse AE i e with widely spaced sensors to detect fracture tests must be performed to simulate a fracture occurring in the bridge structure In complicated geometries such as the Cedar Avenue Bridge AE waveforms from fracture are likely to become distorted and scattered before ever being detected by a sensor This latter observation is especially important for sparse AE monitoring 91 Because of this wave distortion no single trademark characteristic exists for an wave propagating from a fracture However all AE waves associated with fracture of beam tests in the laboratory and the bridge featured multiple characteristics that can and should be exploited to discriminate between fracture AE waves from non fracture AE waves Thus multiple indications of fracture must be considered in order to determine the occurrence of fracture within a reasonable degree of acc
23. where AE noise data is fairly constant and can be easily filtered out When fracture or distress does occur in pressure vessels the characteristics of the AE data are drastically different than the expected AE noise data Pollock 2003 Also the simple geometry of pressure vessels results in relatively clean waveforms AE technology has also been used in local regions of large structures such as bridges AE has been most successful in locations where a known crack is being monitored for further propagation or where a crack is expected to occur Monitoring a localized region allows a user to implement guard sensors which can help filter out waveforms entering the monitoring region from elsewhere in the structure Kosnik 2009 AE technology has also been used to inspect aircraft bucket trucks buildings dams military vehicles mines piping systems railroad tank cars rotating machinery and storage tanks Pollock 2003 In the application of AE technology to monitor the Cedar Avenue Bridge a different approach to AE monitoring was investigated Sensors in the Cedar Avenue Bridge are used to monitor a large area engulfing complex geometries rather than monitoring a single localized region or a uniform geometry This method of using AE technology has its trade offs such as 1 the sensors picking up AE noise from numerous sources other than fracture and 2 the large scale and complexity of the structure affecting waveforms in unpredictable ways How
24. 0 000 I U U 800 100 1200 Absolute Energy aJ vs Time sec 2 2 0E 007 1 8 007 1 6 007 1 4 007 1 2 007 1 0 007 8 0E 006 0 006 4 0E 006 2 0E 006 0 0 000 400 1000 l 1200 Absolute Energy aJ vs Time sec 4 2 0E 007 1 8E 007 1 6E 007 1 4E 007 1 2E 007 1 0E 007 8 0E 006 5 0E 006 4 0E 006 2 0E 006 0 0 000 400 600 1 1000 l 1200 Absolute Energy a vs Time sec lt 6 gt 2 0 007 1 8 007 1 007 1 4 007 1 2 007 1 0 007 8 0 006 6 0E 006 4 0 006 2 0 006 0 0 000 00 1 00 1200 Absolute Energy aJ vs Time sec 8 2 0 007 1 8 007 1 007 1 4 007 1 2 007 1 0 007 8 0 006 0 006 4 0 006 2 0 006 0 0 000 400 600 l 100 1200 A 28 Absolute energy aJ s T 86 d period including faire individual sensors Counts vs Time sec 1 Counts vs Time sec 3 Counts vs Time sec 5 Counts vs Time sec 7 GFit 5000 4500 4000 3500 3000 2500 2000 1500 400 500 5000 4500 4000 3500 3000 2500 2000 1500 1000 500 0 400 600 2000 1800 1600 1400 1200 1000 800 600 400 200 OS T 400 600 1 000 1 200 1000 1 200 400 60 900 1 000 1 200 1 000 1 200 Count
25. 11 55 0 0 0 0 0 0 0 6 27 2014 140613144018 16 2 14 12 04 1 0 0 0 0 0 1 6 28 2014 140613144018 17 15 6 42 1 0 1 0 1 1 4 6 29 2014 140613144018 18 1 16 10 35 0 0 0 0 0 0 0 6 30 2014 140613144018 18 2 17 08 57 1 0 0 0 0 1 2 7 1 2014 7 2 2014 7 3 2014 7 4 2014 7 5 2014 7 6 2014 7 7 2014 7 8 2014 7 9 2014 7 10 2014 7 11 2014 7 12 2014 7 13 2014 7 14 2014 7 15 2014 7 16 2014 7 17 2014 7 18 2014 7 19 2014 7 20 2014 7 21 2014 7 22 2014 7 23 2014 7 24 2014 7 25 2014 7 26 2014 7 27 2014 7 28 2014 7 29 2014 7 30 2014 7 31 2014 Table E 2 July 2014 criteria tabulation for North system 140613144018 19 1 140613144018 19 2 140613144018 20 1 140613144018 20 2 140613144018 21 140613144018 22 1 140613144018 22 2 140613144018 23 140613144018 24 140613144018 25 1 140613144018 25 2 140613144018 26 140613144018 28 140613144018 29 1 140613144018 29 2 140613144018 30 1 140613144018 30 2 140613144018 31 140613144018 32 1 140613144018 32 2 140613144018 33 1 140613144018 33 2 140613144018 34 1 140613144018 34 2 140613144018 35 1 140613144018 35 2 140613144018 36 1 140613144018 36 2 140613144018 37 1 140613144018 37 2 140613144018 38 1 140613144018 38 2 140613144018 39 1 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 35 35 36 37 38 39 40 41 42 43 44 45 46 47 48 47 50 38 57 44 52 54 05 38 4
26. 4 1 3 007 1 2E 0074 1 1E 007 1 0 007 0 006 8 0 006 7 0 006 0 006 5 0 006 4 0E 006 3 0E 006 2 0 006 1 0 006 0 0 000 1 1 1 1 1 1 I 1 100 150 200 250 300 350 400 450 500 550 600 650 700 750 800 Figure A 7 Absolute energy rate aJ s during 86 second period including fracture sensors 6 7 8 A 5 Absolute Energy aJ vs Time sec 1 Absolute Energy aJ vs Time sec 3 Absolute Energy aJ vs Time sec 5 1 0E 007 1 0E 007 2 0E 007 9 0E 006 9 0 006 1 8 007 8 0 006 8 0E 006 1 007 7 0 006 7 0 006 1 4 007 6 0 006 6 0E 006 1 2 007 5 0 006 5 0 006 1 0 007 4 0E 006 4 0E 006 8 0E 006 3 0E 006 3 0E 006 6 0E 006 2 0E 006 2 0E 006 4 0E 006 1 0E 006 1 0E 006 2 0E 006 0 0 000 0 0 000 T 0 0 000 T 200 200 Absolute vs Time sec 7 5 0E 006 4 5 006 4 0 006 3 5 006 3 0E 006 2 5 006 2 0 006 1 5 006 1 0 006 5 0E 005 0 0E 000 i i 1 I I 400 600 800 1 1 400 600 800 Absolute Energy aJ vs Time sec 2 Absolute Energy aJ vs Time sec 4 Absolute Eneray aJ vs Time sec 5 Absolute Eneray aJ vs Time sec 8 1 0 007 1 0 007 5 0 006 5 0E 006 9 0E
27. 55155 1899 15 Supplementary Notes http www lrrb org pdf 201536 pdf 16 Abstract Limit 250 words With bridge infrastructure in Minnesota aging advancing techniques for ensuring bridge safety is a fundamental goal of the Minnesota Department of Transportation MnDOT Developing health monitoring systems for fracture critical bridges is an essential objective in meeting the stated goal This report documents the implementation of two 16 sensor acoustic emission monitoring systems in one of the tie girders of the Cedar Avenue Bridge which is a fracture critical tied arch bridge spanning the Minnesota River between Bloomington and Eagan MN The goal of the project is to develop a process for using acoustic emission technology to monitor one of the girders of the bridge while continuously collecting data from the monitoring systems Given the cost of acoustic emission sensing equipment an approach was adopted to space the sensors as widely as possible Fracture tests were conducted on a specimen acoustically connected to the bridge to simulate fracture in a bridge member Sets of criteria were developed to differentiate between acoustic emission data collected during fracture and ambient bridge i e AE noise data The sets of criteria were applied to fracture test data and AE noise data to determine the validity of the criteria For each criteria set a period of Cedar Avenue Bridge monitoring data was analyzed The results of the analysis of e
28. 6 8 gt 1 000 950 900 850 800 750 700 650 600 550 500 450 400 350 300 250 200 150 100 50 0 Hits vs Time sec 1 Hits vs Time sec 3 Hits vs Time sec 5 Hits vs Time sec 7 Hits vs Time sec 4 GFIt Hits vs Time sec lt 6 gt Figure A 3 Cumulative hits versus time s individual sensors A 3 Hits vs Freq Centroid kHz lt All Channels Figure A 4 Cumulative hits versus frequency centroid kHz all sensors Duration us vs Amplitude dB 8 150000 1400005 130000 120000 110000 1000004 30000 80000 700005 600005 50000 40000 300004 200004 100004 LI l 1 54 50 52 Figure 5 Duration us versus amplitude dB sensor 8 A 4 Absolute Energy aJ vs 8 2 0 008 1 9 008 1 8 008 1 7E 0083 1 6E 0085 1 5 008 1 4 008 1 008 1 2 008 1 008 1 0 008 9 0E 007 8 0E 007 7 0 007 5 5 0E 007 5 0E 007 4 0E 0074 3 0 0075 2 0E 0074 1 0 007 0 0 000 1 I l 110 120 130 140 150 Figure A 6 Maximum absolute energy aJ versus amplitude dB sensor 8 Absolute vs Time sec 6 8 2 0E 007 1 9E 007 1 007 1 7 007 1 007 1 5 007 1 4E 007
29. 800 200 400 600 800 200 400 600 800 200 400 600 800 Figure A 9 Count rate counts s during 86 second period including fracture individual sensors A 6 vs X Position lt All Channels Loc 1 12444 12 120 4 1184 1154 114 112 110 Figure A 10 Time s versus x Position in on notched beam x 0 at crack tip Time sec vs X Position lt All Channels Loc 1 124 u pl 2 11 122 118 116 f lt 114 112 110 os nux Figure A 11 Time s versus x position in on notched beam x 0 at crack tip events with source amplitude greater than 80dB only A 7 A 2 Laboratory Test Number 2 LT2 Hits vs Time sec 4 8 1 l 1 100 120 140 160 200 220 240 260 280 300 320 340 Hits vs 1 Hits vs Time sec 3 Hits vs Time sec 5 Hits vs lt 7 gt Hits vs Time sec 4 Hits vs Time sec 6 Hits vs 8 220 240 260 280 300 220 240 260 280 300 Figure A 13 Cumulative hits versus time sec individual sensors A 8 Hits vs Freq Centroid kHz lt All Channels 50 454 40 354 304 254 205 07 T 1 1 1 l 1 1 I 1 l 1 l T I 1 1 0 20 40 60 80 100 120 140 160 180 200 220 240 260 280 300 320 Figu
30. As plastic regions form elastic energy is released and the permanent deformation results in a waveform that will transfer the deformation to the rest of the specimen Miller amp McIntire 1987 Fracture in an object is another form of permanent deformation The energy released as the surfaces of the crack become stress free propagates away from the crack as an elastic waveform Miller amp McIntire 1987 AE is often affiliated with the onset of structural damage For this reason it is a valuable indicator to be used when monitoring a structure for structural distress The AE monitoring system of this project are used to detect AE waveforms in order to identify structural distress before it grows into critical structural damage While an acoustic emission is defined as a transient wave emitted from local irreversible changes in material an acoustic emission monitoring system is still capable of collecting data from non AE events Steps are taken to insure AE noise is filtered out for example setting an adequate AE threshold and filtering out low frequency components Despite the implementation of these filters AE monitoring systems can still collect an abundance of data from non AE sources These sources include fretting between moving surfaces impacts of vehicles on the bridge deck rain hitting steel and creaking related to temperature movements Often in monitoring of in service bridges data from these non AE sources will outweigh da
31. Duration versus amplitude for the second data set in the north system a Low activity day b High activity day c Anomalous day 66 Durahor us Amplitude dB Ali Channeli 14000 12000 10000 9 i 221 lias 1 i 4000 e 2000 0 i R Son i i D 55 5 75 Dusahondus AmplitudetdB AJ Channets gt 49000 35000 0000 4 25000 2X0 7 4 28 1 0 amp t 2 69 e 22 0007 e lii 1 ndi 5000 il 111 1 D 55 70 5 90 5 b Figure 9 10 Duration versus amplitude for the second data set in the south system a Low activity day b High activity day The final relationship analyzed is the absolute energy rate versus time of the entire system Figures 9 11 and 9 12 The software calculates the magnitude of absolute energy rate by dividing the change in absolute energy over a small time increment The time increment 15 calculated by discretizing the time duration of a plot into a user specified number of increments The following plots are discretized into 1000 time increments Note is made here that the title assigned to the plot by AEWin indicates absolute energy but it is actually the absolute energy rate that is shown in Figures 9 11 and 9 12 Only the anomalous day of th
32. Frequency of exceedance for individual criteria using the third fracture criterion set and the third data set in the North system r Fracture Criteria Counts number of days criterion is exceeded Sample Size days Jun 2014 Augo 6 _ 9 1 8 o de j 2 4 3 5 20 1 14 4 Table 9 8 Frequency of exceedance for individual criteria using the third fracture criterion set and the third data set in the south system _ Fracture Criteria Counts number of days criterion is exceeded Sample Size days 1 2 3 4 5 6 204 1 o 0 o o 3 214 s 0 0 1 o 4 Aug 2014 0 0 o o o o Toa 6 o o 1 0 7 Table 9 9 Number of days a given number of criteria were exceeded using the third fracture criterion set and the third data set in the North system No of Criteria Exceeded No of Days of Days 82 Table 9 10 Number of days a given number of criteria were exceeded using the third fracture criterion set and the third data set in the South system No of Criteria Exceeded No of Days of Days 83 CHAPTER 10 EFFECTIVENESS OF FRACTURE CRITERIA 10 1 Definitions of Effectiveness In the following chapter the effectiveness of each fracture criterion set will be demonstrated using a tabular format to easily compare the three sets Two types of effectiveness measures a
33. Sensor LT2 LT3 LT2 LT3 LT2 LT3 50 3355 so 9 163 146 3790 4470 2917 50 33 33 168 5 5 122 190 uu 237 142 s Table 6 4 North system bridge fracture test results Hit Rate hits s Energy Rate pJ s Count Rate counts s BTN1 BTN2 BTN2 BTN2 16 2667 Joris 20 36 Table 6 5 South system bridge fracture test results __ Hit Rate hits s Energy Rate pJ s Count Rate counts s _6_ _8_ 9 Table 6 5 shows the number of hits that have both duration greater than 30ms and amplitude greater than 90dB This is one of the primary characteristics found in fracture tests that help differentiate fracture from non AE events Table 6 5 only includes data from sensors that are eligible to be used for a high amplitude criterion which are sensors that are sufficiently far enough from the fracture Table 6 6 Number of hits with duration gt 30ms and amplitude gt 90dB for each test Hits gt 30ms amp gt 90dB Note that the tables in this section provide a summary of the fracture test results For graphical results of the tests refer to Appendix B 37 CHAPTER 7 COLLECTION OF ACOUSTIC EMISSION DATA IN THE CEDAR AVENUE BRIDGE 7 1 Bridge Data Collection Summary The data collection of the AE data produced in the Cedar Avenue Bridge for this phase of the project began on November 1 20
34. This is possibly due to snow on solar panels or prolonged cloud cover Data for at least some period of time is collected during the days in this period except for 2 21 Mar 2 2014 Mar 10 2014 Mar 9 2014 Oct 31 2014 No data files are collected during this time This is possibly due to snow on solar panels or prolonged cloud cover Data for at least some period of time is collected during the days in this period except for 3 12 4 7 4 21 4 23 4 25 4 26 4 28 4 30 5 2 5 4 5 6 5 11 5 13 5 14 5 17 5 19 5 20 5 26 5 29 6 1 6 4 6 7 6 10 6 13 6 15 6 22 6 24 6 29 7 3 7 5 7 9 7 10 7 12 7 14 7 15 7 18 7 20 8 2 8 9 8 10 8 26 9 5 9 7 9 24 10 2 10 6 10 13 10 17 The data collection efficiency of the south system is shown graphically in Figure 7 1 The data collection efficiency measures the percentage of days during which at least some data was collected The chart shows the dependency of the system on sunlight because the efficiency during winter months is much lower than other times of year when there is more sunlight 40 South System Data Collection Efficiency Nov 2012 Oct 2014 100 0 80 0 L 70 0 60 0 4 40 0 30 0 20 0 1 100 0 Nov Dec Jan Feb Mar Apr May Jun July Aug Sept Oct Nov Dec Jan Feb Mar Apr May Jun July Aug Sept Oct Efficiency 96 Figure 7
35. ae ewer 24 Table 5 3 Attenuation pencil break test results a 26 Table 5 4 SH H acquisition settings 27 Table 6 1 Notched Beam Fracture Test Summary eeu i e hiver 20 Table 6 2 Bridge and notched beam steel properties 29 Table 6 3 Laboratory fracture test results 36 Table 6 4 North system bridge fracture test results 36 Table 6 5 South system bridge fracture test results a ee 37 Table 6 6 Number of hits with duration gt 30ms and amplitude gt 90dB for each 37 Table 7 1 Timeline of AE data records for the South system 39 Table 7 2 Timeline of AE data records for the North system 42 Table 8 1 First fract re Criterion Seld ec ete e sei taeda BAe 46 Table 8 2 First criterion ipi radii ed 46 Table 8 3 Second fracture criterion tti dtd S cede 47 Table 8 4 Second criterion set exceedances
36. and analyzed on an office computer running MISTRAS AEwin software The central computer sensors and modem are powered by solar charged batteries Current from the solar panels is sent to batteries that power the system Section 5 3 on system power discusses this setup in detail 5 2 System Installation During this phase of the project the second half of the monitoring system north system was installed in the bridge The equipment procured and installed is listed and described in Table 5 1 The procedure for this installation essentially followed the steps as described in the Phase II report Schultz et al 2014 The existing system was relocated to the south half of the bridge and the new system was installed in the north half The final locations of the sensors after installation are shown in Figure 5 2 After the new north system was in place the south system was reconnected to the existing solar panels on the south side of the pedestrian bridge and a new array of four solar panels was installed on the north end of the pedestrian bridge 10 AWG cable was used to wire the solar panel power outputs to the charge controller box located as shown in Figure 5 2 Final locations for both solar panel arrays are shown in Figure 5 3 and 5 4 18 Table 5 1 Acoustic emission equipment from Mistras Group Inc Mistras RN SH II SRM Smart Remote Sensor Highway system Includes outdoor case Sensor Highway 16 channel motherboard At
37. associated with fracture Laboratory fracture test data was also filtered to discard small amplitude hits that may have decayed before reaching the nearest sensor had it been recorded in the bridge This fracture criterion set was developed with the laboratory fracture beam test data as well as data from the two successful bridge fracture tests BTS2 and BTN2 This criterion set focused on the high occurrence rate of parameters during fracture and used both hit based parameters such as counts as well as wave based parameters such as absolute energy This criterion set did not include a frequency parameter because trends with the frequency centroid were found to be ambiguous in some cases This criterion set was the first to make use of the more advanced capabilities of the software such as the calculation of source location and source amplitude The use of source location allowed a time versus location plot to be used not only in defining a criterion but also to provide a detailed time history of AE in active regions of the bridge The use of source location and source amplitude parameters require data from pencil break tests on the structure as well as an understanding of how errors in input values will affect results as discussed in Section 5 5 on sensor calibration Each criterion of the third set is shown in Table 8 5 This set was used to analyze data from both the north and the south system from June 2014 to August 2014 This period of data is the
38. attached to the inside of the girder s web about 5ft from the bottom flange Placement of sensors in this tie girder is ideal because the walking bridge running adjacent to the girder allows for easy access into the girder on either end as seen in Figures 5 3 and 5 4 The walking bridge also supplies the support structure for the solar panel arrays that are placed on top of the walking bridge support frames Solar panel locations shown in Figures 5 3 and 5 4 minimize cable length and provide optimal sunlight The SH II central computers power controller boxes and batteries are located at the center of each systems sensor array i e between sensors 8 and 9 of Figure 5 2 The SH II is located in the center of the sensors to minimize the longest sensor cable needed 100ft The system modems are located at each end of the tie girder for their respective systems The modems are located at the ends of the girders so that antenna cables can be made as short as possible while still allowing antennas to be placed in optimal positions outside of the girder 20 South m North Solar Panels aN Tie Girder F k TENES x y Figure 5 3 Walking bridge adjacent to monitored tie girder photo NORTH SOLAR PANEL LOCATION MONITORED TIE GIRDER WALKING BRIDGE SOUTH SOLAR PANEL LO CATION SST SSS r 1 C L ZINN
39. break tests were performed between sensors with various obstructions between them Sensor groups one and five consist of two sensors with a diaphragm between them groups two and four consist of two sensors separated by a girder splice and group three consists of two sensors with no obstructions between them In linear monitoring a sensor group consists of at least two sensors For each sensor group a velocity can be assigned as the average velocity of a wave traveling through the medium between the sensors Appendix A documents the results of each pencil break test Table 1 shows the average velocity results from the pencil break tests for each group Table 5 2 Average velocities between sensors Wave Velocity in s 1 37729 79864 133469 82886 60220 24 Once the wave velocity between sensors is known the source of an AE event can be located on a line between the sensors using Equation 3 _ At V x X gt 3 Ge oF The variables in Equation 3 are the same as those in Equation 2 If the calculated velocity value is smaller than the actual velocity the software algorithm will locate the event closer to the midpoint of the two sensors If the calculated velocity is larger than the actual velocity the software algorithm will locate the event closer to the first hit sensor If the calculated velocity is so large that the event would be located outside of the region between the two sensors the software algorithm discards
40. c de 30 6 3 3 Sens r LOCAL PE 31 6 3 4 Power Solutions oe Boo dedo Ge gt da fug 33 6 3 3 Data OMe Ct OM ost s Sim aolet as ata tas esa ius asc hat dotata oat 33 6 4 Laboratory Notched Beam Fracture 34 6 5 Fracture Acoustic Emission Results and 35 Chapter 7 Collection of Acoustic Emission Data in the Cedar Avenue Bridge 38 7 1 Bridge Data Collection ok e aetas ug se ye ue cas tu ep 38 7 2 South System Data Collection E e E RE 38 7 3 North System Data Collection TR ES EXE STA CEN 41 7 Solar Panel Power di epp bu hue Mode en 43 Chapter 8 Fracture Criteria Development 45 S LDevelopmentof Prac tite EE S BIN HR CEA 45 8 2 First Fracture Criterion Set oce tuac e eie i ed icta 45 5 3 Second Fracture Criterion odo la aaa ae 47 OA Third Fracture Criterion Setii peita oen elo bietet 48 Chapter 9 Acoustic Emission Analysis of Cedar Avenue Bridge Data 50 9 1 Data Analysis Summary SA QUA 50 9 2 First Bridge A
41. data set in south system Low activity day b PP cio 76 Figure 9 15 Time versus event location for third data set using the third fracture criterion set in the north system showing source amplitudes greater than 80dB a Low activity day b High activity day Anomalous 78 Figure 9 16 Time versus event location for the third data set using the third fracture criterion set in the south system showing source amplitudes greater than 80dB a Low activity day b High Activity CAV NM 79 Figure 9 17 Time versus event location for the third data set in the north system showing all events a Low activity day b High activity day Anomalous day 80 Figure 9 18 Time versus event location for the third data set in the south system showing all cns M suis 81 Figure 10 1 Fracture Effectiveness During Fracture Beam Tests 87 Figure 10 2 Non Fracture AE Signal Rejection Effectiveness from Bridge Data Sets 89 LIST OF TABLES Table 5 1 Acoustic emission equipment from Mistras Group Inc 19 Table 5 2 Average velocities between sensors aera dee
42. described below 1 Data collected on April 4 2013 which is representative of a low activity day of data collection 2 Data collected on September 26 2013 which is representative of a high activity day of data collection 3 Data collected on May 24 2013 which is representative of an anomalous day of data collection A low activity day for the first data set is defined as a day when fewer than two criteria where exceeded A high activity day for the first data set is defined as a day when two or three criteria where exceeded An anomalous day for the first data set is defined as a day when more than three criteria where exceeded See Table 9 2 for the number of days in each category The first relationship that is analyzed is the cumulative number of hits versus time plot for each of the 16 sensors in the south system As seen in the three plots of Figure 9 1 the cumulative number of hits collected over similar amounts of time can vary drastically from day to day Figure 9 1 shows the combined number of hits on all the sensors instead of 16 individual plots for brevity The low activity day is what would be expected on the bridge that is a consistent increase in hits due to ongoing AE noise generated by traffic and bridge motion the latter that is generated by mechanisms such as bolt fretting and sliding frictional surfaces The high activity data plot shows a sharp increase in hits at 27 500 seconds however the spike in the cumulative nu
43. growth and AE absolute energy rate was used to very accurately predict the growth of the crack during the later load cycles This experiment shows how absolute energy rate can accurately replace stress intensity range in the absence of noise data 3 5 Acoustic Emission Analysis during Fatigue Crack Growth in Steel Sinclair Connors and Formby 1977 performed a fatigue crack experiment to determine the characteristics of AE data collected during fatigue cracking of steel steel specimen was loaded in a three point bending setup with a machined notch even with the loading actuator The machined crack produced a high localized stress to allow for a crack to form during loading Three AE sensors where used to collect the AE data produced from the crack tip while a fourth 6 sensor placed within a few millimeters from the crack was used to verify AE propagation from the crack tip During fatigue loading stress intensity factors where varied to determine the effect of stress intensity on AE The loading and specimen was selected to meet the requirements of fully plane strain conditions It was observed that crack propagation rate was proportional to the rate of AE events It was also observed that the total number of AE events was dependent not on the rate of crack propagation but on the total area of fractured material This experiment shows how AE event rate can be used to replace the stress intensity factor term when determining crack
44. have been identified 7 2 South System Data Collection Data collection for the south system was fairly consistent throughout the duration of this project There were long periods of time during which data was collected for at least some portion of every day There were also extended periods of time during which no data was collected The periods during which no data was collected often occurred during winter months when sunlight is scarce The south system required very little maintenance compared to the north system The only significant operational problem with the South System was that one of the original solar panels of the south system required replacement after it was observed to stop producing power The data collection goal of the south system was to obtain 16 months of data during a two year period The system was able to achieve that goal considering that at least some data was collected 21 months of the two year period Table 10 summarizes the periods when data was collected by the south system and it also gives a brief description of possible reasons why some periods of time yielded no AE data for this system 38 Table 7 1 Timeline of AE data records for the South system Begin Date End Date Description Comments Nov 1 2012 Dec 3 2012 Few data files were uploaded to the FTP site during this time The reason for the fragmented data files is unknown Dec 4 2012 Dec 12 2012 Data for portions of eac
45. historic and severity indices The report also discusses load tests performed on a steel bridge with acoustic emission monitoring Two sensor arrays of two sensors each where used for monitoring One array was placed near a column girder interface and the other was placed near the mid span of a girder AE data was collected for both normal traffic loads and for an oversized truck load The results showed that 1 neither tests produced amplitudes greater than 70 dB 2 the signals obtained from the girder column joint where much higher than the girder mid span and 3 AE activity was higher during the overloaded truck test Inspection of the girder column joint revealed no structural damage so the AE activity was attributed to joint fretting CHAPTER 4 ACOUSTIC EMISSION BACKGROUND 4 1 Acoustic Emission Sources Acoustic Emissions AE are energy in the form of transient elastic waves released when materials undergo irreversible deformation Beattie 2013 AE can occur on the microscopic scale where the arrangement of atoms is permanently deformed AE can also occur on the macroscopic scale when a large amount of material is either plastically deformed or undergoes fracture While a material is still in its elastic state deformation of the material results in an internal force resisting the deformation When the stress in the material becomes high enough to exceed the elastic state permanent deformations begin to occur as plastic regions form
46. is possible because the BTN2 and BTS2 tests do not exceed all of the criteria for the first two criterion sets Only the third criterion set can identify fracture with all 6 criteria in all of the fracture beam tests as seen in Table 10 3 Showing the effectiveness for each number of criteria in Tables 10 1 10 3 helps to illustrate how each additional criterion may decrease the probability of identifying fracture Figure 10 1 provides a more striking illustration of the rate at which the various fracture criterion sets lose accuracy when a larger number of fracture criteria are required to be exceeded i e increasing Clearly fracture criterion set three does not lose accuracy to identify fracture over the entire range of j even when j J 85 Table 10 1 First criterion set effectiveness to identify fracture Min No of Criteria Exceeded No of Tests Effectiveness Min No of Criteria Exceeded No of Tests Effectiveness 86 N _ 100 80 o 9 60 o 2 40 20 0 0 2 0 4 0 6 0 8 1 12 Normalized Minimum No of Criteria j J Figure 10 1 Fracture Effectiveness During Fracture Beam Tests 10 3 Effectiveness of Fracture Criterion Sets in Rejecting Non Fracture AE Data The data from the data sets described in Chapter 9 where used to determine the effectiveness of the
47. means to simulate a fracture in the bridge when fracture is sudden and stress concentration factors are high These tests however may produce AE data with different characteristics if the beam is loaded in cycles to fatigue failure Further experimentation should be conducted on fatigue fracture in both a laboratory setting and on an in service bridge to help determine if the protocols and criteria developed in this project are applicable to fracture from fatigue or if different fracture criteria and data processing protocols are needed for fatigue crack detection Experiments have been conducted by others to monitor the local behavior of flexural members that develop fatigue cracking but experiments implementing sparse AE sensor systems have not been used yet for monitoring fatigue cracking Solar power is not recommended under most if not all circumstances to be the sole source of power for an AE monitoring system Problems will occur if large arrays of solar panels are installed adjacent to heavily traveled roads because as was experienced in this project ice snow de icing salts and other road debris as well as vandalism are believed to have caused damage on multiple occasions to the solar panels Moreover protective wire meshes installed to avoid most of the observed damage to the solar panels reduced the amount of incident sunlight on the panels Even when the solar panels were not damaged they were unable to continuously power t
48. most active for the second data set because both systems were operating and enough sun was available to power the two systems for most of the days Table 8 6 shows the criteria that where exceeded for each of the fracture beam tests Note that the fracture beams in BTNI and 51 debonded before allowing eligible sensors to detect AE from fracture Table 8 6 shows the criteria that were exceeded for each of the fully successful notched beam tests BTNI and BTN2 are excluded from this table because of inadequate AE transfer Given the success exhibited by the third fracture criterion set Table 8 6 it was deemed the most effective for long term monitoring of the Cedar Avenue Bridge Success is judged by all six criteria in the third fracture criterion set being triggered in each of the fracture beam tests that produced usable data LT1 LT2 LT3 BTN2 and BTS2 For the first Table 8 2 and second Table 8 4 failure criterion sets some of the fracture criteria were not triggered in tests BTN2 and BTS2 48 Table 8 5 Third fracture criterion set hit rate of Shits s over 20 seconds absolute energy rate of 4 25pJ s over 86 seconds Two of three consecutive sensors register 3 average count rate of 220counts s over 86 seconds Duration for a hit greater than 90dB exceeds 30ms 5 Events of source amplitude greater than 80dB form cluster of 2 events in 1 5 x1 3s 6 Cluster of 11 events in 22 2 75 Table 8 6 Third criterion set exceed
49. of the crack tip Yu Ziehl Zarate amp Caicedo 2011 A limitation of the counts parameter is that is a direct function of sensor properties such as resonant frequency and damping ratio and also a function of AE threshold so tests should be performed before using it in practice Energy of the voltage signal is another good metric for measuring fracture Energy of an event is calculated using Equation 1 Miller and McIntire 1987 1 1 U V t dt z 20 where U is the energy of the wave R 15 the resistance of the circuit and V is the voltage as a function of time Absolute energy rate has been found to be related to crack growth rate and has been used to predict crack growth and fatigue life in laboratory test specimens Yu Ziehl Zarate amp Caicedo 2011 In similar tests the peak in the energy rate has been associated with the onset of structural damage Beattie 2013 and a large increase in the cumulative energy rate has been observed at the point of critical fracture Barsoum Suleman Karcak amp Hill 2009 A way to display energy and help indicate fracture is by analyzing the distribution of the number of hits within discretized absolute energies Beattie 2013 Plots of hits vs absolute energy are used in the first criterion set described in Chapter 8 The development of source location techniques has added another important characteristic of fracture to the arsenal of AE parameters AE from fracture will propagate f
50. products or manufacturers Trade or manufacturers names appear herein solely because they are considered essential to this report ACKNOWLEDGMENTS The authors offer their gratitude to Moises Dimaculangan from the MnDOT Office of the Bridges and Structures who served as the technical liaison as well as Nancy Daubenberger Paul Kivisto Jihshya Lin Todd Niemann Paul Rowekamp Tom Styrbicki and Paul Pilarski also from the Office of Bridges and Structures and members of the Technical Advisory Panel The authors also express their gratitude to Mark Pribula MnDOT Metro Division for the time he took to assist our activities at the bridge site and to Duane Green also of the Metro Division who served on the Technical Advisory Panel The authors also appreciate the administrative assistance of Shirlee Sherkow and Bruce Holdhusen MnDOT Research Services Section for their efforts to keep this project on schedule and within budget The authors thank Paul Bergson of the Department of Civil Environmental and Geo Engineering at the University of Minnesota as well as fellow students Alireza Nojavan Jacob Robole Andrew Morgan and Sam Konieczny for their assistance in the maintenance of the bridge monitoring equipment TABLE OF CONTENTS Chapter l Inttodue BOR gei reo RE qus eden Eoo dvd oda cto cot ev 1 Chapter 2 Background Scope and Objective 2 PABILMdcucni
51. the SH II internal clock to the correct date uploading data to FTP and time SH II stopped collecting data on April 15 for reasons unknown 1 Mistras sable to communic te Inquire with Mistras about appropriate 4 23 14 with modem but cannot connect to HOUPICSDODURE procedure to take AA site visit No procedure provided by the SH II and states something is Mistras not working with the SH II i Modem is working SH II inoperative upon arrival Upon reboot system remains Inform Mistras of the findings who s 5 23 14 inoperative response is that the system may have been LED flashing signifying error damaged due to inappropriate use of solar Modem is working panels mesh protection on panels System Current 6 4 amps Upon reboot 5 begins collecting data Initial plan was to remove the SH II and 6 13 14 Modem is working send it back to Mistras for them to look at System continuously collected data it However SH II started working again for the next 73 days so it remained in place System current 2 3 amps SH II is acquiring data 6 19 14 Modem ceases to upload data to FTP site dan pee along Pole Modemi KoL E cables because only 3 of the 4 solar panels 8 1 14 were supplying power to the system System current 16 amps after Brok li aes f beu roken connection was located at one o the splice locations and fixed SH II is acquiring data Modem not working Try to reactivate modem No signal is
52. the crack growth rate equation because stress intensity factor is not easily defined in bridge members The experimental setup included five AE sensors placed in close proximity to the fracture region Emissions from fretting and other noise sources where filtered from the AE data by first running the fatigue test at a stress range too small to induce cracking The major characteristic of the noise data was that it consisted of hit amplitudes primarily below 50 dB Noise data was also further filtered using Swansong filters These filters characterize data from false AE events as unclean waveforms with small duration and long amplitudes Data that was filtered from the AE data set was not used in the data analysis During the tests the specimens where fatigue loaded until failure occurred AE data from the fracture was collected throughout the test Analysis of the AE data collected from the fatigue fracture showed that cumulative absolute energy and cumulative counts increased exponentially as did the length of the crack with increasing number of load cycles This infers that the rate of each of these corresponding parameters reached a maximum value as the crack reached a critical length Large increases in energy and count rates were also observed at the point when the cyclic loads where increased by 10 AE data from pre critical crack lengths were used to predict the growth of the crack at higher numbers of load cycles The relationship between crack
53. the north system and which is representative of an Note that no anomalous data was collected in the south system A low activity day for the third data set is defined as a day when fewer than three criteria where exceeded A high activity day for the third data set is defined as a day when three or four criteria where exceeded An anomalous day for the third data set is defined as a day when more than four criteria where exceeded See Table 9 10 for the number of days in each category The first relationship that is analyzed is the hit rate versus time plot for each of the 16 sensors in each of the AE systems Figure 9 5 and 9 6 Figures 9 5 and 9 6 show the cumulative number of hits for all the sensors instead of 16 individual plots for brevity The criterion associated with this type of plot is a hit rate of 100 hits in 20 seconds on at least two out of three adjacent sensors This criterion uses the same type of plot for the same data as in the second criterion set which is why new plots are not shown in this section This hit rate was decreased from that used in the second fracture criterion set 100 hits in 12 seconds so that the data from bridge fracture beam tests BTS2 and BTN2 would meet the criterion Also the change in the criterion allows sensors registering the high hit rate to be separated by a single sensor and still exceed the criterion i e two of three consecutive sensors This change was made to account for the possibility that
54. the structure and can also provide the approximate location of crack formation The MISTRAS Sensor Highway II data acquisition module was selected based on a study to determine the most suitable vendor to fit MnDOT s needs Schultz amp Thompson 2010 MISTRAS was the vendor for all components of the monitoring system including the sensors central computer solar panel power system and cellular modems Most of the traditional uses of AE spawn from the desire to monitor a single location or detail where a fracture is expected to occur Fracture is most likely to occur in regions of high stress or in connection details vulnerable to fatigue loading Schultz and Thompson 2010 document finite element analysis of a tie girder in the Cedar Avenue Bridge and identify the locations with the highest stress range in the girder at L3 and L3 which are shown in Figure 5 1 However with fatigue cracking fracture does not necessarily have to occur in the region of highest stress range because of the stochastic behavior of fatigue cracks Fatigue cracking is possible to occur at any location along the bridge and because this is a fracture critical bridge as much area as possible should be monitored Therefore a non traditional monitoring approach is adopted for this project by pushing the monitoring range of each sensor to minimize monitoring costs while still including all regions of the tie girder As part of this sparse sensor approach sensors are e
55. three criterion sets to reject non fracture AE data obtained from the Cedar Avenue Bridge Each criterion set was evaluated with its corresponding data set as described in Chapter 9 i e first data set with first criterion set etc The effectiveness to reject non fracture AE data 7 is defined using Equation 5 For example in Table 10 4 at least one criterion is exceeded 109 days of the total 193 days during the data set This means that using only one criterion a false positive would be produced 56 percent 109 193 of the time i e using one criterion is 44 percent effective Tables 10 4 10 8 show the effectiveness of using a given minimum number of criteria to analyze bridge AE data Note that the use of all criteria in each set is 100 effective but calculating the effectiveness of using less than all criteria can help indicate the evolution of accuracy for each data set as the minimum number of fracture criteria increases Figure 10 2 provides a more striking illustration of the rate at which the various fracture criterion sets gain accuracy when a larger number of fracture criteria are required to be exceeded i e increasing j Clearly fracture criterion set three gains accuracy at a faster rate with j especially when the AE data recorded with the South system is considered 87 Table 10 4 Non AE signal rejection effectiveness for first criterion set in South system Min No of Exceeded of Days
56. to be effective in both field and laboratory tests The algorithm consists of three steps all of which must be met for the event to be considered a true AE fracture event The ringdown counts of a hit need to be within a specific range the rate of occurrence of hits must be above a specified value and the hits must have originated from a single location All events that do not pass all three criteria are discarded The hits with a high frequency bias left over after filtering are considered to be AE from fracture Piezoelectric sensors were used in this monitoring scheme because of their high sensitivity to displacements The tradeoff here is that 4 piezoelectric sensors cover a narrower band of frequencies and distort the original waveform but allow for detection and location of very sensitive impulses The AE monitoring system was implemented on bridges both with known fatigue crack locations and on details that are susceptible to fatigue cracking In these monitoring tests arrays of two sensors spaced at 18 inches were placed on either side of the detail in question Guard sensors where used in cases when erroneous data was being collected at the center of the sensor array regardless of the array location Guard sensors helped to eliminate waves originating from other sources in the bridge In all cases AE monitoring technology was supplemented with visual and ultrasonic inspection In all cases the cracks detected by visual and ultrason
57. 0 18000 16000 s 14000 12000 10000 8000 6000 4000 A 2000 BN 56 Fi A m Q mum mm D S m u 1 68 70 72 74 76 78 80 82 84 86 88 90 92 94 36 98 100 Figure 61 Duration ps versus amplitude dB sensors 6 7 8 9 A 32 Absolute Eneray aJ vs Amplitude dB 6 9 5 0 008 4 5 008 4 0E 008 7 3 5 008 3 0E 008 3 2 5E 008 3 2 0 008 1 5 008 1 0 008 5 0E 007 0 0E 000 1 1 1 110 120 130 140 150 Figure 62 Maximum absolute energy aJ versus amplitude dB sensors 6 7 8 9 Absolute Energy a vs Time sec 5 9 5 0E 007 4 5E 007 4 4 0E 007 7 007 3 0E 007 3 2 5 007 2 0 007 1 5 007 1 0 007 5 0 006 0 0 000 1 I I 1 200 250 300 550 600 650 700 750 800 850 300 Figure A 63 Absolute energy rate aJ s during 86 second period including fracture sensors 6 7 8 9 A 33 Absolute Energy aJ vs lt 8 gt Absolute Energy aJ vs Time sec b Absolute Energy aJ vs Time sec 1 GFlt 1 0E 007 1 0E 007 5 0E 006 0 006 3 0E 006 4 5E 006 8 0 006 8 0 006 4 0 006 7 0 006 7 0 006 006 0 006 6 0E 006 3 0E 006 5 0 006 5 0E 006 2 5E
58. 00 1000 200 400 600 800 1000 200 400 600 800 1000 Absolute vs Time sec 8 Absolute Energy al vs Time sec 11 Absolute Energy al vs 16 10 10 200000 9 4 ER 180000 84 84 160000 7 1 7 140000 84 64 120000 54 54 100000 4 1 44 80000 34 34 60000 24 zi 40000 14 IET 20000 I I I 1 I I 1 071 I I 0 200 400 600 800 1000 200 400 600 800 1000 200 400 600 800 1000 Figure 38 Absolute energy rate aJ s during 86 second period including fracture individual sensors Counts vs 8 Counts vs Time sec lt 10 gt Counts vs Time sec 15 20 10 50 18 94 45 16 84 40 14 ze 35 12 54 30 10 s 25 8 4 4 20 6 34 15 4 24 10 2 1 5 071 I l I 1 07 I 1 200 400 600 800 1000 200 400 600 800 1000 200 400 600 800 1000 Counts vs Time sec 8 Counts vs Time sec 11 Counts vs Time sec 16 10 10 200 ELI 94 180 84 84 160 7 1 5 140 84 64 120 5 5l 100 44 44 80 34 34 60 2 1 24 40 14 14 20 1 071 1 1 200 400 600 800 1000 200 400 600 800 1000 200 400 600 800 1000 Figure A 39 Count rate counts s during 86 second period including fracture individual sensors A 21 5 Bridge Test South System Number 1 BTS1 Hits vs Time sec 5 8 Hits vs Time sec lt 6 gt Hits vs Time sec 1
59. 00 600 800 1000 200 400 600 800 1000 200 400 600 800 1000 Figure A 65 Count rate counts s during 86 second period including fracture individual sensors A 34 Time sec vs X Position lt All Channels Loc 1 h 7 8 E 2254 2205 2154 2104 2055 1954 Time 8 1 an 1 1 I 1 l l l l 1 1 U 1 l 1 1 180 160 140 120 100 80 50 40 20 0 20 40 60 80 100 120 140 150 Figure A 66 Time 5 versus event position in only events with source amplitude gt 80dB shown Time sec vs X Position lt All Channels Loc 1 h 02 8 I 195 4 Time 8 1 an 1 1 1 1 l l l l 1 l 1 l 1 I l 180 160 140 120 100 80 50 40 20 0 20 40 60 80 100 120 140 150 Figure A 67 Time s versus event position in all events A 35 APPENDIX NOTCHED BEAM FRACTURE TEST AMPLITUDE FILTERS The sensors in the notched beam fracture tests where often much closer to the location of fracture than a sensor monitoring the bridge would be The sensors in the bridge discard all hits with amplitude below 55dB In order to calculate hit rates from fracture tests that can be compared to bridge AE data the attenuation of signal amplitude must be accounted for An attenuation rate of 0 13 dB in was determined in Section 5 5 2 of the report This attenuation rate was assumed to be accurate for both the bridge and the fracture test girder In the bridge a hit of 55dB occurring midway between sensor
60. 018_53 66 07 23 1 0 1 0 1 1 4 8 19 2014 140613144018 54 1 67 09 56 0 0 0 0 0 1 1 8 20 2014 140613144018 54 2 68 10 38 0 0 0 0 0 1 1 8 21 2014 140613144018 55 1 69 09 03 0 0 0 0 0 0 0 8 22 2014 140613144018 55 2 70 10 46 0 0 0 0 0 1 1 8 23 2014 140613144018 56 1 71 14 51 0 0 0 0 0 1 1 8 24 2014 140613144018 56 2 72 06 49 1 0 1 0 0 0 2 8 25 2014 140613144018 57 73 07 36 0 0 0 0 0 0 0 APPENDIX F CRITERIA EXCEEDANCES OF THE THIRD CRITERIA SET SOUTH SYSTEM This appendix documents which fracture criteria where exceeded for each file in the third fracture criterion set using data collected in the south system A 1 denotes that the criterion was exceeded and a 0 denotes that the criterion was not exceeded Refer to Section 8 4 of the report for criterion definitions Table F 1 June 2014 criteria tabulation for South system 1 Criterion Exceeded 0 Criterion NOT Exceeded Criteria Day File 1 2 3 4 5 6 Total dd hh mm 6 1 2014 140908141854_0 0 08 48 0 0 0 0 0 0 0 6 2 2014 140910020454_0 0 01 08 0 0 0 0 0 0 140910041743_0 0 00 50 0 0 0 0 0 0 6 3 2014 140910072225 0 0 00 37 0 0 0 0 0 0 0 140910090709_0 0 02 19 0 0 0 0 0 0 140910164717_0 0 02 19 0 0 0 0 0 0 6 4 2014 6 5 2014 gt 140913002030 0 0 00 20 0 0 6 6 2014 140913132135 0 0 04 54 1 0 0 0 0 1 2 140913233350 0 0 00 32 0 0 0 0 0 6 7 2014 6 8 2014 140915072453_0 0 08 58 0 0 0 0 0 1 1 140916191708_0 0 00 20 0 0 0 0 0 0 6 9 2014 0 1409
61. 1 South system data collection efficiency 7 3 North System Data Collection Data collection for the north system was less consistent than for the south system over the course of the one year period when it was planned to have collected AE data The system required multiple visits to restart the SH II unit after this unit lost power it was be unable to restart and power the system again The south system frequently lost power but it was able to restart and return to an operational status when adequate sunlight returned Much of the trouble shooting and maintenance tasks are documented in Appendix D The monitoring goal of this system was to collect data for eight months over the course of one year Due to issues with the power supply and hardware data was collected during 5 of the months in the one year period Table 11 summarizes the periods when data was collected by the north system and it also gives a brief description of possible reasons why some periods of time yielded no AE data for this system 41 Table 7 2 Timeline of AE data records for the North system Begin Date End Date Description Comments Nov 1 2013 Mar 19 2014 No data is collected The batteries did not have high enough voltage to keep the system on and inadequate power was supplied from the solar panels Mar 20 2014 Apr 15 2014 This period of collection occurred after the system batteries where replaced The system collects continuous
62. 1 I 1 D 1 I 200 300 400 50 600 700 800 1 I I 1 I 1 10 200 300 400 500 600 700 800 1 I 1 I 1 10 200 300 400 500 600 700 800 Figure A 47 Count rate counts s during 86 second period including fracture individual sensors 25 A 6 Bridge Test North System Number 2 BTN2 Hits vs Time sec lt 8 11 gt I 80 100 120 140 220 240 260 280 300 320 Figure A 48 Cumulative hits versus time s sensors 8 9 10 11 Hits vs 8 Hits vs 10 Hits vs Time sec 15 100 oo 1 7 1 100 30 1804 90 80 80 70 70 60 60 50 50 40 40 30 30 20 20 10 10 0 I I 0 7 I I 1 I 0 1 I I l 140 160 180 200 220 140 160 180 200 220 140 160 180 200 220 Hits vs Time sec lt 9 gt Hits vs Time sec 11 Hits vs Time sec 15 20 10 140 160 180 200 220 140 160 180 20 220 Figure 49 Cumulative hits versus time s individual sensors A 26 Hits vs Freq Centroid kHz lt All Channels 1 I 1 1 I l l l 1 90 100 110 120 130 140 150 160 170 180 190 200 210 220 230 240 250 260 270 280 Figure 50 Hits versus frequency centroid kHz all sensors during fracture Duration us vs Amplitude dB 8 11 Figure A 51 Duration us versus amplitude dB sensors 8 9 10 11 A 27 Absolute vs Amplitu
63. 10 Solar Powered Sensor Highway System User s Manual Rev 1 Princeton Junction NJ Physical Acoustics Corporation Pollock A A 2003 Acoustic Emission Inspection Princeton Junction NJ MISTRAS Holdings Group Schultz A E Morton D L Tillmann A S Campos J E Thompson D J Lee Norris A J 2014 Acoustic Emission Monitoring of a Fracture Critical Bridge St Paul Minnesota Department of Transportation Schultz A amp Thompson D 2010 Development of an Advanced Structural Monitoring System St Paul MN Minnesota Department of Transportation Scruby C B 1987 An Introduction to Acoustic Emission Instrument Science and Technology 20 946 953 Sinclair A C Connors D C amp Formby C L 1977 Acoustic Emission Analysis during Fatigue Crack Growth in Steel Materials Science and Engineering 28 263 273 Yu Z Ziehl P Zarate B amp Caicedo J 2011 Prediction of fatigue crack growth in steel bridge components using acoustic emission Journal of Constructional Steel Research 67 1254 1260 96 APPENDIX FRACTURE BEAM TEST RESULTS This appendix documents the results of all of the notched beam fracture tests performed for this project LT1 LT2 and LT3 are the names of the tests performed in the Theodore V Galambos Structures Laboratory in the Department of Civil Environmental and Geo Engineering at the University of Minnesota BTN1 BTN2 BTS1 and BTS2 are the n
64. 12 and continued until October 31 2014 At the beginning of this time period the south original sensor array was still centered about the mid span of the girder In May of 2013 this sensor array was moved to the southern one half of the bridge thus the designation south system and a new system was installed in the northern one half of the bridge thus the designation north system Details of the south system including installation are available elsewhere Schultz et al 2014 The north system is nominally identical to the south system and a summary of the equipment and installation is provided in Chapter 5 Both systems where monitored over the course of the collection period and frequent maintenance and troubleshooting procedures were carried out to keep the systems operational A timeline of troubleshooting and maintenance procedures is shown in Appendix D Although the systems were not able to continuously collect AE data a large amount of data was collected and analyzed Enough data has been collected during this phase of the project to characterize the AE data that can be expected from the Cedar Avenue Bridge It is assumed that the vast majority if not all of the bridge AE data is produced by non fracture events because no cracks have ever been observed during inspection of the bridge This assumption is also strengthened by the fact that after evaluation of the bridge data using the proposed criteria no datasets that indicate fracture
65. 16225408_0 0 01 10 0 0 0 0 0 0 140917011535_0 0 00 18 0 0 0 0 0 0 6 10 2014 140917121346_0 0 00 58 0 0 0 0 0 0 0 140917141719_0 0 00 59 0 0 0 0 0 0 140918125959_0 0 00 29 0 0 0 0 0 0 6 11 2014 140918164802_0 0 00 58 0 0 0 0 0 0 0 140918184951_0 0 01 11 0 0 0 0 0 0 140919011431_0 0 01 07 0 0 0 0 0 0 6 12 2014 140919063307 0 0 00 53 0 0 0 0 0 0 1 140919103323_0 0 00 14 0 0 0 0 0 1 6 13 2014 6 14 2014 6 15 2014 6 16 2014 140923074640_0 0 08 40 0 0 0 0 0 0 0 6 17 2014 140924145736_0 0 07 35 0 0 0 140924233516_0 0 01 44 0 0 0 0 0 0 6 18 2014 6 19 2014 140926142443_0 0 03 42 0 0 0 0 0 0 0 140927045421_0 0 00 36 0 0 0 0 0 0 6 20 2014 140927083929_0 0 02 25 0 0 0 0 0 0 0 140927213118_0 1 01 13 0 0 0 0 0 0 6 21 2014 6 22 2014 140929222212_0 0 00 24 0 0 6 23 2014 140930015415_0 0 07 45 0 0 0 0 0 0 6 24 2014 6 25 2014 141002051853_0 0 07 37 0 0 0 0 0 0 0 6 26 2014 141003232446_0 0 00 33 0 0 0 0 0 0 0 6 27 2014 141004010104_0 0 08 36 0 0 0 0 0 0 0 6 28 2014 141005153627_0 0 09 04 0 0 0 0 0 0 0 6 29 2014 6 30 2014 141007005920_0 0 09 30 0 0 0 0 0 0 0 7 1 2014 7 2 2014 7 3 2014 7 4 2014 7 5 2014 7 6 2014 7 7 2014 7 8 2014 7 9 2014 7 10 2014 7 11 2014 7 12 2014 7 13 2014 7 14 2014 7 15 2014 7 16 2014 7 17 2014 7 18 2014 7 19 2014 7 20 2014 7 21 2014 7 22 2014 7 23 2014 7 24 2014 7 25 2014 7 26 2014 7 27 2014 7 28 2014 7 29 2014 7 30 2014 7 31 2014 14100813543
66. 3 0 141010125039 0 141010203743 0 141011093849 0 141011104344 0 141012072853 0 141013112541 0 141014191400 0 141014225853 0 141015075030 0 141015174553 0 141015192256 0 141015205945 0 141018091008 0 141019143234 0 141023095413 0 141024151446 0 141026011612_0 141026032737_0 141026052125 0 141026081033 0 141027003933 0 1 141027003933 0 2 141029001604 0 141030003415 0 141031001502 0 141031085813 0 141101011130 0 141101013001 0 141102001254 0 141102003004 0 141103004529 0 141103012833 0 141103020616 0 141103025101 0 141103032156 0 141103042847 0 141103053957 0 141103062638 0 141103075148 0 141104002728 0 141104004442 0 141104031701 0 141105001333 0 141105065334 0 141106000808 0 141107002215 0 141107004003 0 0 03 17 0 00 26 0 00 47 0 02 13 0 00 41 0 00 53 0 09 12 0 00 29 0 04 32 0 00 13 0 00 32 0 00 23 0 00 40 0 08 11 1 00 21 0 10 13 0 09 33 0 03 07 0 01 49 0 02 24 0 00 18 0 15 54 1 07 14 0 00 24 0 08 56 0 06 36 0 00 24 0 00 15 0 04 26 0 00 13 0 02 31 0 00 23 0 00 32 0 00 37 0 00 28 0 00 55 0 01 01 0 00 30 0 01 06 0 00 49 0 00 14 0 02 15 0 05 24 0 05 43 0 02 24 0 09 36 0 00 14 0 09 11 oooo 00 oO 0 000 0 pa 0 0 oo
67. 4 2605 2405 320 Hl 5 15 lt ax Figure A 20 Time s versus x position in on girder x 0 at fracture beam Time sec vs X Position All Channels Loc 2 300 4 280 2604 240 2204 2004 180 1604 1404 1204 104 80 604 40 25 4 15 Ul Time Figure 21 Time s versus x position in on girder x 0 at fracture beam events with source amplitude greater than 80dB only 12 A 3 Laboratory Test Number 3 LT3 Hits vs Time sec lt 4 8 gt Hits vs Time sec 1 Hits vs Time sec 3 Hits vs Time sec 5 Hits vs Time sec 7 GFIt gBssgsdss58 1 I I 420 440 400 420 Hits vs Time sec 2 Hits vs Time sec B 28838823888 1 1 1 360 380 400 420 380 400 420 440 380 Figure A 23 Cumulative hits versus time s individual sensors A 13 Hits vs Freq Centroid kHz lt All Channels GFIt 1 1 1 1 1 1 1 1 1 1 1 l 80 90 100 110 120 130 140 150 160 170 180 190 200 210 220 230 240 250 260 270 280 Figure A 24 Hits versus frequency centroid kHz all sensors during fracture Duration us vs Amplitude dB 7 8 Figure A 25 Duration us versus amplitude dB sensors 7 8 14 Absolute vs amp mplitude dB 7 8 1 0E 010 3 5E 008 3 0E 009 8 5 009 8 0E 008
68. 40 160 180 Figure A 56 Time 5 versus event position in only events with source amplitude gt 80dB shown Time sec vs X Position lt All Channels Loc 1 1 9 10 n Time I I 1 U 1 1 I 160 140 120 100 80 50 40 20 0 20 40 80 100 120 140 150 180 Figure A 57 Time s versus event position in all events A 30 A 7 Bridge Test South System Number 2 BTS2 Hits vs Time sec lt 6 9 gt 1 l I 1 l 1 1 l I 1 110 120 130 140 150 160 170 180 190 200 210 Figure A 58 Cumulative hits versus time s sensors 6 7 8 9 Hits vs Time sec 8 Hits vs Time sec 5 Hits vs Time sec 1 lt lt 100 7 504 304 454 80 4 404 1 60 1 80 200 220 1 O 1 200 220 1 60 1 80 20 220 Hits vs 8 Hits vs 2 GFlt 200 100 1804 90 1605 2 140 4 70 505 405 302 20 1 1 0 I I 0 I I 160 180 200 220 160 180 200 220 Figure A 59 Cumulative hits versus time 5 individual sensors A 31 Hits vs Freg Centroid kHz lt All Channels GFlt 1 l 1 1 1 1 1 I I I I I I I I 30 100 110 120 130 140 150 150 170 180 190 200 210 220 230 240 250 260 270 280 Figure A 60 Hits versus frequency centroid kHz all sensors during fracture Duration us vs Amplitude dB 5 3 34000 32000 30000 26000 24000 22000 2000
69. 51521 0 140827154938 0 140828132157 0 140829135641 0 140829213339 0 140830133456 0 140831132058 0 140831135431 0 140831151310 0 140831180608 0 0 09 28 0 21 48 0 09 14 0 08 18 0 00 12 0 00 32 0 08 31 0 09 33 0 07 09 0 00 37 0 00 57 0 03 03 0 00 49 0 00 18 0 00 17 0 00 32 0 04 42 0 00 50 0 01 02 0 01 40 0 00 20 0 00 34 0 00 54 0 00 23 0 09 05 0 08 38 0 09 03 0 08 55 0 07 06 0 02 36 0 07 26 0 00 28 0 00 28 0 04 53 0 01 00 0 09 01 0 00 59 0 00 17 0 01 36 0 00 37 0 00 33 0 01 29 0 00 18 0 00 31 0 04 48 0 07 53 0 07 32 0 00 32 0 09 06 0 00 23 0 00 58 0 01 52 0 00 50 oo oooo 00 00 0 oooo 00 oooo0ocoo0o0o0ocoo0o 0 0c 9c 0c 9c 0c 0c 9c 0 0 0 F 3 oooo 0 0 00 0 oo oooo 00 oooo O Ojo ojo oo 00 00 00 00 00 0 00 oooo O 0 0 00 0 oo oooo 00 00 0 S OO O 00 O O jO Ojo o 0 Oj S 9c oO oO 0c 0c oO Oo oooo O 0 000 0 oooo 00 00 0 oo Table F 3 August 2014 criteria tabulation for South system oo ooo 0 00 00 0 oooo0o 00 0 oooo 0c Ojo ojo 0c 0c 090 9c 9c 09C OoOo 0 0 oooo o 0 0 00 0 oo oooo 00 00 0 oooo0o 00 S S O jS OS S Ol O jO Ojo o ojo oO 9c O 9c 9c Ojo ooo 00 00
70. 87 1 Ea EE 3 3 3 EX 3 4 9 12 8 8 16 13143 104 7930 L 9 2 3 15153 104 68633 Ca 13303 104 78178 Ls Dorsal Ders 162 ss 5 16 4 3 12 444 12 446 13133 104 79190 2 1 24 4 3 10647 10648 1289 96 2 2 24 4 3 13214 13216 1256 96 7643 2 3 24 4 3 15820 15821 1226 96 7809 2 4 24 4 3 18366 18367 1373 96 6920 24 4 3 20875 20876 1243 96 77033 _ LLL Wave Velocity Average 796 RM 961 1 602 522 564 095 00 345 905 223 61 DU 8 735 10 296 1952 13 576 15 296 3 242 4 785 6 434 8 129 9 879 1572 10 809 12 375 13 975 15704 117 398 4 3 10773 112 556 Li 3 Tasso 4 3 16554 4 3 4500 6 023 N N ta 8 737 10297 1 953 13 577 15 297 3244 104 4 787 104 6 435 104 8 130 104 9 880 1106 104 11 573 104 1379 _ 6 12377 95 13 976 1344 96 115 705 95 117399 95 19145 95 10 774 12 557 1323 112 14451 16 355 4501 6 025 7 932 1 3 64 A 2 Table C 3 Group 3 pencil break v
71. 9 56 01 21 32 31 07 38 30 16 10 18 13 27 46 04 59 05 40 48 54 1 0 1 1 0 1 0 0 1 0 0 0 0 1 1 0 1 0 0 1 0 0 0 00 00 00 0000 ooo Ol O O F j O O O S O 0 O 00 00 00 0 S O OlO O O O O 0 0 00 00 00 PPP P PIP F O F O O F OR OR gt we OONN PPS e N P P N P Q Q O F O N N O Q PF Table E 3 August 2014 criteria tabulation for North system 8 1 2014 140613144018 39 2 49 09 03 0 0 0 0 0 0 0 8 2 2014 140613144018 40 1 50 11 29 0 0 0 0 0 1 1 8 3 2014 140613144018 40 2 51 13 46 0 0 0 0 0 0 0 8 4 2014 140613144018 41 1 52 15 27 0 0 0 0 0 1 1 8 5 2014 140613144018 41 2 53 14 37 1 0 0 0 0 1 2 8 6 2014 140613144018 42 1 54 14 48 0 0 0 0 0 1 1 8 7 2014 140613144018 43 1 140613144018 43 2 55 19 34 1 0 0 0 0 0 1 8 8 2014 0 0 0 0 0 0 0 8 9 2014 140613144018 44 1 1 1 57 14 51 0 0 0 0 0 0 0 8 10 2014 140613144018 44 1 1 2 1 58 16 19 1 0 1 0 1 1 4 8 11 2014 140613144018 45 1 59 10 23 0 0 0 0 0 0 0 8 12 2014 140613144018 45 2 60 09 40 0 0 0 0 0 1 1 8 13 2014 140613144018 46 1 61 10 17 0 0 0 0 0 1 1 8 14 2014 140613144018 46 2 62 07 22 0 0 0 0 0 0 0 8 15 2014 140613144018 47 1 63 05 50 0 0 0 0 0 1 1 8 16 2014 140613144018 47 2 64 07 52 0 0 0 0 0 0 0 8 17 2014 140613144018 48 0 140613144018 52 65 09 52 1 0 1 0 1 0 3 8 18 2014 140613144
72. B Data 51 9 3 5 Data v end ua q a S um 60 9 4 Thard Data S6et etas nw ente E et 72 Chapter 10 Effectiveness of Fracture Criteria nli ee rec eio ect HO peas 84 10 1 Definitions of BEHeetTVeness eut e etes tom Toe ped tc 84 10 2 Effectiveness of Fracture Criterion Sets in Identifying Fracture 85 10 3 Effectiveness of Fracture Criterion Sets in Rejecting Non Fracture AE Data 85 10 4 DISCUSSIOD uiii iss a ied eee to ceo 89 Chapter 11 Summary Conclusions and 91 DD Us Summary 91 142 91 11 3 Recommendations eiii ettet teet ren dee reta i t te i ee uia eine Tate 93 References m D EE 95 Appendix A Fracture Beam Test Results Appendix B Notched Beam Fracture Test Amplitude Filters Appendix C Velocity Calibration Results Appendix D Troubleshooting and Maintenance Timeline Appendix E Criteria Exceedances of the Third Criteria Set North System Appendix F Criteria Exceedances of the Third Criteria Set South System LIST OF FIGURES Figure 4 1 Lamb waves Left Symmetric mode Right Antisymmetric m
73. DANCES OF THE THIRD CRITERIA SET NORTH SYSTEM This appendix documents which fracture criteria where exceeded for each file in the third fracture criterion set using data collected in the north system A 1 denotes that the criterion was exceeded and a denotes that the criterion was not exceeded Refer to Section 8 4 of the report for criterion definitions Table E 1 June 2014 criteria tabulation for North system 1 Criterion Exceeded 0 Criterion NOT Exceeded Criteria Day File or File Range End Time dd hh mm 1 2 3 4 5 6 Total 6 1 2014 0 6 2 2014 0 6 3 2014 0 6 4 2014 0 6 5 2014 0 6 6 2014 0 6 7 2014 0 6 8 2014 0 6 9 2014 0 6 10 2014 0 6 11 2014 0 6 12 2014 0 6 13 2014 140613144018 0 1 5 0 1 0 0 0 1 1 3 6 14 2014 140613144018 1 1 0 1 0 1 1 4 140613144018_2 1 11 14 6 15 2014 140613144018_3 2 6 5 1 0 1 0 1 1 4 6 16 2014 140613144018_4 1 0 1 0 1 1 4 140613144018_5 3 5 13 6 17 2014 140613144018 6 4 12 4 0 0 1 0 0 1 2 6 18 2014 140613144018 7 5 12 43 140613144018 10 5 18 21 5 6 19 2014 140613144018 11 140613144018 12 6 11 17 1 0 1 0 1 0 3 6 20 2014 140613144018 13 1 7 15 26 0 0 0 0 0 0 0 6 21 2014 140613144018 13 2 8 16 39 0 0 0 0 0 0 0 6 22 2014 140613144018 14 1 9 11 31 1 0 1 0 1 1 4 6 23 2014 140613144018 14 2 10 9 44 1 0 0 0 0 1 2 6 24 2014 140613144018 15 1 11 11 30 1 0 0 0 0 1 2 6 25 2014 140613144018 15 2 12 9 34 1 0 1 0 0 0 2 6 26 2014 140613144018 16 1 13
74. E activity can be detected from non fracture mechanisms Due to this variability consistent trends in the AE noise data were not often observed For this reason multiple criteria were defined in the project for AE data to be considered to have originated from fracture Based on the evaluation of the bridge data using the proposed fracture criteria no AE data induced by fracture was collected by the monitoring system on the Cedar Avenue Bridge during the monitoring period Continued research is recommended to further develop the implementation of AE systems as monitoring technology for fracture critical steel bridges Implementation of sparse AE systems should be considered for steel bridges with different configurations such as multi girder bridges and truss bridges Evaluation of the fracture criteria developed as part of this project on other bridge types is essential Finally before the current system can be used with full confidence to supplement visual inspection of bridge components the system should be tested on a bridge or bridge model that undergoes fracture This test can be achieved by loading a decommissioned bridge or laboratory bridge model to induce fracture Such a test would validate the system and data evaluation methods so that they can be used on a large scale with even greater confidence CHAPTER 1 INTRODUCTION This report documents the development of an advanced warning system that was used to monitor a fracture critical stee
75. Figure 5 5 Power supply circuit Physical Acoustics Corporation 2010 5 5 Sensor Selection The AE sensors selected for this project are Physical Acoustics Corporation R15I LP AST sensors The sensors utilize the properties of a piezoelectric crystal which induces a voltage proportional to strain in the crystal Stress waves travelling through the structure will excite the piezoelectric crystal in the sensor The motion of the sensor is a function of the excitation as well as the physical properties of the crystal After the stress wave has passed the crystal will continue to ring at its resonant frequency which in this case is 150kHz The resonating nature of the crystal will insure that the waveform arriving at the data processing unit will always have a measureable frequency component at 150kHz 22 The 151 sensors contain a built in preamplifier and have the capability of performing an automatic sensor test AST that consists of sending out pulses for adjacent sensors to detect This test is intended to evaluate source location capability and general sensor sensitivity The PAC R151 LP AST is also a low pass resonant sensor and operates in a narrow band primarily between 100 kHz and 200 kHz as shown in Figure 5 6 Narrow band resonant sensors were chosen for this project because of their high sensitivity to disturbances in the structure Choosing a sensor with a lower frequency bound of about 100 kHz has the adva
76. Table 10 5 Non AE signal rejection effectiveness for second criterion set in North system m J 469 3 4 J 5 wo 7 39 p s Ss 5 6 E Table 10 6 Non AE signal rejection effectiveness for second criterion set in South system Min No of Criteria Exceeded of Days Effectiveness 46 24 Table 10 7 Non AE signal rejection effectiveness for third criterion set in North system Table 10 8 Non AE signal rejection effectiveness for third criterion set in South system No of Criteria Exceeded No of Days Effectiveness 9 xr 7 19 4 0 Ek 19 1 oak N mm mua 60 Set 1 South Set 2 North Set 2 South 20 _ E Set 3 North T Set 3 South 40 Effectiveness 0 0 2 0 4 0 6 0 8 1 1 2 Normalized Minimum No of Criteria j J Figure 10 2 Non Fracture AE Signal Rejection Effectiveness from Bridge Data Sets 10 4 Discussion Upon analyzing the effectiveness of a fracture criterion set to identify AE signals that contain fracture events the fewer the criteria in a set the more likely it will be to identify a fracture event However this is so only because fewer criteria mean a more lenient threshold has to be overcome and instances of fracture event identification are in reality cases of false posit
77. Tables 9 5 and 9 6 show the number days that a given number of criteria are exceeded The number of criteria exceeded varies each day however during no single day are all criteria exceeded thus the criterion set lead to the conclusion that no fracture events recorded in the second data set 70 Table 9 3 Frequency of exceedance for individual criteria using the second fracture criterion set and the second data set in the North system Table 9 4 Frequency of exceedance for individual criteria using the second fracture criterion set and the second data set in the South system J Fracture Criteria Counts number of days criterion is exceeded ayami 1 4 2 1 6 4 mexa 8 2 1 2 s 2 Mexa s 7 s 6 3 6 s s r G 4 Myma 8 e 5 7 mmu 7 5 2 4 6 m4 4 a 8 4 6 9 oaoa 6 Tea 3 71 Table 9 5 Number of days a given number of criteria were exceeded using the second fracture criterion set and the second data set in the North system Table 9 6 Number of days a given number of criteria were exceeded using the second fracture criterion set and the second data set in the South system No of Criteria Exceeded of Days 57 77 1 S e J 19 9 4 Third Data Set The third criterion set defined in section 8 4 was used to eva
78. a single sensor may be malfunctioning The second relationship analyzed with the third fracture criterion set is the absolute energy rate versus time as seen in figures 9 11 and 9 12 As discussed in the previous section absolute energy rate is calculated by dividing the change in a time step by the duration of the time step However in this criterion set the absolute energy rates of each individual sensor is analyzed instead of the system as a whole This allows the system user to gain a higher resolution of bridge activity To make sure the bridge AE data was comparable with the fracture test data the length of the time step was kept at a constant 86s about one thousandth of a day This time step was chosen because it is the smallest duration of a histogram bin that the software allows for a 24 hour data file Two of three consecutive sensors exceeding an average absolute energy rate of 4 25pJ s over 86s would exceed the second criterion For absolute energy plots of individual sensors refer to Appendix A 73 The third relationship analyzed with the third fracture criterion set and using the third data set is the count rate versus time Count rate is the number of times an AE signal will exceed a predefined hit threshold as seen in Figure 4 3 Throughout the monitoring phase of this project a threshold of 55dB is used This plot was added to the third fracture criterion set to represent the findings in literature of the direct relationship of stre
79. ach period showed that the criteria could differentiate between the bridge AE noise data and the fracture test data The AE noise data never met all of the criteria in the set whereas all criteria were met during each of the applicable fracture tests 17 Document Analysis Descriptors 18 Availability Statement Acoustic emission Fatigue tests Fracture properties Bridges No restrictions Document available from Steel bridges Structural health monitoring National Technical Information Services Alexandria Virginia 22312 19 Security Class this report 20 Security Class this page 21 No of Pages 22 Price Unclassified Unclassified 164 Protocols and Criteria for Acoustic Emission Monitoring of Fracture Critical Steel Bridges Final Report Prepared by Anton S Tillmann Arturo E Schultz Javier E Campos Department of Civil Environmental and Geo Engineering University of Minnesota June 2015 Published by Minnesota Department of Transportation Research Services amp Library 395 John Ireland Boulevard MS 330 St Paul Minnesota 55155 1899 This report represents the results of research conducted by the authors and does not necessarily represent the views or policies of the Minnesota Department of Transportation and or the University of Minnesota This report does not contain a standard or specified technique The authors and the Minnesota Department of Transportation and the University of Minnesota do not endorse
80. ames of the fracture beam tests performed within the Cedar Avenue Bridge For each parameter only certain sensor results are applicable in determining characteristics of bridge fracture data Tables A 1 through A 4 along with Figure 1 must be referred to in order to understand the relevance of each sensor in each of the plots Only the sensors that realistically simulate bridge fracture are used in creating the fracture criteria but this section includes additional sensor data for completeness O Value eligible for determining criteria Value not eligible for determining criteria O Value irrelevant due to fracture notched beam debonding Figure A 1 Fracture test results key Table A 1 Laboratory fracture test results Hit Rate hits s Energy Rate pJ s Count Rate counts s Sensor 171 LT2 tra LT2 LT3 172 173 L 2 150 j3333 so 9 163 146 4470 2917 521 67 168 3215 4662 2760 1 Table A 2 North system bridge fracture test results Table A 3 South system bridge fracture test results Rate hits s Energy Rate pJ s Count Rate counts s Sensor BTS1 BTS2 BTS1 BTS2 BTS1 BTS2 2 166 05 1 4 Table 4 Number of hits with duration 30ms and amplitude 90dB for each test Sensor s Hits gt 30ms amp gt 90dB A 2 A 1 Laboratory Test Number 1 LT1 Hits vs Time sec lt
81. ams were machined to the dimensions shown in Figure 6 1 The sections for BTNI and BTS1 where cut to the exact dimensions of the notched beams that were used in LT2 and LT3 The sections for BTN2 and BTS2 where similar with the only difference being that the hole diameter was decreased to increase the length of fracture to 3 8 The bottom flange of the beams were removed by machining save for a 6 segment by which to mount the specimen and a 2 25 segment to provide a flat surface to apply the jacking force The notch was cut with an electric discharge machining wire EDM 1 5 from the supporting flange The notch angle of 30 degrees was selected to provide a sufficiently large stress concentration to produce brittle fracture upon loading The circular hole cut just above the notch was to further facilitate beam fracture by reducing the moment of inertia of the cross section The hole also allowed the beam to undergo complete fracture in the region between the circle and the notch that in turn created prominent AE fracture signals The fracture area was increased in the second set BTN2 and BTS2 to increase the amount of AE activity by emitting a larger amount of fracture energy One study determined a rough range of 2 44 events per square millimeter of crack growth Sinclair Connors amp Formby 1977 so increasing the fracture area from 0 25x0 326in 0 375x0 326in increase in area of 26 29mm is expected to produce a notable difference A frac
82. ances Criterion Number 49 CHAPTER 9 ACOUSTIC EMISSION ANALYSIS OF CEDAR AVENUE BRIDGE DATA 9 1 Data Analysis Summary All the data that was collected during the time periods defined in Chapter 7 were analyzed and evaluated using one of the fracture criterion sets defined in Chapter 8 Each data file produced by the AE systems in the bridge was replayed in a desktop computer using Mistras AEwin software The software enabled plots of various parameters introduced in Chapter 8 to be analyzed and the data file to be evaluated using the relevant fracture criterion set The number of fracture criteria exceeded each day was recorded results of which are given in Appendices E and F Continuous bridge AE data was discretized into individual segments representing one day of data This procedure enables a user to dedicate a few minutes to analyze the data collected over a 24 hour time segment Such segments are believed to be a short enough to isolate and identify any possible fracture occurrences while also being long enough to reduce the time commitment to process multiple files In each of the sections of this chapter data plots from 1 anomalous records 2 records representative of periods with high levels of non fracture AE activity and 3 records representative of periods with low levels of non fracture AE activity are displayed and discussed The nature and source of the anomalous records are not known and their low frequency of oc
83. cal to the first one was added and the two systems were used to collect AE data from one of the tie girders spanning the Minnesota River in the Cedar Avenue Bridge AE data was collected from November 1 2013 until October 31 2014 data collected by the two AE systems was then evaluated to assess the efficiency of data processing protocols relying on fracture criterion sets developed in this project and defined in Chapter 8 Tables 8 1 8 3 and 8 5 To quantify the characteristics of AE fracture data several tests were performed in the laboratory and other ones tested in the bridge to determine threshold values for a series of parameters that were used to define fracture criteria for AE signals In these tests Chapter 6 notched steel beams were fractured and the AE signals were collected with the AE monitoring equipment The AE data collected during the fracture tests were used to develop data processing protocols and evaluation criteria in the form of criteria that rely on AE parameters computed by software provided by the equipment manufacturer Mistras AEWin The protocols and criteria were designed to discriminate between 1 true AE signals associated with steel fracture and 2 non fracture AE signals recorded on the Cedar Avenue Bridge and generated by non AE sources Each data file containing non fracture AE data recorded on the bridge was evaluated by counting the number of fracture criteria were exceeded No AE data file
84. cient of steel for frequencies between 100 and 500 kHz of 5 dB m Maji et al 1997 The attenuation coefficient is input into AEwin and used to determine the amplitude of events at the source The software increases the amplitude of the wave at the sensor to the amplitude of the wave at the source source amplitude by adding distance traveled multiplied by the attenuation coefficient 5 7 System Settings The SH II data acquisition system allows users to customize the data collection settings to fit the specific needs of the individual project Some of the customizable features of the SH II include preamplifier frequency filters waveform features to collect and timing parameters In general the systems in this project were set to collect as much data as possible in order to fully understand the characteristics of the AE data Therefore all hit driven time driven and frequency spectrum parameters where activated for the collection of the bridge data A high pass filter of 100kHz was used to block much of the AE noise of lower frequencies not associated with AE low pass filter of IMHz was used to block frequencies beyond the capabilities of sensor detection Other settings where left to the recommendations of MISTRAS such as pre amplification level and timing parameters SH II systems for the north and south systems where supplied with slightly different timing parameters MISTRAS stated that this will not make affect the AE data very m
85. criteria was developed from data collected in the laboratory notched beam tests and from BTS1 the only bridge notched beam test completed at the time Data from sensors on the bridge during BTS1 where not used because the fracture beam did not remain acoustically connected to the bridge during the test Many of the parameters discussed in previous literature and previous phases of this project where used to develop the criteria in this 45 set As noted in the AE literature a high hit rate high frequency bias high amplitudes with long duration and high absolute energy are all characteristics that together indicate fracture As seen in Appendix B these characteristics are found in all of the fracture beam tests Table 8 1 defines the first fracture criterion set These specific criteria where selected not only because they are representative of fracture but also because they can be evaluated using the AEwin software Efficient data evaluation is an important attribute of the AE data processing method so that it allows for effective use of time This set of criteria was used to evaluate the south system data from November 1 2012 to October 31 2013 i e the first data set Table 8 2 shows the criteria that where exceeded for each of the fracture beam tests Note that the fracture beams in and BTS1 debonded before allowing eligible sensors to detect AE from fracture Also BTN2 and BTS2 had not been conducted at the time the first fractu
86. criterion sets only J is needed However it is useful to evaluate j when j lt J and compare it among sets of criteria in order to see the rate with respect to minimum number of criteria at which the fracture criterion sets approach 100 effectiveness The effectiveness to reject non fracture AE data 7 is defined from data collected in the bridge as g J 22 5 where j is the minimum number of criteria from the fracture criterion set that are met during Np days and Np is the total number of days in the data set As proposed here j should be equal 84 to 100 when j J the maximum number of criteria in a given set 5 for set 1 and 6 for sets 2 and 3 because the notion is that none of the fracture criteria in a given set are triggered by AE data from non fracture events recorded on the bridge Thus to compare fracture criterion sets only J is needed However it is useful to evaluate j when j lt J and compare it among sets of criteria in order to see the rate with respect to minimum number of criteria at which the fracture criterion sets approach 100 effectiveness The effectiveness metrics Jand loosely correlated Assume that the parameters Ny and Np are interchangeable that is Nr 2 Np That would correspond to a case in which exactly one fracture event occurs every day in a bridge From Equations 5 and 6 it can be shown that 100 Of course this ideali
87. currence 26 times during a 2 year duration for the first and second data sets excludes them from being generated by heavy traffic that occurs every day In addition the vertical and horizontal scales are selected automatically by the AEWin software to maximize the viewing window for the data being plotted Consequently side by side comparisons of the same type of plot may not have the same scales if the magnitudes of the data sets being plotted differ The first data set consists of data only from the south half of the bridge because the north system was not yet operational The second data set includes data collected in both the north and the south system The first two data sets consist of all the data collected throughout this phase of the project The third data set is a subset of the second data set and consists of the data collected during the three months June July and August that the both systems where operating most consistently The third data set was especially useful for testing the third set of fracture criteria that was developed after the collection of all data for this phase of the project 50 9 2 First Bridge AE Data Set The first bridge AE data set was collected in the south AE system from November 1 2012 to October 31 2013 This data set was evaluated using the first set of fracture criteria as defined in Section 8 2 To aid in the description of the data analysis plots from three data files will be shown and are
88. data Apr 15 2014 Jun 12 2014 No data is collected The reason the system stopped collecting data is unknown Jun 13 2014 Aug 24 2014 The system collected continuous data during this period after being restarted on June 13 Aug 25 2014 Oct 31 2014 No data was collected Batteries could no longer keep system on continuously and system was unable to turn back on after losing power The data collection efficiency of the north system is illustrated graphically in Figure 7 2 The efficiency chart for the north system indicates how the system was susceptible to terminating data collection As in Figure 7 1 data collection efficiency is defined as the percentage of days in a month for which at least some data was collected For the north system to work it needed full voltage at the batteries otherwise it would require a site visit to restart the system The most successful months of data collection were the summer months June August 42 North System Data Collection Efficiency Nov 2013 Oct 2014 100 0 80 0 70 0 60 0 40 0 30 0 20 0 10 0 0 0 Feb Mar Apr May Jun July Aug Nov Dec Efficiency e e e o Sept Oct Figure 7 2 North system data collection efficiency 7 4 Solar Panel Power Source The numerous gaps in the data collection periods were all related to the power supply system that relied on solar panels During period
89. de dB 8 11 4 5E 008 3 5E 0084 2 5E 0085 2 0E 008 1 5 0085 5 0 0074 I I I 110 120 130 140 150 Figure A 52 Maximum absolute energy aJ versus amplitude dB sensors 8 9 10 11 Absolute Energy aJ vs Time sec 8 11 2 0E 007 1 9 0075 1 8 0074 1 7 007 1 6 007 1 5 007 1 4 007 1 3E 007 4 1 2 007 1 1 007 1 0 007 9 0E 006 8 DE 006 7 0 006 0 006 5 DE 006 4 0 006 3 0 006 2 DE 006 1 0E 006 0 0 000 1 l 1 1 1 1 I 1 I l 1 I l I l l l 200 250 300 350 400 450 500 550 600 650 700 750 800 850 300 950 1000 1050 Figure A 53 Absolute energy rate aJ s during 86 second period including fracture sensors 8 9 10 11 A 28 Absolute vs Time sec 8 Absolute Energy aJ vs Time sec 10 Absolute Energy aJ vs 15 GFit 1 0E 007 5 0E 006 2 0E 006 9 0E 006 4 5E 006 1 8E 006 8 0E 006 4 0E 006 1 006 7 0E 006 3 5E 006 1 4 006 6 0 006 3 0E 006 1 2 006 5 0 006 2 5E 006 1 0 006 4 0E 006 2 0E 006 8 0 005 3 0E 006 1 5 006 0 005 2 0E 006 1 0E 006 4 0E 005 1 0E 006 5 0E 005 2 0E 005 0 0E 000 WE i 0 0E 000 99 j j 0 0E 000 i i 200 400 600 800 1000 200 400 600 800 1000 200 400 600 800 1000 Absolute Energy a vs Tim
90. e as the crack propagated At this point the retrofit was installed to attempt to stop the crack from propagating further After the addition of the retrofit the AE data collection was flooded with noise from the bolted connections of the retrofit The large amount of fretting noise was only differentiable from cracking AE by the source location of the events The monitoring of the actual steel bridges was more difficult than the laboratory experiment because the fatigue crack was only monitored during a small portion of its life and the high stresses were not matched in the bridge testing Strain gauges with source location filtering of AE data were used to monitor the crack location as sand trucks where driven over the bridge Only 0 375 crack events where recorded per truck After the addition of the retrofits AE monitoring could not be used because the geometry of the retrofit members would not allow for accurate locations of sources to be located 3 4 Prediction of Fatigue Crack Growth in Steel Bridge Components using Acoustic Emission Yu Ziehl Zarate and Caicedo 2011 performed laboratory fatigue tests on specimen designed to develop fatigue cracks in order to determine the characteristics of AE events from fatigue cracking A model is introduced that relates the absolute energy of voltage waves produced by the sensors to the stress intensity range near the crack tip This relationship is used to replace the stress intensity factor term in
91. e north system produces a sufficiently large absolute energy rate to exceed the associated criterion threshold of 1x10 aJ 10pJ Absolute energy rate is a powerful parameter because it is unaffected by hit threshold level and it is dependent on both the magnitude and duration of AE activity In Figure 9 11 and 9 12 the vertical axis shows the absolute energy rate in attojoules 10118 joules second of the cumulative absolute energy collected by the system The horizontal axis shows 67 the time seconds after the data record began Note that the maximum energy rate of the data file increases from low to high to anomalous activity 68 NERNEEENENNE Figure 9 11 Absolute energy rate versus time for the second data set in the north system a Low activity day b High activity day c Anomalous day 69 a lu Lj ee c emm iod x Figure 9 12 Absolute energy rate versus time for the second data set in the south system a Low activity day b High activity day Tables 9 3 and 9 4 show the number of times each criterion was exceeded in each month Criterion one is very regularly exceeded because AE noise from traffic can often cause this criterion to be exceeded Criteria such as four and six are exceeded much less often and they consequently serve an important role in identifying AE data from fracture
92. e sec 9 Absolute Energy aJ vs 11 Absolute Energy aJ vs Time sec lt 16 gt 100000 2 0E 006 10 90000 1 8 006 EE 80000 1 006 8 70000 1 4 006 tia 60000 1 2 006 6 50000 1 0 006 55 40000 8 0 005 44 30000 0 005 3 20000 4 0E 005 2 10000 2 0 005 14 Oml 00 000 28 1 I Umum I 200 400 600 800 1000 200 400 600 800 1000 200 400 600 800 1000 Figure A 54 Absolute energy rate aJ s during 86 second period including fracture individual sensors Counts vs Time sec 8 Counts vs Time sec 10 Counts vs 15 500 500 200 450 450 180 400 400 160 350 350 140 300 300 120 250 250 100 200 200 80 150 150 60 100 100 40 50 50 20 DIE 1 I 0 D d I I I I 200 400 600 800 1000 200 400 600 800 1000 200 400 600 800 1000 Counts vs Time sec 8 Counts vs Time sec 11 Counts vs 15 50 200 10 45 180 94 40 84 35 7 30 64 25 54 20 44 15 34 10 25 5 ta DS 1 l l 1 Umm I I I 200 400 600 800 1000 200 400 600 800 1000 200 400 600 800 1000 Figure A 55 Count rate counts s during 86 second period including fracture individual sensors A 29 Time sec vs X Position lt All Channels Loc 1 GFit 200 5 E i 1704 Time l l 1 l l I l 1 1 1 l I I I 160 140 120 100 80 50 40 20 0 20 40 60 80 100 120 1
93. ected on July 11 2014 using the north system and which is representative of an anomalous day Note that no anomalous data were collected using the south system A low activity day for the second data set is defined as a day when fewer than three criteria where exceeded A high activity day for the second data set is defined as a day when three or four criteria where exceeded An anomalous day for the second data set is defined as a day when more than four criteria where exceeded See Table 9 6 for the number of days in each category The first relationship that is analyzed is the cumulative number of hits versus time plot for each of the 16 sensors in the north Figure 9 5 and south Figure 9 6 systems Figures 9 5 and 9 6 show the cumulative number of hits for all the sensors instead of 16 individual plots for brevity The two criteria associated with this type of plot Table 8 3 are a combined hit rate slope of 60 100 hits in 12 seconds for all sensors and hit rate of 100 hits in 12 seconds for two consecutive sensors The criterion for the high hit rate on adjacent sensors is obviously stricter that the one for all the sensors but having the two individual criteria provides greater differentiation of fracture and non fracture AE data Note the increase in the magnitude of the slope as the activity increases 61 Hite we Time pec Carnet O Figu
94. eel bridges The research conducted in this project includes 1 the analysis of AE data collected from the bridge 2 the completion of fracture simulation tests within the Cedar Avenue Bridge to study the system s response to actual fracture and 3 the analysis of laboratory fracture test data to accurately characterize AE from fracture Fracture simulation tests were performed in the bridge because the Cedar Avenue Bridge has not experienced any observable cracking during its service life and it is assumed to be a sound structure Without the fracture simulation tests there would not be any known fracture AE events measured within the bridge Data collected during the bridge fracture simulations and the laboratory fracture tests were used to determine the characteristics of AE data associated with fracture events After collecting AE data with the characteristics of fracture criteria were developed for evaluating data collected in the bridge using the AE monitoring system This AE data designated noise data from a two year collection period was analyzed AE Noise in this report is used to define all disturbances sensed by the AE system but which are not initiated by fracture AE Noise data is the data collected by the AE system from these AE noise sources Three different sets of criteria were used to analyze AE data collected in the bridge Due to the variability in bridge conditions and loading a wide spectrum of A
95. elocity results Group Test 4 d Gin S S ta s 2 s Dt ms DD in 72827 eum 64m 287 99 Wave Velocity in s 134502 134341 134341 133214 133214 133043 131741 131741 132286 133971 134454 04 04 04 275 9725 7257 96 u 16 050 16 050 832 112 2237 2238 7123 5 568 564 703 96 946 947 793 96 WaveVeledty Average Group f DD n 1 1 1 1 4 i 1 1 4 10267 96 Wave Velocity Average C 3 134615 133111 132602 132602 134775 135154 132725 133469 Wave Velocity in s 48660 89982 87589 87384 89837 73361 87589 88071 93503 82886 Table C 5 Group 5 pencil break velocity results 5 1 8 14 15 48 822 48 824 1746 112 6447 2 8 14 15 34097 34099 1935 112 57881 8 14 15 40537 40538 1448 112 77348 5 1 16 14 I5 15049 15057 8064 104 1997 24 14 15 59172 59174 1847 96 51976 24 14 15 2892 2893 1720 96 X 55814 24 14 15 6445 6448 3118 96 _ 30789 5 1 8 5 14 J1974 19746 1719 112 6554 5 2 8 15 14 22053 22055 2008 112 577 3 8 15 14 24 236 24 237 1721 112 650788 4 8 15 14 26516 26518 2040 112 _ 54900 1 16 15 14 8945 8947 1501 104 _ 69287 2 16 15 14 11407 11409 1613 104
96. ever this method of monitoring is believed to be able to adequately signal fracture while providing the most cost effective sensor arrangement possible 4 4 Acoustic Emission Parameters Every waveform that exceeds a user specified threshold is documented in the AE monitoring software as a hit The waveform that the software collects is actually the dynamic response of the vibrating piezoelectric crystal to the motion of the structure and not the surface waveform itself This can be seen in Figure 4 3 where the multiple oscillations shown are caused by the high resonant frequency of the crystal The shape of the actual surface waveform could be mathematically determined from the crystal response but does not necessarily resemble the 12 voltage waveform The voltage wave produced by the sensor i e piezoelectric crystal is assigned parameters by the software in order to characterize the wave The parameters of the waveform can be used to describe the wave and help determine if the wave is a byproduct of structural distress or merely a result of a nondestructive mechanism Figure 4 3 shows an idealized voltage wave collected by an AE system I Duration 4 Rise Time gt AE Amplitude AE Threshold AE Signal Time of Hit AE Counts Figure 4 3 Idealized voltage wave and selected parameters Pollock 2003 The waveform of Figure 4 3 is the output of the piezoelec
97. f the hits from noise had an average frequency of between 1kHz and 30kHz but there were also discrete values of higher frequencies with a fair number of hits This low frequency characteristic was used to filter out AE noise once the fatigue loading was started It was found that AE noise hits fall in an isolated region on a duration vs counts plot and for the most part can be distinguished from actual plastic deformation hits Three different types of notched beams were tested under fatigue loading A thin beam designed to fracture under plain stress a thick beam designed to fracture under plane strain and a medium size that was designed to fracture under a mixed mode The results of the tests are plotted as cumulative energy vs number of fatigue cycles plot The plot shows an initial increase in energy during initiation of the crack Then for a large portion of the test the cumulative energy gradually increases over time Finally at the time the crack becomes critically active the cumulative energy drastically increases 3 2 Acoustic Emission Monitoring of In Service Bridges Hopwood and Prine 1987 implemented AE monitoring on nine in service steel bridges in a test to determine if AE technology was capable of detecting fatigue crack growth in bridge components The AE monitoring used a filtering algorithm to discriminate AE noise data from the bridge and actual fatigue cracking AE The algorithm was based on empirical data and has been proven
98. from the Cedar Avenue Bridge This report contains 11 chapters and 6 appendices Chapter 2 provides the summary of previous phases of the project and the scope and objective of this phase of the project Chapter 3 provides an overview of previous experiments that have taken place in the field of acoustic emission monitoring Chapter 4 gives a background of acoustic emission technology and the theory behind the creation and collection of AE waves Chapter 5 describes the methodology being used in this monitoring project Chapter 6 describes the tests conducted to produce and collect acoustic emission waves from a fracture event Chapter 7 outlines the monitoring timeline and the data collection process Chapter 8 describes how the different fracture criterion sets were developed Chapter 9 describes the evaluation of the bridge AE data Chapter 10 consists of a discussion of the test results and data analysis Chapter 11 concludes the report with a summary and closing comments CHAPTER 2 BACKGROUND SCOPE AND OBJECTIVE 2 1 Background This report details the work done in the third and final phase of the Cedar Avenue Bridge Acoustic Emission Monitoring Project During the first phase of the project Schultz amp Thompson 2010 the monitoring technology of acoustic emission AE was selected for monitoring the Cedar Avenue Bridge AE technology was selected for the project because it was best suited to monitor fatigue cracking and fracture of weld
99. h day was uploaded to the FTP site Dec 12 2012 Apr 3 2013 No data files were uploaded to the FTP site during this time The reason for the lack of data files is unknown Apr 4 2013 Apr 7 2013 Data for the majority of each day was uploaded to the FTP site Apr 8 2013 Apr 26 2013 Few data files were uploaded to the FTP site during this time The reason for the fragmented data files is unknown Apr 27 2013 Nov 1 2013 Oct 31 2013 Dec 11 2013 Data from each day was uploaded to the FTP site except for 5 1 5 2 5 4 5 5 5 8 5 9 5 11 5 14 8 9 9 18 9 28 10 3 10 4 10 15 10 18 and 10 31 The time period of data acquisition during these days ranges from about an hour to the entire day Data for at least some period of time is collected during the days in this period except for 11 9 11 16 11 17 11 21 11 24 11 28 11 30 12 3 12 4 12 5 12 8 12 9 12 10 39 Table 7 1 continued Timeline of AE data records for the South system Dec 12 2013 Jan 13 2014 No data files are collected during this time This is possibly due to snow on solar panels or prolonged cloud cover Jan 14 2014 Feb 3 2014 Data for at least some period of time is collected during the days in this period except for 1 16 1 17 1 27 1 29 1 31 Feb 4 2014 Feb 19 2014 Feb 18 2014 Mar 1 2014 No data files are collected during this time
100. h system a 82 Table 9 8 Frequency of exceedance for individual criteria using the third fracture criterion set and the third data set in the south system 82 Table 9 9 Number of days a given number of criteria were exceeded using the third fracture criterion set and the third data set in the North system 82 Table 9 10 Number of days a given number of criteria were exceeded using the third fracture criterion set and the third data set in the South system 83 Table 10 1 First criterion set effectiveness to identify fracture 86 Table 10 2 Second criterion set effectiveness to identify fracture 86 Table 10 3 Third criterion set effectiveness to identify fracture 86 Table 10 4 Non AE signal rejection effectiveness for first criterion set in South system 88 Table 10 5 Non AE signal rejection effectiveness for second criterion set in North system 88 Table 10 6 Non AE signal rejection effectiveness for second criterion set in South system 88 Table 10 7 Non AE signal rejection effectiveness for third criterion set in North system
101. he AE systems in this experiment because of the lack of sun and snow ice cover in the winter reduced incident sunlight from the wire meshes and shadowing from the bridge members and nearby trees For these reasons a power supply that can support continuous monitoring is essential to assure that the monitoring system is operational if fracture occurs It is recommended that the system be powered with a standard 120V 60Hz alternating current from a reliable source such as the local electrical utility network A land based internet connection is recommended to insure that the monitoring system is always accessible for a remote login and or data upload Wireless connection are less reliable that land based internet connections and often suffer communication interruptions Furthermore wireless modem antennas are susceptible to factors such as vandalism damage during bridge maintenance and equipment failure If continuous monitoring is desired the SH II will need to upload files for replay to a website at specified time intervals In order to detect fracture bridge AE data should be analyzed daily The AEwin software has commands and options that can be used to facilitate this activity the auto file close criteria of AEwin should be set to close and reopen after an elapsed time of 24 93 hours the use continued files box should be unchecked to avoid redefining time domain boundaries for every analysis The AEwin software p
102. ible for use in development of fracture criteria as noted in Figure 6 6 and as described in the following C Value eligible for determining criteria C Value not eligible for determining criteria Value irrelevant due to fracture notched beam debonding Figure 6 6 Fracture test results key Data collected by the sensors is eligible to be used for criteria depending on the placement of the sensor and the parameter being measured In the laboratory tests sensor inputs are filtered so that small amplitude hits not likely to reach a sensor in the bridge before attenuating below 55dB are discarded Appendix C Because of this filter sensors on the girder in the lab tests detect realistic hit rates and can be considered in determining the hit rate criterion threshold The laboratory test sensors that are considered eligible for energy and count rates are only the ones sufficiently far away from the notched to simulate bridge sensor spacing In terms of the in bridge fracture tests only sensors positioned in their usual monitoring positions are considered eligible The results of the in bridge fracture tests show that fracture can be detected with the sensor spacing used for the monitoring of the bridge see Figure 5 2 for sensor monitoring positions The controlling values for three AE parameters that were found to be important in Chapter 8 are derived from the values in Tables 6 3 6 4 and 6 5 The hit rate absolute energy rate and coun
103. ic inspection were also detected by the AE monitoring system These tests documented AE technology s ability to be able to detect fatigue cracking of in service bridges 3 3 Acoustic Emission Monitoring of Fatigue Cracks in Steel Bridge Girders McKeefry and Shield 1999 used AE monitoring technology and strain gauge technology to monitor three in service steel bridges both before and after a retrofit The retrofit was designed to reduce the stress in a damaged region in the flange of a bridge member by transferring stress to a double angle AE technology was used to monitor the cracked region before and after the implementation of the retrofit in both the laboratory test and in the bridge retrofits The AE data was analyzed after each test in combination with the strain gauge data AE events that occurred while the stress in the flange was in maximum tension and originated from a specific location where considered to be possible crack events The member in the laboratory experiment was subject to fatigue loading both before and after the addition of the retrofit AE activity was observed to increase dramatically at the same time that cracking in the flange was observed Stress concentrations were relieved midway through the pre retrofit lab experiment by removing the fins on the underside of the flange AE from the vicinity of the crack decreased immediately after the stress reduction Well after the stress reduction AE activity continued to increas
104. identifying the cluster In these plots the vertical axis is the time seconds from the beginning of the data record and the horizontal axis is the distance along the direction of the bridge girder inches from the southernmost sensor 79 ree repone re 3 men se w Figure 9 17 Time versus event location for the third data set in the north system showing all events a Low activity day b High activity day c Anomalous day 80 um vam mut w a um mme mac a Lm ila m u x mm ku ww w Figure 9 18 Time versus event location for the third data set in the south system showing all events Tables 9 7 and 9 8 show the number of times each criterion was exceeded in each month Criterion one and six are the most commonly exceeded because they rely solely on a high number of hits and that condition is often created by AE noise Criteria such as two and four are exceeded much less often thus they play a more important role in identifying AE data from fracture Tables 9 9 and 9 10 show the number days that a given number of criteria are exceeded The number of criteria exceeded each day however during no single day are all criteria exceeded thus the criterion set lead to the conclusion that no fracture events were recorded in the third data set 81 Table 9 7
105. idi o po ei atii 12 4 5 Characteristics of Acoustic Emission from Fracture 14 Chapter 5 Cedar Avenue Bridge Monitoring Methodolosy 17 5A System OVerVieW n EEE EEE 17 5 2 System Installation eR 5 E 18 Did oyster GeOmet XV uyan Sut ua 19 E SII E ODi EEEE AEE AE EE Mtt eic M E EEA 21 5 9 SensoriSelectiOB qala ete breite ut ele uu tet PE 22 2 0 Sensor vacate AG see ee 23 56 1 Wave Velocity CaliBEatlOn eor oe uto 24 5 6 2 Wave Attenuation Calibration tio cipe 26 Se IBS m AS a Led 27 Chapter 6 Acoustic Emission Acquisition in Fracture Beam Test 28 GO L OVeEVISW ds euis cedat equ o pU edat tuo co opt a m R Mte eue 28 6 2 Notched Beam Fracture Test Summatry 29 6 3 Cedar Avenue Bridge Notched Beam Test Experimental Setup 20 6 3 1 Beam Specimen Fabrication 29 0 3 2 OTIC hm Mud ue c
106. iginal south system was moved to the southern half of the bridge Before installation the north system was used for a series of laboratory experiments during which a steel beam was fractured and the resulting AE data was collected The data collected during these laboratory tests would become the basis for developing criteria to be used in the Cedar Avenue Bridge capable of differentiating between fracture AE data and bridge AE data produced by non fracture sources 2 2 Scope This phase of the project consisted of 1 the collection of AE data produced by the bridge 2 the creation of data evaluation metrics and 3 the evaluation of the bridge AE data Data from the bridge was collected by downloading data stored on the system s data storage website and during times when the cellular connection was unavailable data was directly downloaded from the system s computer inside the bridge girder Data evaluation metrics where created in 2 the form of a criterion set based on fracture beam tests The criterion set was then used to evaluate the AE bridge data to determine if any fracture events had been collected by the bridge AE system Based on research performed during this phase of the project recommendations are made for advancing the use of AE technology in bridge health monitoring 2 3 Objective The overall objective of the project was to develop a system that is able to detect the onset of crack initiation and crack propagation i
107. il break tests Schultz et al 2014 have been used to determine a maximum sensor spacing of 20ft however it was still a concern that AE from the fracture lost too much signal strength traveling between beam diaphragm and bridge To help determine if the sensors were spaced too far apart intermediate sensors were placed halfway between array sensors and the diaphragm surface for BTS2 and BTN2 Figure 6 4 SENSOR t SENSOR 2 SENSOR 15 SENSOR 16 Ter o SENSOR 7 SENSOR SENSOR 9 Cs SENSOR 10 4 a ez 3 5 QA 5 OI LEL a b Figure 6 3 Sensor locations for BTS1 b BTN1 SENSOR 7 sensor SENSORS 3 WR l y 2 VQ 8 ME 2 5 SENSOR 2 SENSOR 16 SENSOR 1 1 SENSOR 15 3 af 3 EEE M 14 b Figure 6 4 Sensor locations for a BTS2 b BTN2 32 The location of BTS1 and BTS2 was between sensors 7 and 8 of the south system The 5 module is located between sensors 8 and 9 of the system thus this configuration allowed for easy communication between the jack pump operator and the computer operator BTN1 and BTN2 were conducted in the north system between sensors 9 and 10 The north system module is also between sensors 8 and 9 but of the north system Refer to Figure 5 2 for sensor locations throughout the bridge 6 3 4 Power Solution At the time of both BTNI and BTSI the north and south SH II systems were operating u
108. ilable for different bridge types The Kaiser effect is discussed which is the lack of AE at stress levels less than the previous maximum The Felicity Effect is introduced as the breakdown of the Kaiser effect and is often associated with structural distress Historic and severity indices where described to be the quantification of statistical analysis of AE parameters The historic index is the ratio of the average signal strengths of the later hits to the average signal strength of all the hits The severity index is the average signal strength of the most severe hits Together they can be plotted on an intensity chart where data points from greater structural damage will have a higher historic index as well as a higher severity index The report outlines a case study of AE technology to monitor a prestressed concrete bridge The purpose of the experiment was to assess the need for intermediate diaphragms in prestressed bridges under live loads AE sensors as well as other NDT equipment were used to assess any damage sustained during the loading tests Four AE sensors where placed in close proximity to one of the girder diaphragm interfaces This experiment included three loading tests and the loading was achieved by driving heavy sand loaded trucks over the bridge The AE data collected during the load tests was analyzed using the intensity analysis technique It was observed that high loading on the bridge lead to intensity plots with relatively high
109. ion set and the first data set Fre Criteria Counts mmber of days criterion is exceeded 1 a 4 3 2 9 9 9 9 9 9 wens o o 9 9 9 _ Amm 3 4 2 9 Mm s B 7 2 T 5 wap w 3 7 mom 7 3 Lens s L o T nu s L 4 T M TL 971 7 7 T o o TL 59 Table 9 2 Number of days a given number of criteria are exceeded using the first fracture criterion set and the first data set No of Criteria Exceeded No of Days of Days 9 3 Second Data Set The second bridge AE data set was collected in both the north and south AE systems from November 1 2013 to October 31 2014 This data set was evaluated using the second fracture criterion set as defined in Section 8 3 To aid in the description of the data analysis five plots will be shown and are described below l a Data collected on July 3 2014 using the north system and which is representative of a low activity day 1 Data collected on July 23 2014 using the south system and which is representative of a low activity day 2 a Data collected on June 28 2014 using the north system and which is representative of a high activity day 2 b Data collected on July 12 2014 using the south system and which is representative of a high activity day 18 3 Data coll
110. ives On the other hand when analyzing the effectiveness of a fracture criterion set to reject non fracture AE events the opposite is true That is the more fracture criteria are being used the less likely a false positive will be identified Thus an optimal fracture criterion set must have near perfect effectiveness for both identifying fracture 7 100 and rejecting non fracture AE events 7 100 Based on the study reported in this document a large number of relevant fracture criteria are needed at least 5 for the sparse ie widely spaced sensor application investigated here 89 For the fracture criterion sets developed here Tables 10 1 10 3 show that the third fracture criterion set is the most effective in identifying fracture It does not lose fracture identification effectiveness for as many as six simultaneous fracture criteria being exceeded Fracture criterion sets one and two were not able to identify fracture in the bridge fracture tests using all of their criteria The third criterion set in contrast was able to identify the fracture events in all the fracture tests even with the maximum number of fracture criteria being used E J 100 Similarly Tables of Section 10 4 10 8 show that all fracture criterion sets are 100 effective in rejecting non fracture AE signals J 100 when all criteria are used j J However the third criterion set appears to be the most effective when fewer than the maxim
111. l Engineering May June 280 286 Hellier C J 2012 Chapter 10 Acoustic Emission Testing In C J Hellier amp M Shakinovsky Handbook of Nondestructive Evaluation pp 10 1 10 39 New York New York The McGraw Hill Companies Higgins C M Senturk E A amp Turan T O 2010 Comparison of Block Shear and Whitmore Section Methods for Load Rating Existing Steel Truss Gusset Plate Connections Journal of Bridge Engineering March April 2010 160 171 Hopwood II T amp Prine D W 1987 Acoustic Emission Monitoring of In Service Bridges Frankfort KY Kentucky Transportation Cabinet Kosnik D E 2009 Acoustic Emission Testing of a Difficult to Reach Steel Bridge Detail Journal of Acoustic Emisison Vol 27 11 17 Maji A K Satpathi D amp Kratochvil T 1997 Acoustic Emission Source Location Using Lamb Wave Modes Journal of Engineering Mechanics Feburary 1997 154 161 McKeefry J amp Shield C 1999 Acoustic Emission Monitoring of Fatigue Cracks in Steel Bridge Girders St Paul Minnesota Department of Transportation Miller R K amp McIntire P 1987 Nondestructive Testing Handbook Volume 5 Acoustic Emission Testing United States of America American Society for Nondestructive Testing 95 Nair A amp Cai 5 2010 Acoustic Emission Monitoring of Bridges Review and Case Studies Engineering Structures 32 1704 1714 Physical Acoustics Corporation 20
112. l bridge An advanced warning system offers the potential to detect initiation and propagation of fracture in bridges and if so proper steps can be taken to alleviate the structural distress before further damage is sustained The need to monitor fracture critical bridges arises due to the concern over a bridge s inability to support itself after key members have failed Fracture critical bridges are not inherently unsafe however more care should be taken while inspecting these bridges because fracture in a key member can undermine the capacity of the bridge if the crack is allowed to propagate The tied arch steel bridge that carries Minnesota State Highway 77 over the Minnesota River was selected for this project This bridge is known as the Cedar Avenue Bridge MnDOT 9600N The advanced warning system was chosen to consist of commercially available monitoring equipment that detects the acoustic emission phenomenon as a structure is undergoing fracture The purpose of choosing to monitor the Cedar Avenue Bridge is not because the bridge is thought to be unsafe or susceptible to fatigue cracking The Cedar Avenue Bridge has not experienced any known cracking in its lifetime The bridge was chosen to serve as a platform on which to develop implement and test the monitoring technology This report documents the collection of data to insure the adequate operation of the system and the development of data analysis procedures for use on data collected
113. l in analyzing system wide AE activity 25 GIRDER DIAPHRAM EN 1 GIRDER SPLICE Figure 5 7 Obstructions between sensors 5 6 2 Wave Attenuation Calibration The second purpose of the pencil break tests is to determine the attenuation of a wave traveling through the bridge in order to validate a sensor spacing of 10 ft The data from the pencil break tests that is relevant to the calculation of attenuation are given in Table 2 Table 5 3 Attenuation pencil break test results E s s 0 112 a 1 N A 12 108 0 080 3 2 4 NA 4 0 205 Eum 0 232 12 fios 0 194 0 117 EGER DES 128 0 140 2 4 2 5 2 10 132 28 96 68 65 0 145 2 E 3 5 2 8 112 128 0 128 4 132 0 108 4 re l l pueti 0 114 0 103 0 133 0 114 0 064 0 128 26 The designations 5 52 55 S4 refer to the first second third and fourth closest sensors d d ds d4 indicate the distance to each sensor and A A denote the maximum amplitude of the signal at each sensor The attenuation also known as attenuation coefficient is the slope of the best fit linear line representing the data from each pencil break The average of the attenuation coefficient values in this experiment is 0 128 dB in 5 04 dB m and this value matches the attenuation coeffi
114. lationship analyzed with the third fracture criterion set and using the third data set is duration versus amplitude as seen in Figures 9 9 and 9 10 The fracture criterion associated with this plot is that a hit must exceed 90dB in amplitude and have duration of at least 30ms This fracture criterion is similar to one in the first and second fracture criterion sets however the duration threshold was dropped from 50ms to 30ms in order for the criterion to be satisfied by the data for fracture beam tests BTN2 and BTS2 The fifth relationship analyzed with the third fracture criterion set is the correlation of the time for an event versus the location of the event The events are filtered so that those with source amplitudes greater than 80dB are shown The fracture criterion considered here is that two events must occur within 1 5 inches and 1 3 seconds of each other Examples of this plot can be 76 seen in Figures 9 15 and 9 16 For these plots the automated software scans for clusters where two events occur within the 1 5 inches and 1 3 seconds and indicates its findings by identifying the cluster In these plots the vertical axis is the time seconds from the beginning of the data record and the horizontal axis is the distance along the direction of the bridge girder inches from the southernmost sensor 77 weetes pe rerne totar Own totar Cena ae oe snop mem eser i ie Ww 4 EI lt re 5 am
115. luate the third data set where the latter that is defined as the most active period of the second data set see Section 9 3 for both the north and the south systems The results of the evaluation of each data record are shown in Appendix E and F This data set is required to compare the efficiency of the third criterion set to the first and second criterion sets Because the third data set uses data from the second data set many of the same data plots are applicable to both second and third criterion sets Because of this relationship this section will reference the plots of the previous section when applicable The most active period of the second data set took place from June 1 2014 to August 31 2014 as seen in Figures 7 1 and 7 2 To aid in the description of the data analysis five plots are shown and described below The plots show how the third fracture criterion set in Section 8 4 where used to evaluate the data set 1 Data collected on July 3 2014 using the north system and which is representative of a low activity day 72 1 b Data collected July 23 2014 using the south system and which is representative of a low activity day 2 a Data collected on June 28 2014using the north system and which is representative of a high activity day 2 b Data collected on July 12 2014 using the south system and which is representative of a high activity day 3 Data collected on July 11 anomalous day 2014 using
116. m was purchased and installed first and the north system was purchased and installed approximately two years later Each system consists of 16 sensors evenly spaced at 10ft intervals The south system is the original system and for previous phases of the project it monitored one half of the bridge tie girder centered about the mid span of the bridge At the time of installation of the second system the original system was moved to the southern half of the bridge and the second system was installed in the north half of the bridge as seen in Figure 19 5 2 Sensors have remained in the locations shown in Figure 5 2 for the entirety of the current phase of the project with the exception of fracture simulation tests where selected sensors were moved into close proximity of the test region Sensors where moved back to their locations shown in Figure 5 2 after the tests i Po S ee ee Ik L Ln L NORTH MODEM LOCATION SOUTH MODEM LOCATION X TYPICAL SP ACING 9 SOUTH SYSTEM SH II BATTERIES AND CHARGE CONTROLLER NORTH SYSTEM SH4I BATTERIES AND CHARGE CONTROLLER SOUTHSYSTEM NORTHSYSTEM Figure 5 2 North and South system sensor positions and numbering The sensors are located in the downstream tie girder of the northbound half of the bridge and are
117. mber of hits is not larger than what has been observed for the Cedar Avenue Bridge on days of heavy traffic The anomalous data shows a rapid increase in hits throughout a large part of the collection period Reasons for this behavior are still unknown The criterion associated with this plot is a cumulative hit rate slope of the line that must exceed 100 hits in 12 seconds on any sensor 51 Fee ee Os hs fi soceri 0 200 a 70000 8000 1000 20000 wo soo 800 9000 Figure 9 1 Cumulative number of hits versus time for the first data set a Low activity day b High activity day c Anomalous day 52 The second relationship analyzed is the number of hits versus frequency centroid Figure 9 2 Frequency centroid is analogous to the center of mass of the frequency spectrum of the sensor response In both the high activity day and anomalous day two peaks are present One centered around 150kHz which coincides with the resonant frequency of the sensors and another at about 110kHz The low activity day has its peak at about 120kHz Fracture does not produce an exact known frequency but it is thought that higher frequencies are a characteristic of fracture The criterion associated with this plot is that the peak of the frequency centroid distribution must be above 160kHz during the period of the high hit rate In Figure 9 2 the vertical axis shows the number of hits at a specific frequency cen
118. medium between two sensors is calculated by producing an event at a known position and recording the difference in arrival times of the wave at the two sensors For the calculation of velocity in the Cedar Avenue Bridge events are created by pencil break tests at known distances from two sensors Source position sensor position and arrival time difference are input into Equation 2 to determine the velocity of the wave 2 Xg X2 X4 2 V At V is the velocity of the wave x is the position of the AE source x and x are the positions of the sensors where x is greater than xi and At is the time of arrival at x minus the time of arrival at X2 This equation theoretically produces division by zero when the source is at the midpoint of the two sensors In reality a source at the midpoint can produce a wide range of velocities depending on variations in the wave medium For either consideration it is not a good idea to perform the velocity calibration pencil breaks midway between two sensors For the most accurate results pencil breaks should be conducted close to one of the sensors Doing so forces the wave to travel over a larger span during the duration of At and therefore yields a more representative average velocity between the sensors Distances from the nearest sensor in the Cedar Avenue Bridge calibration tests range from 4 inches to 12 inches which is relatively close compared to the 120 inch span between sensors Pencil
119. meet multiple criteria to be associated with a fracture event and thus the assembly of criteria is organized as a set of criteria or criterion set AE data meeting some but not all of the criteria are not considered to have originated from a fracture event because each criterion is developed to be an indication of fracture A set of criteria is considered to be valid for use during continuous bridge monitoring with the assumption that fracture of bridge member will release fracture energy at least as large as that of the fractures of the notched beams The thinnest load carrying members of the bridge within the sensor array are the angles connecting the lower laterals gt thick to the tie girder A fracture with a length of about 0 25 in one of these angles would release energy equivalent to that for the fracture areas in BTN2 and BTS2 the largest notched beam fracture areas Three sets of fracture criteria were developed throughout the project Each new set of criteria were refined with the additional observations from more notched beam tests conducted at the bridge as well as the additional monitoring data from the north system Thus the growing collection of bridge monitoring data added to the knowledge base of the non fracture AE data which also informed the development of new fracture criterion sets The following sections describe the development of each fracture criterion set 8 2 First Fracture Criterion Set The first set of fracture
120. mplitude The criteria of the second fracture criterion set are shown in Table 8 3 This criterion set was used to evaluate data from the north and south systems from November 1 2013 to October 31 2014 i e the second data set Table 8 4 shows the criteria that where exceeded for each of the fracture beam tests Note that the fracture beams in and BTS1 debonded before allowing eligible sensors to detect AE from fracture Also BTN2 and BTS2 had not been conducted at the time the second fracture criterion set was developed Table 8 4 shows the criteria that were exceeded for each of the fully successful notched beam tests BTNI and BTN2 are excluded from this table because of inadequate AE transfer Table 8 3 Second fracture criterion set 2 Two adjacent sensors individually register 100 hits over 12 seconds 3 Anplitude of a hit on any sensor exceeds 90dB _ 4 Duration of a hit from any sensor above 90dB exceeds 5015 005 The absolute peak of the hits vs frequency centroid graph exceeds 140kHz 6 The absolute energy rate exceeds 10pJ s Table 8 4 Second criterion set exceedances 8 4 Third Fracture Criterion Set The third set of criteria was created in an effort to refine the second set by making it less susceptible to false positives This was accomplished by evaluating the fracture beam test data during the small time range when fracture was occurring instead of the entire record which included signals not
121. mputationally intensive to analyze each individual waveform making the parameterizing of each hit an essential process Rarely is a single transient wave isolated from all other disturbances to produce the idealized signal in Figure 4 3 It is more common to have multiple waveforms superimposed or close together to produce a noisy signal similar to that of Figure 4 4 Depending on the definition of a hit multiple transient waves could be included in a single hit or multiple hits could be counted from a single transient wave Timing parameters are introduced in order to avoid errors in defining hits and misleading data collection The following timing parameters are illustrated in Figure 4 4 Peak definition time PDT is the time after the peak amplitude that the system attempts to determine a new peak amplitude After the PDT has expired the original peak amplitude will not be replaced The hit definition time HDT is the time after the last threshold exceedance when the hit is ended The hit lockout time HLT is the time after the HDT has expired during which threshold crossings will not activate a new hit A new hit can only be started after both the HDT and the HLT have expired Maximum duration is the longest possible time that a hit can be recorded for before it is automatically ended for a new hit to begin sw vs Tian A rore GFL HDT HLT ae i Max Duration lt 4 dll it D D D
122. n the Cedar Avenue Bridge This objective was to be achieved through the use of an Acoustic Emission monitoring system The overall objective was further subdivided into project goals to help achieve the primary objective The goals of this project were 1 to determine the characteristics of an AE wave created from a fracture event 2 to collect bridge AE data to determine the characteristics of AE waves that are for the vast majority not from fracture and 3 to develop a procedure for monitoring and evaluating bridge AE data By achieving these goals the project will have advanced AE technology towards the realm of monitoring large portions of bridges or entire bridges with relatively few widely spaced sensors CHAPTER 3 LITERATURE REVIEW 3 1 Acoustic Emission Monitoring and Fatigue Life Prediction in Axially Loaded Notched Steel Specimens Barsoum Suleman Karcak and Hill 2009 performed an experiment using acoustic emission AE sensors to monitor fatigue cracking in an axial loaded notched beam The data collected during various stages where used to predict the fatigue life of the beam The experimental setup included a notched beam specimen of 305mm with the acoustic emission sensors placed on either side of the notch Ambient noise measurements were taken before applying stress to the beam in order to characterize ambient noise data produced by the fatigue loading equipment and other noise sources It was found that a vast majority o
123. nt and 12 18 13 inoperative mode designated by a anomalous system LEDs when system is blinking green LED inoperative Provide diagnosis results to System Current 2 0 amps Mistras 1 8 14 SH II believed to be off or Inquire with Mistras about anomalous LED inoperative a second time with no response Test voltages of batteries Retrieve the one d with highest and lowest voltage for charge 2 18 14 testing Results are two of the batteries can no longer hold charge and should be replaced SH II is off after batteries have been removed MDN for modem from this fall has 3 13 14 been given to random cell phone Discover moderate plan bas Deen lost by sprint user Modem account no longer exists for reasons unknown 3 17 14 Created new account for modem data plan account Replaced all four batteries Discovered only two of four solar panels pee were powering the system Checked the 3 20 14 connection at each panel and discovered a Modem was attempted to be activated loose connection which was then fixed Activate modem but sprint did not update the modem info on their end so it didn t D 1 work SH II is collecting data New sprint account is created and new plan 3 25 14 Modem needs to be activated on 1s created for modem with new MDN new account MSL MSID SH II is collecting data Modem is activated at the bridge Changed 4 10 14 Modem is connected to web and
124. ntage of filtering out some mechanical noise which is dominant in frequencies below 100 kHz Pollock 2003 This sensor also rejects AE noise that attenuates very quickly in the large expanse between bridge sensors Pollock 2003 dB ref 1V m s ms m 0 4 0 6 Frequency MHz Frequency response of the R15I AST Calibration based on ASTM E1106 Calibration based on ASTM E976 Figure 5 6 Frequency response of R1SI AST MISTRAS Products and Systems Division 2010 5 6 Sensor Calibration Pencil break tests were conducted at the time that the system was installed in the Cedar Avenue Bridge Schultz et al 2014 A pencil break test consists of breaking a pencil lead within the monitoring region and recording the arrival time and amplitude of the resulting waveform at multiple sensors AEwin software can calculate the source of an AE event given the velocity of the waveform and the difference in arrival times of the two sensors If AEwin is able to determine the position of the AE source the software can then calculate the amplitude at the source with the correct attenuation input Source location and source amplitude are only two of many features that are calculated in AEwin but these two features are the only ones that require field calibration testing since they depend on data collected at multiple sensors 23 5 6 1 Wave Velocity Calibration The velocity of a wave in the
125. o insure a secure connection The beam location allowed for 12 9 of free space between the notched beam and the ceiling top flange of tie girder An 11 tall hydraulic jack was placed with its supporting base on the beam and oriented so the cylinder jacking action was against the ceiling For the first set of tests Velcro was used to secure the jack to the beam in order to hold the jack in place before and after loading For the second set of tests the jack was manually held in place until the jacking force created enough friction to hold it in place for the tests Figure 6 2 Field setup for notched beam test 6 3 3 Sensor Locations For the first set of tests the two outermost sensors in the sensor array were relocated onto the notched beam itself The purpose of these sensors is to help determine the time when the majority of the fracture took place Data from surrounding sensors can then be analyzed during that time to determine if fracture characteristics are present Figures 6 3 and 6 4 show the placement of these sensors and the relative beam location of surrounding sensors 31 The surrounding sensors in the first set of tests did not detect a significant amount of AE This is most likely because the beam did not stay adequately bonded to the bridge as discussed in section 6 3 5 on data collection There was also a concern that the sensors were spaced too far from the notched beam to be able to detect the sound of its fracture Penc
126. ode 10 2 eee 10 Figure 4 3 Idealized voltage wave and selected parameters Pollock 2003 13 Figure 4 4 Timing parameters used to define an individual hit esses 14 Figure 5 1 Connection spacing and naming eed enero ane tei eut 18 Figure 5 2 North and South system sensor positions and numbering 20 Figure 5 3 Walking bridge adjacent to monitored tie girder photo 21 Figure 5 4 Walking bridge adjacent to monitored tie girder plan view 21 Figure 5 5 Power supply circuit Physical Acoustics Corporation 2010 22 Figure 5 6 Frequency response of R151 AST MISTRAS Products and Systems Division 2010 23 Figure 5 7 Obstructions between Sensors pir at gum ends 26 Figure 6 1 Notched beam specimen profile for a BTNI BTSI b BTN2 BTS2 All dimensionsoam 1866s cn Shes choca ripe hte eI ein MISIT 30 Figure 6 2 Field setup Tor notched beam test eet out d Eod etg potere Im 3l Figure 6 3 Sensor locations for BTSI b BTN1 32 Figure 6 4 Sensor locations for BTS2 b BTN2
127. om N270 wide temperature range CPU 2GB internal 550 64GB SATA 550 Windows XP operating system AEwin ready and Ethernet connectivity to a factory network or Internet Time synchronization capability between units up to 12 feet 110 220VAC or 9 28 VDC power at 30 watts SH 4 AE four 4 channel AE plug in module for Sensor Highway with 1 MHz AE bandwidth 9380 7003 AEwin SH 16 software for automated AE data collection file link signal and alarm processing and remote communication software 9380 5165 Solar Panel Kit stand alone 520 watt solar power kit with 4 days of battery backup Includes four 130 watt Solar Panels four 110Ah batteries with enclosure 45A charge controller 400 watt AC inverter with enclosure and mounting pole and hardware 5 9380 5035 Cellular wireless 3G modem with remote CPU reset capability __ NEMA enclosure for extemally mounted modem 9800 7110 RMA Remote T setup setup charges includes AE system preparation for remote access phone email support and standard web hosting account setup charges 16 low power pre amplified sensor 150kHz with 26 dB gain AST coated for outdoor use 5 meter coaxial RG 58A U cable and BNC connectors On Site support by one MISTRAS employee for two days includes travel amp expenses 5 3 System Geometry The Cedar Avenue Bridge monitoring equipment is comprised of two individually operating systems The south syste
128. onitor a large area of the bridge structure proved to be a challenging task Without the ability to filter AE noise from outside the monitoring region the AE sensors where at the mercy of complex combinations of sound waves from a multitude of sources Non fracture sources were observed to produce very high hit rates at times and strong intensities at others The key to discarding false positives from non fracture sources is having multiple fracture criteria that target various characteristics of AE signals from fracture events including location of the source of the AE activity Despite the anomalous and high activity data sets discussed in Chapter 9 the AE system with far spaced AE sensors and the proposed fracture criteria particularly fracture criterion set three holds promise for differentiating fracture and non fracture AE events in steel bridges It is probably necessary to perform some fracture tests using the notched beam test developed as part of this study when implementing far spaced AE sensor systems in other bridges 92 11 3 Recommendations The following recommendations are offered in regards to future use or research concerning the use AE sensor systems in fracture critical steel bridges especially if a sparse sensor network is being considered The fracture tests used in this project to determine AE fracture characteristics have used a test specimen that is acoustically connected to the bridge This test setup is an efficient
129. p lt Wa da da lt S We lt n 4 t P n w ww peste petet tom 1 lt nm laaa T oan ss a Ls e D 8 9 2 w D i i w n n w TJ c Figure 9 15 Time versus event location for third data set using the third fracture criterion set in the north system showing source amplitudes greater than 80dB a Low activity day b High activity day c Anomalous day 78 b Figure 9 16 Time versus event location for the third data set using the third fracture criterion set in the south system showing source amplitudes greater than 80dB a Low activity day b High activity day The final relationship analyzed with the third fracture criterion set and using the third data set is time versus the position of the events For this plot all events are plotted regardless of their amplitude The criterion associated with this plot is that 11 events must occur within 22 inches and 2 7 seconds of each other For these plots the automated software scans for clusters where 11 events occur within the 22 inches and 2 7 seconds and indicates its findings by
130. power the AE system continuously throughout the year they would not produce enough power during dark winter months and under the cover of snow After a single spell of little sunlight the batteries can become drained and may only be able to keep the system running during the daytime For these reasons solar panels seriously undermine the reliability of the monitoring system and they should not be used to power AE sensor systems for long term monitoring of bridges and other transportation structures 44 CHAPTER 8 FRACTURE CRITERIA DEVELOPMENT 8 1 Development of Fracture Criteria The purpose of the fracture criteria in this project is to differentiate AE data collected during fracture from AE data from other sources collected during continuous bridge monitoring The fracture criteria must be clearly satisfied when evaluating the AE data collected during the fracture beam tests Moreover more than one fracture criterion must be used to evaluate the AE data because non fracture AE data can vary greatly from among AE data files recorded by the same sensor but at different times For example non fracture AE data from impact loading the bridge will yield only a few events but these will feature large amplitudes On the other hand AE data from fracture may have low amplitudes depending upon the distance to the sensors but will produce a large number of hits and trigger other associated AE fracture parameters For these reasons AE data must
131. project Phase II Schultz et al 2014 included a set of these tests that will be referred to as the laboratory notched beam fracture tests The tests done in Phase where performed in the Theodore V Galambos Structures Laboratory of the Department of Civil Environmental and Geo Engineering at the University of Minnesota These laboratory notched beam fracture tests produced very distinct AE results that could be easily differentiated from AE noise data collected at the Cedar Avenue Bridge In the laboratory notched beam tests steps were taken to realistically simulate a fracture in the bridge by mounting the small fracture beam on a large girder representing the bridge girder The results of these tests formed the basis for a set of criteria that could be used to indicate fracture The controlled nature of the laboratory notched beam tests allowed for a strong correlation between fracture and AE parameters because of the relative close proximity of the sensors and the absence of AE noise However in the Cedar Avenue Bridge sensors are spaced farther apart and AE noise is almost always present So the question arose could a similar fracture in the Cedar Ave Bridge be detected given the current spacing of the sensors and the unique geometry of the box girder and its diaphragms To answer this question a series of notched beam fracture tests were conducted inside the Cedar Avenue Bridge AE sensor arrays If these tests could produce similar
132. rate of a fatigue loaded specimen This report defines three mechanisms of fatigue crack growth that exhibit slightly different AE behavior which can be observed experimentally in a highly controlled environment The three mechanisms are 1 new yielding at the edge of the plastic zone 2 microfracture in the region of intense plastic strain at the crack tip and 3 unsticking of partially rewelded areas Mechanisms where new surfaces are created were found to have amplitudes proportional to the stress intensity factor that induced the cracking Also the number of AE events collected was proportional to the area of surface created This experiment is important because it shows how AE activity can be representative of the state of stress and crack propagation during fatigue cracking of a specimen 3 6 Acoustic Emission Monitoring of Bridges Review and Case Studies Nair and Cai 2010 reviewed AE monitoring techniques and analyses with an emphasis in cases applied to bridge monitoring The advantages and disadvantages of AE monitoring methods are discussed Advantages are that material dynamics are observable in real time because of continuous monitoring and damage generated AE can be documented without precise sensor placement The disadvantages are that 1 discrimination of noise requires several trial monitoring sessions 2 quantitative AE analyses are difficult for actual bridges and 3 standardized procedures are not universally ava
133. re calculated in this chapter 1 the effectiveness of the criterion set to identify a fracture and 2 the effectiveness of the criterion set to reject non fracture AE signals The effectiveness to identify fracture 7 is determined by using the criterion sets in situations where fracture was known to occur namely the fracture beam tests The effectiveness to reject non fracture AE data 4 is determined by using the criterion sets in situations where fracture is known not to have occurred namely the AE data collected in the bridge when fracture tests were not being conducted These metrics are appropriate for the evaluation of the fracture criterion sets because accurate fracture criteria must be able to effectively identify fracture when it occurs i e fracture beam tests and to reject non fracture AE signals when fracture does not occur i e bridge data The effectiveness to identify fracture is defined from AE data recorded during the fracture beam tests as 200 100 4 T where is the minimum number of criteria from the fracture criterion set that are met in Nr fracture beam tests and Nr is the total number of fracture beam tests As proposed here 70 should be equal to 100 when j J the maximum number of criteria in a given set 5 for set 1 and 6 each for sets 2 and 3 because the notion is that all fracture criteria in a given set are triggered during a fracture event Thus to compare fracture
134. re 9 5 Cumulative number hits versus time for the second data set in the north system a Low activity day b High activity day c Anomalous day 62 Hits vs lt All Channeli His vs Timelsec o Channet gt 0 50000 100000 150000 b Figure 9 6 Cumulative number of hits versus time for the second data set in the south system a Low activity day b High activity day The second relationship analyzed is the number of hits versus frequency centroid Figures 9 7 and 9 8 The criterion threshold for the peak of the frequency centroid distribution was changed from a value of 160 kHz in the first fracture criterion set to a value of 140 kHz in the second fracture criterion set which is used here Doing so resulted in a more conservative criterion to account for the inherent uncertainty of frequency analysis of AE data In Figure 9 7 the vertical axis shows the number of hits at a specific frequency centroid value The horizontal axis shows the frequency centroid in kilohertz which is the centroid of the power spectrum of the waveform Note that the peak of the frequency centroid distribution is higher for the more active data sets Figures 9 7b 9 7c 9 8b and 9 8c 63 D 90 100 no 120 130 19 150 1960 170 19 1 2X0 210 22 90 30 100 10 120 19 19 150 1960 170 19 119 2X0 210 220 30 100 110 120 120 140 150 160 170 190 190 200 220 c Figure 9 7 Number of hits versus frequency cen
135. re A 14 Hits versus frequency centroid KHz all sensors during fracture Duration us vs Amplitude dB 7 8 340000 320000 300000 280000 260000 240000 220000 200000 180000 160000 140000 120000 100000 80000 60000 40000 20000 1 U l l jl il 1 il 70 72 74 76 78 80 82 84 86 88 90 92 94 96 98 Q ws g Figure A 15 Duration us versus amplitude dB sensors 7 8 9 Absolute vs amp mplitude dB 7 8 2 0 010 1 010 1 8 010 1 7 010 1 010 1 5E 010 1 4 0105 1 3E 0104 1 2E 0105 1 1E 010 1 0E 010 1 3 0E 008 7 8 0E 009 7 7 0E 0095 5 0E 008 1 5 0E 008 4 0E 009 3 3 0E 008 7 2 0E 008 1 1 0 008 7 0 0 000 I 1 l 110 120 130 140 150 Figure A 16 Maximum absolute energy aJ versus amplitude dB sensors 7 8 Absolute vs Time sec 7 8 5 0E 008 4 5E 008 4 0E 008 3 5 008 3 0 008 2 5E 008 2 0E 008 1 5 008 1 0 008 5 0 007 0 0E 000 1 1 I l l l l I l l l 1 300 350 400 450 550 600 650 750 850 300 950 1000 1050 Figure A 17 Absolute energy rate aJ s during 86 second period including fracture sensors 7 8 A 10 Absolute Energy aJ vs Time sec 1 2 06 008
136. re criterion set was developed Table 8 2 shows the criteria that were exceeded for each of the fully successful notched beam tests BTNI and BTN2 are excluded from this table because of inadequate AE transfer Table 8 1 First fracture criterion set riterion Numbe Hit rate of 500 hits per minute for any given sensor Peak of frequency centroid distribution exceeds 160 kHz for the time period and sensor with the high hit rate Table 8 2 First criterion set exceedances 172 xe 46 8 3 Second Fracture Criterion Set The second fracture criterion set was the first one to be used to evaluate both the north and south systems This fracture criterion set was developed using the data from the laboratory fracture beam tests as well as BTSI and However both BTS1 and BTNI experienced the uncoupling of the fracture beam from the bridge so the sensors on the bridge during these tests did not collect an adequate amount of fracture data The second fracture criterion set varied slightly from the first in that a sixth criterion was added and the method for using the absolute energy parameter was changed Hit rate for the entire sensor array was added as a criterion because it allowed for a better understanding how the hit rate at individual sensors varies from the rest of the sensor array Also the maximum absolute energy in a hit was replaced by the absolute energy rate to make the energy criterion independent of a
137. results to the laboratory notched beam tests then detecting fracture should be feasible in the Cedar Avenue Bridge during continuous health monitoring 28 6 2 Notched Beam Fracture Test Summary In addition to the three notched beam tests conducted in the Theodore V Galambos Structures Laboratory four notched beam tests were conducted inside of the Cedar Avenue Bridge two in each of the north and the south systems To keep references to specific notched beam fracture tests brief test designations as well as test features are shown in Table 4 Table 6 1 Notched Beam Fracture Test Summary 6 3 Cedar Avenue Bridge Notched Beam Test Experimental Setup 6 3 1 Beam Specimen Fabrication An S4x9 5 structural steel beam of length 24 inches was used to fabricate the notched beam for all four bridge notched beam tests The steel beam was made from ASTM A992 hot rolled steel The properties of the steel closely match the bridge girder steel M H D 3309 that conforms substantially to ASTM A242 Higgins et al 2010 Some properties for each steel type are shown in Table 5 Any variation in the generation and transfer of AE waves in the two types of steel are assumed to be negligible because of the similar properties Table 6 2 Bridge and notched beam steel properties Yield Strength Ultimate Tensile Strength Application Steel Type ksi ksi Notched Beam Specimen 50 65 Bridge Girder ps 50 70 29 The be
138. rom a point source relative to sparsely spaced sensors Therefore a high rate of events at a specific location can be an indication of fracture Events emanating from a specific source location have been used to discriminate between non AE events and AE from fracture McKeefry amp Shield 1999 Hopwood II amp Prine 1987 Microcracking has been observed to produce a large number of events of smaller amplitude and as the fracture becomes visible macrocracks are observed to generate fewer events but of larger amplitude Colombo et al 2003 In the absence of source location high hit rates can also be used to help indicate fracture A hit is defined as a transient wave occurring at individual sensors while an event is comprised of a wave from a single source hitting multiple sensors If sensors are spaced far enough away that hits from an event will only 15 reach a few sensors then hit based characteristic acts similarly to an event based characteristic but without as much precision Genuine AE hits from fracture generally have high peak amplitudes and the majority of false emissions are characterized as having a low average amplitude in one experiment average amplitudes below 50dB where considered to be non fracture events Yu et al 2011 Amplitude of a hit can be related to the intensity of the source i e intensity of the fracture so high amplitude coupled with the other fracture characteristics can be a good indication of fracture
139. roved to be a powerful tool for analyzing data that had already been collected However the graphical interface of the software is designed with a bias toward analyzing data from a test rather than monitoring a structure over a long period of time The following suggestions are provided to facilitate the use of the graphical interface in the AEWin software First normalization of the duration of time steps is recommended in order to have results comparable from one data file to another Second for continuous monitoring the software must be set to create a new data file with a new timer for each day 94 REFERENCES MISTRAS Products and Systems Division 2010 8151 8 Sensor Integral Preamplifier Acoustic Emission Sensor Princeton Junction NJ MISTRAS Barsoum F F Suleman J Karcak A amp E V 2009 Acoustic Emission Monitoirng and Fatigue Life Prediction in Axially Loaded Notched Steel Specimens Acoustic Emission Group 40 63 Beattie A G 2013 Acoustic Emission Non Destructive Testing of Structures using Source Location Techniques Albuquerque NM and Livermore CA Sandia National Laboratories Bohse J 2013 Acoustic Emission In H Czichos Handbook of Technical Diagnostics pp 137 160 Berlin Heidelberg Springer Colombo I S Main I G amp Forde M C 2003 Assessing Damage of Reinforced Concrete Beam Using b value Analysis of Acoustic Emission Signals Journal of Materials in Civi
140. s conservative would have amplitude at the source of 62 7dB Using the distance of each sensor in the fracture test from the fracture source amplitude of 62 7dB was converted to amplitude at the sensor In Tables B 1 and B 2 the minimum allowable amplitudes at each sensor are calculated so all hits with source amplitude of lower than 62 7dB are discarded This filter was assigned to sensors during data analysis of the fracture tests Table B 1 Lower limit of amplitude of a hit allowed at sensor considering bridge attenuation in dB in 4 12 4 in dB 3 5 4 Hl IE 8 4 4 2 3 8 a rs a 2 62 4 24 59 4 o 5 24 59 1 2 3 5 H 29 60 5 10 6 7 7 L s 466 567 s 563 Table B 2 Lower limit of amplitude of a hit allowed at sensor considering bridge attenuation res BTS2 BTN2 Min Amp dB B 1 APPENDIX VELOCITY CALIBRATION RESULTS This appendix contains the results of the velocity calibration pencil break tests Differences of arrival times at consecutive sensors were used to calculate the average velocity of the wave in the region between sensors Wave velocity is calculated using Equation 2 which is described in Chapter 5 In the following tables Equation 2 is solved by dividing AD in by the At us for each row Table C 1 Group 1 pencil break velocity results Group l ESOS
141. s allowing it to provide advanced warning for structural damage in steel bridges The Cedar Avenue Bridge was selected to be monitored because it is a major fracture critical steel bridge and an important artery in the transportation network The first phase included finite element analysis of the Cedar Avenue Bridge and modeling of local regions of high stress The results of the finite element analysis were used to make the decision to monitor a large region of the bridge rather than to focus on localized regions susceptible to fatigue as has been done previously with AE Hopwood II amp Prine 1987 The decision to monitor a large region of the bridge was made because there are numerous points along the bridge that may be susceptible to fracture During the second phase of the project the installation of the first south system took place The sensors where installed at 10 ft spacing and the sensor array was centered about the midspan of the bridge This sensor distribution was chosen because it allowed for many highly stressed connections to be monitored The second phase also included the calibration of the AE system to the Cedar Avenue Bridge which included pencil break tests to determine the attenuation and wave velocity between the sensor locations The sensor array remained in this location while being set to continuously collect data until May 2013 when a second identical AE system was installed in the north portion of the bridge and the or
142. s of optimal sunlight the four solar panels charged four 12V batteries that were then used to power the system During periods of scarce sunlight the voltage in the batteries dropped as power was drawn into the SH II without being replenished by the solar panels As time progressed the batteries lost more voltage as they aged and sat uncharged Due to this situation the batteries had to be replaced in the north system The solar panels were vulnerable to roadway debris including snow ice sand and de icing salts when they were oriented in the optimal direction for sunlight capture To protect the panels from becoming damaged a thin gauge wire meshing was used to cover the face of the solar panels and deflect flying debris The protective meshing was observed to decrease the current output of the panels by about 25 Another problem that came inherently with the solar panel use was the disconnection of power leads Workers moving past the power cables in the small confines of the box girder entrance caused one of the splices to break within its casing This break in the power line further contributed to the draining of the batteries until it was located and fixed Problems such as this are dependent on bridge conditions however the harsh environment of field testing is likely to uncover such problems 43 Even without bridge specific or project specific problems that occurred at the Cedar Avenue Bridge the panels would still not be able to
143. s vs 2 Counts vs Time sec 4 GFit Counts vs Time sec 5 Counts vs Time sec 8 GFit 1000 2000 2000 900 1800 1800 800 1600 1600 3500 700 1400 1400 3000 600 1200 1200 2500 500 1000 1000 2000 400 800 800 1500 300 600 600 1000 200 400 400 500 100 200 200 ar 055 I I 0 I 0 T I 400 600 1000 1200 400 600 800 1000 1200 400 800 1000 1200 400 600 800 1000 1200 Figure A 29 Count rate counts s during 86 second m including fracture individual sensors A 16 Time sec vs X Position lt All Channels Loc 1 GFit 1 2 5 440 5 4354 4205 4154 4104 4054 3804 3854 380 1 1 l 1 D 1 D 1 i 15 44 13 12 41 40 9 8 7 6 5 4 3 2 1 0 1 2 3 4 5 5 Figure A 30 Time 5 versus x position in on notched beam x 0 at crack events with source amplitude greater than 80dB only vs X Position lt All Channels Loc 1 GFit 2 Figure A 31 Time 5 versus x position in on notched beam 0 at crack tip A 17 A 4 Bridge Test North System Number 1 BTN1 Hits vs Time sec 8 11 Figure A 32 Cumulative hits versus time s sensors 8 9 10 11 Hits vs 8 Hits vs Time sec 10 Hits vs Time sec lt 15 gt TS BELL nm 8 EL oo 1 e 1 81 80 35 74 704 30 64 604 25 54 50 1 20 4 40 a p 1 1
144. sing power stored in each system s four solar powered 12V batteries By the time BTN2 and BTS2 were to be conducted neither the north or the south system could reliably remain operating from the solar powered batteries This is because the batteries were on average receiving inadequate current from the solar panels to keep the batteries at a high enough voltage to power the SH II The north system SH II is equipped with a 120V AC input cord however the south system is only equipped with the DC input that both systems use to receive power from the batteries The solution for providing a consistent power supply was to connect a 12V battery charger in parallel with the batteries The battery charger was set to supply 2A at 12V to keep the batteries voltage high enough to power the system The batteries are located approximately 120 from the entrance of the box girder so a pair of extension cords were needed to traverse the distance The battery charger was powered with a 2000 watt invertor generator placed outside and away from the box girder entrance to keep emissions out of the bridge 6 3 5 Data Collection The AE data for all of the bridge tests was collected with the SH II units operating under normal monitoring settings These settings are discussed in Chapter 5 on the monitoring methodology for the Cedar Avenue Bridge Before loading the notched beam the current acquisition mode of the SH II was stopped and a new test file was created This tes
145. ss intensity to count rate Fracture beam tests LT1 LT2 LT3 BTN2 and BTS2 verified literature findings with high count rates as well see Section 6 5 During analysis the count rate plot for each of the 16 sensors was evaluated but Figures 9 13 and 9 14 show the total count rate of all sensors for brevity The criterion in the third set associated with these plots is that two of three consecutive sensors must register an average count rate of 220 counts per second for a duration of 86 seconds For count rate plots of individual sensors refer to Appendix A In Figure 9 13 the vertical axis shows the cumulative count rate counts per second for all sensors in the system The horizontal axis shows the time seconds from the beginning of the record Note that the magnitude of the maximum count rate increases from low to high to anomalous activity 74 e ka om m ee tys 4 t Figure 9 13 Count rate versus time for third data set in north system Low activity day b High activity day c Anomalous day 75 4 Mild RR m u n Ni VY NT en mm Ll m ELI rer b Figure 9 14 Count rate versus time for third data set in south system a Low activity day b High activity day The fourth re
146. sult in the scattering of wave energy in multiple directions The more complex the structure the more waves will attenuate due to reflection Hellier 2012 The last form of attenuation is absorption which is the transfer of elastic wave energy into heat as friction between molecules absorbs wave energy Absorption results in a constant decibel decrease of signal amplitude as the wave front moves farther from the source The types of attenuation most prevalent in monitoring the Cedar Avenue Bridge are reflection and absorption Geometric spreading is not as important because bridge members are relatively small in two dimensions causing the wave front area to remain relatively constant However the long distances between sensors results in measurable attenuation due to absorption which is strictly a function of distance traveled The relatively complex geometry of the bridge results in significant reflection attenuation as well Tie girder diaphragms and connection members along the bridge are all discontinuities at which a wave can be reflected and have its energy scattered 4 3 Acoustic Emission Monitoring Acoustic emission AE monitoring is the process of collecting waveforms in a structure with the goal of detecting the onset of structural distress Waveforms passing through an AE sensor excite the piezoelectric crystal within the sensor The voltage wave produced from the crystal is then sent to the central computer for data processing Like an
147. t rate observed during fracture tests are generally higher than values from AE data collected during monitoring of the bridge Criteria developed in Chapter 8 require two of three consecutive sensors to exceed a given threshold value The threshold values were chosen so that the AE data from the notched beam fracture tests meet all of the criteria associated with fracture see section 8 4 35 Values in unshaded white cells were not used to determine fracture criteria because their positions did not realistically simulate a bridge monitoring environment These sensors where placed either on the notched beam and used for crack validation or where placed close to the notched beam and used for attenuation measurements but not for determining or validating fracture criteria The acoustic connection between notched beam and structure was not maintained during three of the seven notched beam tests These tests are denoted with some cells shaded gray in the following tables During the first laboratory fracture test the beam was partially damaged during preliminary testing The damage prevented complete contact between the notched beam and the girder thus hindering wave propagation In and BTS1 cold weather conditions prevented complete application of epoxy this resulted in a discontinuity between notched beam and bridge girder Table 6 3 Laboratory fracture test results Hit Rate hits s Energy Rate pJ s Count Rate counts s
148. t file would hold all AE data collected during the experiment A stopwatch timer was started at the same time as the data acquisition to compare the time of audible fracture with data collected during the test Once the SH II was in acquisition mode the pressure in the jack was gradually increased using a manual pump Figure 16 The pump operator was positioned safely on the opposite side of the diaphragm to the notched beam during the fracture test Loading of the beam was increased until the area of the test beam between the notch and the hole was completely fractured The SH II acquisition file was then saved and transferred to a portable hard drive for later analysis 33 Figure 6 5 Hand pump connected to the jack just out of view to the top of picture Removal of the notched beams after the test revealed how well the connection between the beam and the bridge was maintained during the test After both BTNI and BTS1 the connection was very poor and could not support the weight of the beam after removal of the clamp and jack The poor connection was most likely a result of the epoxy being applied cold and unworkable especially considering the lack of surface preparation The connection discontinuity from the beam to the bridge during the test is believed to be the primary reason for lack of AE data picked up at sensors not on the notched beam itself The connection after tests BTN2 and BTS2 was nearly intact but the beam was easily removed b
149. ta from real AE fracture events To be able to properly discriminate between non AE and AE from fracture the behavior of the waveforms and the capabilities of the AE technology must be understood 4 2 Acoustic Emission Wave Propagation 4 2 1 Wave Propagation Modes Energy released from an acoustic emission initially travels away from the deformation as bulk waves Bulk waves are the propagation of energy through a three dimensional space The two types of bulk waves are compression and shear waves The particles in compression waves 9 move in the same direction as the traveling waveform The particles in shear waves move perpendicular to the direction of travel of the waveform Beattie 2013 Bulk waves travel through a homogenous material until reaching a boundary or a surface At the surface the wave is reflected but some of the wave energy contributes to the formation of a surface wave Waves on the surface of the air structure interface travel as either plate Lamb waves or surface Rayleigh waves The mode the surface wave takes is a function of the wavelength and the plate thickness Scruby 1987 If the thickness of the plate is on the order of a few wavelengths both sides of the plate will contribute to wave motion creating a Lamb wave as seen in Figure 4 1 If the plate thickness is large compared to the wavelength then the surface wave will propagate as a Rayleigh wave as seen in Figure 4 2 Beattie 2013 The wave motion perpendic
150. the event and no location is produced Considering this characteristic of the AEwin source location algorithm it is better to underestimate the velocity and end up with a source calculated close to the midpoint than to overestimate the velocity and lose the event data Locating multiple sources with locations erring towards the center can provide much more useful data than events that are not registered because of source location error In the Cedar Avenue Bridge no two adjacent monitoring regions have the same velocity because of the bridge geometry i e diaphragms splices or nothing between sensors as seen in Figure 5 7 Therefore each sensor save the end sensors would be required to be assigned to two groups one including the sensor to the left and one to the right resulting in a total of 15 groups for the best accuracy However AEwin software only allows for a maximum of eight sensor groups Therefore for data analysis all sensors are assigned to a single group The result of this simplification is that only one velocity is assigned to all of the sensors To avoid events being discarded in areas where the velocity is overestimated the average of the velocities in groups one and five slowest average velocities because of the diaphragms is assigned to the group consisting of all the sensors Assigning all the sensors to a single group also has the benefit of viewing AE activity throughout the array with a single plot which is helpfu
151. tion of that hit must also exceed 50ms In Figure 9 3 duration values are shown in microseconds along the vertical axis and amplitude in decibels is shown along the horizontal axis 55 LTTLIITTITITI 75 Figure 9 3 Duration versus amplitude for the first data set Low activity day b High activity day c Anomalous day 56 The final relationship analyzed for the first fracture criterion set is the maximum absolute energy of a hit versus amplitude Figure 9 4 This plot shows the maximum energy of all the hits at discretized amplitudes Absolute energy of the voltage wave is defined in Equation 1 The energy parameter accounts for both the magnitude of the voltage wave as well as its duration Both the high activity and anomalous days have high maximum energy hits above 90dB while the low activity day has relatively low amounts of energy as seen in Figure 9 4 The criterion associated with this plot is that the maximum absolute energy of a hit above 90dB must be above 10pJ 10 aJ In Figure 9 4 the vertical axis shows the absolute energy value in attojoules 101 joules of the hit with the maximum absolute energy at the corresponding amplitude The horizontal axis shows the amplitude in decibels of the hit 57 Ampltadel48 Channels Figure 9 4 Maximum absolute energy versus amplitude for the first data set Low activity day b High activity day c Anomalous day
152. tric crystal after being amplified by a predetermined value If the voltage of the amplified waveform exceeds the AE threshold then a hit is documented The wave in Figure 4 3 would be counted as a single hit The software would store the magnitudes of the parameters shown in Figure 4 3 with the associated hit AE amplitude is the maximum amplitude of the voltage signal after amplification in decibels with reference to ImV AE duration is the time period of the first threshold crossing to the last within the hit Time of hit is the time when the AE threshold is first exceeded Rise time is the time from the time of hit to the time the maximum amplitude occurs AE counts are the number of times the threshold is exceeded during the hit Parameters that are not shown in Figure 4 3 that have been used in analysis are frequency centroid which is the centroid of the frequency spectrum of the hit peak frequency which is the frequency with the largest amplitude of the frequency spectrum counts to peak which is the number of counts before the maximum amplitude occurs and energy which is proportional to the area under the squared voltage signal The software is able to store numerous hits all with the listed parameters to describe each hit Once the parameters of each hit are calculated the original waveform is often discarded because of the large amount of storage space needed for storing each waveform Also as thousands of 13 hits are recorded it becomes co
153. troid for the second data set in the north system a Low activity day b High activity day c Anomalous day 64 Freq Ad Channels D 0 10 110 9 130 140 150 160 170 180 190 200 210 220 vs Freq Af Channeli 9 1 no 1720 130 140 150 160 170 19 190 210 220 b Figure 9 8 Number of hits versus frequency centroid for the second data set in the south system a Low activity day b High activity day The third relationship analyzed is the plot of duration versus amplitude for each hit Figures 9 9 and 9 10 This plot represents the criterion of amplitude greater than 90 dB for any hit and amplitude greater than 90 dB for any hit with duration greater than 50ms As seen in figures 9 9 and 9 10 none of the amplitudes above 90 dB extend above the threshold of 50ms This stricter criterion relative to what was used in the first set eliminates even the anomalous data files from consideration as fracture events and relegates them to the category of non fracture events In Figure 9 9 duration values are shown in microseconds along the vertical axis and amplitude in decibels is shown along the horizontal axis Note that the more active data records 9 9b 9 9c 9 10b and 9 10c contain hits with larger amplitudes than low activity data record but the duration of the hits is comparable 65 IHH K ani ji c Figure 9 9
154. troid value The horizontal axis shows the frequency centroid in kilohertz which is the centroid of the power spectrum of the waveform 53 455424528 B 100 19 120 1330 140 150 160 170 10 10 20 210 50 50 100 110 320 130 140 150 3960 17 19 149 20 20 220 9 0 100 110 120 130 340 150 160 170 180 190 20 210 220 Figure 9 2 Number of hits versus frequency centroid for the first data set a Low activity day b High activity day c Anomalous day 54 The third relationship analyzed is the plot of duration versus amplitude for each hit Figure 9 3 Hits with long durations and high amplitudes have been observed in all of the applicable fracture beam tests Both the high activity and the anomalous data have amplitudes above 90dB which is the threshold for the criterion High amplitude does not necessarily imply fracture however all fracture is expected to produce high amplitude hits The data for low and high activity days do not have long durations associated with the high amplitude which helps to rule them out as not representing fracture Long durations with high amplitudes are thought to be associated with fracture because of the continuous emission from a propagating fracture The data for the anomalous day has long duration and high amplitude so it meets the 4 criterion for the first fracture criterion set The two criteria that are associated with this plot are that 1 the amplitude must exceed 90dB and 2 the dura
155. ture occurring in a bridge member would be expected to have a larger fracture area than either of the tests 6 00 a b Figure 6 1 Notched beam specimen profile for BTN1 BTS1 b BTN2 BTS2 All dimensions in inches 6 3 2 Connection The beams were tested with the cut flange and notch on the top face They were fixed to a plate that serves to anchor one of the support cables inside of the box girder of the Cedar Avenue Bridge as shown in Figure 6 2 The beams were adhered with Loctite E 20NS Hysol epoxy adhesive to the plate and then clamped down with a large heavy duty steel clamp An epoxy adhesive was used to prevent any damage to the tie girder in the form of hole drilling or steel welding The support cable anchor plate was chosen for the test location because there is no other horizontal surface inside the girder on which to clamp the beam 30 For both sets of tests beams were installed at least one week prior to running the tests This allowed enough time for the epoxy to cure in the cold weather During the application of epoxy for the first set of tests BTN1 and BTS1 care was not taken to keep the epoxy warm and workable Because of this condition less epoxy than desired was used to attach the beams For the second set of tests the epoxy was kept warm which allowed for even distribution of epoxy over the connection surface Also for the second set rust particles and paint were sanded away from the connection area t
156. uch Nonetheless differences in settings should be noted for later data analysis Table 5 4 SH II acquisition settings PDT HDT HLT E d Filter Filter M Ins Ins ms System ERI e D ome m m om 27 CHAPTER 6 ACOUSTIC EMISSION ACQUISITION IN FRACTURE BEAM TEST 6 1 Overview As noted in the literature review there have been many experiments preformed with AE sensing technology These findings have helped provide insight to the kind of emissions to expect during fracture events e g high event or count rate Sinclair Connors amp Formby 1977 Although the general trends of AE during fracture events have been identified and discussed there has not been extensive research to develop quantifiable measures associated with AE from fracture events The experiments described in this section are designed to capture AE from a steel fracture event and provide thresholds for AE parameters to be used in the monitoring of bridges Detection of cracking in a structure depends on the ability of the detection method to differentiate between safe levels of AE from elastic stress and other miscellaneous excitation and dangerous levels of AE that are associated with fracture To determine the levels of AE associated with fracture beams with a notch and hole to create a stress concentration where loaded monotonically to fracture and the 5 system was used to record the AE produced during the fracture The previous phase of this
157. ular to the surface the structure is the primary source of AE signals because of the orientation of the piezoelectric material in the sensor As seen in Figures 4 1 and 4 2 Lamb and Rayleigh wave have a major component of particle motion perpendicular to the surface and therefore will be the major source of AE waveforms collected by the monitoring system Figure 4 2 Rayleigh waves 10 Although surface waves detected by the AE sensor may be caused by crack formation the waveform by the time of detection can be drastically different than the original waveform at the source This occurs because the original waveform will undergo many reflections and create new waves at each reflection The multiple waves propagating throughout the structure will interfere with each other further distorting the detected wave signal Hellier 2012 4 2 2 Wave Attenuation Attenuation is the phenomenon of wave amplitude decreasing as the wave travels farther away from its source There are three causes of attenuation in a real structure geometric spreading reflection and absorption Hellier 2012 Geometric spreading is the dominant attenuation mechanism in an infinite medium Geometric spreading is the result of the increase in wave area while maintaining a constant energy as the wave front moves farther away from the source Reflection redirects the energy of a wave at the structure boundaries Any discontinuity or surface that a wave encounters will re
158. um number of criteria is used j J For example if one criterion is taken from each set the third criterion set will still be nearly perfect that is J 1 9996 and 100 respectively for the North and South Systems The second fracture criterion set will be a little slightly less effective with i J 1 95 and 97 respectively for the North and South Systems The first fracture criterion set is the worst performer with J 1 9396 The superior performance of the third fracture criterion set is evaluated in Tables 10 1 10 8 and illustrated in Figures 10 1 and 10 2 The enhanced performance of this fracture criterion set is driven by increased utilization of the parameters that are available for calculation using Mistras AEwin software as well as the more in depth analysis of the collected AE data by checking individual sensors instead of sensor groups 90 CHAPTER 1 SUMMARY CONCLUSIONS AND RECOMMENDATIONS 11 1 Summary The goal of the project discussed in this report was to determine if acoustic emission AE technology can be used for sparse monitoring of fracture critical steel bridges This project followed two earlier phases that included system design in Phase I Schultz amp Thompson 2010 and implementation of one 16 sensor system and preliminary data collection and processing in Phase Schultz et al 2014 For this third phase of the overall program a second AE system which was nominally identi
159. uracy Examples of non fracture AE waves include 1 high numbers of transient waveforms traveling though the medium 2 large amounts of excitation in piezoelectric vibrating crystals and 3 waveforms propagating from a localized region After conducting this project the following conclusions can be made 1 Despite inherent challenges sparse AE sensor systems i e with sensors placed at maximum spacing can detect the occurrence of fracture even in a noisy environment such as a bridge given proper fracture criteria and the protocols to enforce them 2 sensor spacing of 10ft along the tie girder determined from pencil break tests proved to be adequate as verified by notched beam fracture tests conducted in the Cedar Avenue Bridge 3 The fracture tests performed in the bridge produced AE data that does match the AE data produced by the bridge under the range of conditions experienced during the monitoring periods This feature was used to advantage by defining characteristics of the AE data from the fracture beam tests that was not present in the bridge data when fracture beams were not being tested at the bridge 4 The final pair of bridge notched beam fracture tests provided strong evidence that a small amount of fracture can be detected by sensors spaced at 10ft along the bridge girder 5 Continuous monitoring has a low probability of being achieved when the sole power source is an array of solar panels Using the sensors to m
160. venly spaced at 10ft in a line parallel with the road At 10ft spacing two monitoring systems are capable of covering the full span of the tie girder This method of monitoring is known as linear monitoring and is best suited for structures where one dimension is much longer than the others e g a bridge girder Pencil beak tests performed during Phase II Schultz et al 2014 were performed to validate the adequacy of spacing the sensors at 10ft The selected spacing insures a waveform never passes through multiple diaphragms or attenuates beyond detection before reaching a sensor The 10ft spacing is also sufficient to cover a large expanse of bridge with a limited number of sensors 17 29 10 60 29 1050 29 1050 29 10 0 29 1050 29 10 50 29 1050 29 10 50 29 10 50 L1 L2 L3 L4 5 L6 15 L4 Ly Le 10 368 6 00 _ 29 10 50 29 10 50 29 10 50 Figure 5 1 Connection spacing and naming Signals collected by the vibrating piezoelectric sensors are processed in the SH II central computer where transient waveforms are documented by calculating and storing parameters that characterize the waveform These waveform parameters are stored in a data file on the computer s hard drive After the file has been created the system will send the data file to an online database maintained by the equipment manufacturer Online data files can then be downloaded
161. vity day b High activity day ou pat sau o ae e Ra ERE A 63 Figure 9 7 Number of hits versus frequency centroid for the second data set in the north system Low activity day b High activity day c Anomalous day 64 Figure 9 8 Number of hits versus frequency centroid for the second data set in the south system a Low activity day b High activity day deceret nn nasa 65 Figure 9 9 Duration versus amplitude for the second data set in the north system a Low activity day b High activity day c Anomalous day sese 66 Figure 9 10 Duration versus amplitude for the second data set in the south system a Low activity day b High activity day rta NIS E 67 Figure 9 11 Absolute energy rate versus time for the second data set in the north system a Low activity day b High activity day c Anomalous 69 Figure 9 12 Absolute energy rate versus time for the second data set in the south system Low activity day b High activity day o ose S M deat NOE 70 Figure 9 13 Count rate versus time for third data set in north system a Low activity day b High activity day c Anomalous day tei bean d 75 Figure 9 14 Count rate versus time for third
162. y form of non destructive testing AE monitoring has its advantages and disadvantages The primary advantage of AE monitoring is that it can detect the formation of a crack at its onset It is able to do this by constantly detecting and storing waveforms from the structure The resonating sensors that are primarily used in AE monitoring are very sensitive and have the ability to pick up waves from slight defects in the structure An unusually high onset of transient waves is often considered a sign of 11 structural distress or fracture AE monitoring can detect the high wave activity and store the characteristics of the waveforms for analysis to help users determine if fracture may be present The disadvantage of AE comes innately with its advantages The system s ability to detect waves from fracture initiation means that it can also detect waves produced by numerous other sources The sensors are sensitive enough to detect just about any sound occurring in the bridge such as friction between connections vehicles driving over expansion joints and even rainfall striking the girder In practical monitoring projects all of the AE noise creates a large amount of data and care needs to be taken in order to detect sounds of fracture in the midst of the constant AE noise Traditionally AE technology has been used to monitor components with simple geometries or small regions of a larger structure AE monitoring is popular in monitoring of pressure vessels
163. y hand after taking off the clamp Although the interface for the second set of tests was partially broken sensors on the bridge still show high amounts of AE activity which suggests much better transmission of stress waves across the epoxy joint during the tests 6 4 Laboratory Notched Beam Fracture Test The laboratory beam fracture tests conducted during the previous phase of the project are described in detail elsewhere Schultz et al 2014 The purpose of these tests was to produce AE waves from fracture in the absence of AE noise Three tests were conducted each with slightly different arrangements of eight sensors The test involved fracturing a small steel beam that was acoustically coupled to a large steel girder Data collected during these tests form the basis for the first two sets of criteria used for bridge data evaluation Plots depicting the data collected during the notched beam tests are shown in Appendix B 34 6 5 Fracture Acoustic Emission Results and Discussion This section provides a summary of tabulated results from both the laboratory notched beam fracture tests and the in bridge notched beam fracture tests The rate of occurrences of selected parameters is shown for the individual sensors used for the tests Results from all eight sensors used in the laboratory tests are shown and results from the six sensors closest to the fracture in the bridge experiments are shown Table cells are colored to denote if they are elig
164. zed case is highly unrealistic for the Cedar Avenue Bridge thus the two effectiveness metrics must be determined by independent means fracture beam tests for and data collected in the bridge for 7 10 2 Effectiveness of Fracture Criterion Sets in Identifying Fracture Each of the fully successful fracture beam tests LT1 LT2 LT3 BTN2 and BTS2 is used to define the effectiveness to identify fracture for the three fracture criterion sets The evaluation is achieved using Equation 4 and is summarized in Tables 10 1 10 3 In the tables the effectiveness to identify fracture is calculated for a minimum number of criteria being used The effectiveness value is calculated as the percentage of the tests where at least the minimum number of criteria is exceeded For example the use of only one or two criteria can indicate fracture in all fracture tests Using a small number of criteria one or two will result in a large number of false positives when applied to bridge data as shown in section 10 3 which is why numerous criteria are required 5 for set 1 and or 6 each for sets 2 and 3 As more criteria are used the effectiveness 7 of the first and second criteria sets decrease This means that some of the criteria in these sets are not triggered even though fracture did occur in the test For example the first criterion set using all five criteria can only successfully indicate fracture in 3 of the 5 of the tests 60 This

Protocols and Criteria for Acoustic Emission Monitoring of Fracture

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