Damage Localization in a Steel Truss Bridge Using Influence Lines Identified from Vehicle-Induced Acceleration
Publication: Journal of Bridge Engineering
Volume 28, Issue 4
Abstract
In the last few decades, structural health monitoring (SHM) has proven a helpful tool to support the maintenance and management of civil infrastructure. However, typical measurement networks are expensive and require considerable initial efforts. The user-friendliness and interpretability of the outcome of SHM systems are crucial factors in motivating infrastructure owners and decision-makers to sustain their costs. For this reason, simple algorithms that provide structural parameters with direct physical interpretability for professionals familiar with the typical quantities involved in structural engineering are still the most used in field applications. This paper proposes an original method to identify curvature influence lines of bridges and viaducts only using the structural acceleration response induced by vehicular loads. Acceleration time histories collected at sparse locations through standard accelerometers are employed. In contrast to SHM approaches based on modal parameters, the proposed method does not need strict synchronization, thus being suitable for wireless and low-cost monitoring solutions. Identified influence lines are used to define a spatially dense damage indicator for accurate localization of structural anomalies with a clear physical meaning. Experimental results obtained for a steel truss bridge analyzed in different damage conditions prove the efficacy of the proposed method for situations where modal-based approaches may fail.
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Acknowledgments
The authors gratefully acknowledge the availability of data recorded on the Old ADA Bridge, freely available in Kim et al. (2021b).
References
Alamdari, M. M., K. Kildashti, B. Samali, and H. V. Goudarzi. 2019. “Damage diagnosis in bridge structures using rotation influence line: Validation on a cable-stayed bridge.” Eng. Struct. 185: 1–14. https://doi.org/10.1016/j.engstruct.2019.01.124.
Aloisio, A., R. Alaggio, and M. Fragiacomo. 2020a. “Time-domain identification of the elastic modulus of simply supported box girders under moving loads: Method and full-scale validation.” Eng. Struct. 215: 110619. https://doi.org/10.1016/j.engstruct.2020.110619.
Aloisio, A., R. Alaggio, and M. Fragiacomo. 2020b. “Dynamic identification and model updating of full-scale concrete box girders based on the experimental torsional response.” Constr. Build. Mater. 264: 120146. https://doi.org/10.1016/j.conbuildmat.2020.120146.
Aloisio, A., L. Di Battista, R. Alaggio, and M. Fragiacomo. 2020c. “Sensitivity analysis of subspace-based damage indicators under changes in ambient excitation covariance, severity and location of damage.” Eng. Struct. 208: 110235. https://doi.org/10.1016/j.engstruct.2020.110235.
Aloisio, A., M. M. Rosso, and R. Alaggio. 2022. “Experimental and analytical investigation into the effect of ballasted track on the dynamic response of railway bridges under moving loads.” J. Bridge Eng. 27 (10). https://doi.org/10.1061/(ASCE)BE.1943-5592.0001934.
Bhowmik, B., T. Tripura, B. Hazra, and V. Pakrashi. 2020. “Real time structural modal identification using recursive canonical correlation analysis and application towards online structural damage detection.” J. Sound Vib. 468: 115101. https://doi.org/10.1016/j.jsv.2019.115101.
Breccolotti, M., and M. Natalicchi. 2022. “Bridge damage detection through combined quasi-static influence lines and weigh-in-motion devices.” Int. J. Civ. Eng. 20 (5): 487–500. https://doi.org/10.1007/s40999-021-00682-0.
Brincker, R., and C. E. Ventura. 2015. Introduction to operational modal analysis. Chichester, UK: Wiley.
Cavadas, F., I. F. C. Smith, and J. Figueiras. 2013. “Damage detection using data-driven methods applied to moving-load responses.” Mech. Syst. Sig. Process. 39 (1–2): 409–425. https://doi.org/10.1016/j.ymssp.2013.02.019.
Chang, K.-C., and C.-W. Kim. 2016. “Modal-parameter identification and vibration-based damage detection of a damaged steel truss bridge.” Eng. Struct. 122: 156–173. https://doi.org/10.1016/j.engstruct.2016.04.057.
Chen, S.-Z., G. Wu, D.-C. Feng, and L. Zhang. 2018a. “Development of a bridge weigh-in-motion system based on long-gauge fiber Bragg grating sensors.” J. Bridge Eng. 23 (9). https://doi.org/10.1061/(ASCE)BE.1943-5592.0001283.
Chen, Z.-W., Q.-L. Cai, and S. Zhu. 2018b. “Damage quantification of beam structures using deflection influence lines.” Struct. Control Health Monit. 25 (11): e2242. https://doi.org/10.1002/stc.2242.
Chen, Z.-W., S. Zhu, Y.-L. Xu, Q. Li, and Q.-L. Cai. 2015. “Damage detection in long suspension bridges using stress influence lines.” J. Bridge Eng. 20 (3): 05014013. https://doi.org/10.1061/(ASCE)BE.1943-5592.0000681.
Dessi, D., and G. Camerlengo. 2015. “Damage identification techniques via modal curvature analysis: Overview and comparison.” Mech. Syst. Sig. Process. 52–53 (1): 181–205. https://doi.org/10.1016/j.ymssp.2014.05.031.
Djurić, Z. 2000. “Mechanisms of noise sources in microelectromechanical systems.” Microelectron. Reliab. 40 (6): 919–932. https://doi.org/10.1016/S0026-2714(00)00004-4.
Fan, W., and P. Qiao. 2011. “Vibration-based damage identification methods: A review and comparative study.” Struct. Health Monit. 10 (1): 83–111. https://doi.org/10.1177/1475921710365419.
Frøseth, G. T., A. Rønnquist, D. Cantero, and O. Øiseth. 2017. “Influence line extraction by deconvolution in the frequency domain.” Comput. Struct. 189: 21–30. https://doi.org/10.1016/j.compstruc.2017.04.014.
Frýba, L. 1999. Vibration of solids and structures under moving loads. Berlin: Springer Science & Business Media.
Giordano, P. F., and M. P. Limongelli. 2020. “Response-based time-invariant methods for damage localization on a concrete bridge.” Struct. Concr. 21 (4): 1254–1271. https://doi.org/10.1002/suco.202000013.
He, W., T. Ling, E. J. O’Brien, and L. Deng. 2019. “Virtual axle method for bridge weigh-in-motion systems requiring no axle detector.” J. Bridge Eng. 24 (9). https://doi.org/10.1061/(ASCE)BE.1943-5592.0001474.
He, W.-Y., W.-X. Ren, and S. Zhu. 2017. “Damage detection of beam structures using quasi-static moving load induced displacement response.” Eng. Struct. 145: 70–82. https://doi.org/10.1016/j.engstruct.2017.05.009.
Heitner, B., F. Schoefs, E. J. O’Brien, A. Žnidarič, and T. Yalamas. 2020. “Using the unit influence line of a bridge to track changes in its condition.” J. Civ. Struct. Health Monit. 10 (4): 667–678. https://doi.org/10.1007/s13349-020-00410-7.
Huseynov, F., D. Hester, E. J. O’Brien, C. McGeown, C.-W. Kim, K. Chang, and V. Pakrashi. 2022. “Monitoring the condition of narrow bridges using data from rotation-based and strain-based bridge weigh-in-motion systems.” J. Bridge Eng. 27 (7). https://doi.org/10.1061/(ASCE)BE.1943-5592.0001872.
Huseynov, F., C. Kim, E. J. O’Brien, J. M. W. Brownjohn, D. Hester, and K. C. Chang. 2020. “Bridge damage detection using rotation measurements—Experimental validation.” Mech. Syst. Sig. Process. 135: 106380. https://doi.org/10.1016/j.ymssp.2019.106380.
Khan, M. A., D. P. McCrum, L. J. Prendergast, E. J. O’Brien, P. C. Fitzgerald, and C.-W. Kim. 2021. “Laboratory investigation of a bridge scour monitoring method using decentralized modal analysis.” Struct. Health Monit. 20 (6): 3327–3341. https://doi.org/10.1177/1475921720985122.
Kim, C.-W., K. Chang, S. Kitauchi, P. McGetrick, K. Hashimoto, and K. Sugiura. 2014. “Changes in modal parameters of a steel truss bridge due to artificial damage.” In Safety, reliability, risk and life-cycle performance of structures and infrastructures, edited by G. Deodatis, B. R. Ellingwood, and D. M. Frangopol, 3725–3732. Boca Raton, FL: CRC Press.
Kim, C.-W., F.-L. Zhang, K.-C. Chang, P. J. McGetrick, and Y. Goi. 2021a. “Ambient and vehicle-induced vibration data of a steel truss bridge subject to artificial damage.” J. Bridge Eng. 26 (7): 04721002. https://doi.org/10.1061/(ASCE)BE.1943-5592.0001730.
Kim, C.-W., F. Zhang, K.-C. Chang, P. McGetrick, and Y. Goi. 2021b. “Old_ADA_Bridge-damage_vibration_data.” Mendeley Data, V2. Accessed February 3, 2023. https://doi.org/10.17632/sc8whx4pvm.2.
Lynch, J. P., Y. Wang, K. J. Loh, J.-H. Yi, and C.-B. Yun. 2006. “Performance monitoring of the Geumdang Bridge using a dense network of high-resolution wireless sensors.” Smart Mater. Struct. 15 (6): 1561–1575. https://doi.org/10.1088/0964-1726/15/6/008.
Martinez, D., A. Malekjafarian, and E. O’Brien. 2020. “Bridge health monitoring using deflection measurements under random traffic.” Struct. Control Health Monit. 27 (9). https://doi.org/10.1002/stc.2593.
Martini, A., E. M. Tronci, M. Q. Feng, and R. Y. Leung. 2022. “A computer vision-based method for bridge model updating using displacement influence lines.” Eng. Struct. 259: 114129. https://doi.org/10.1016/j.engstruct.2022.114129.
Nassif, H. H., M. Gindy, and J. Davis. 2005. “Comparison of laser Doppler vibrometer with contact sensors for monitoring bridge deflection and vibration.” NDT & E Int. 38 (3): 213–218. https://doi.org/10.1016/j.ndteint.2004.06.012.
O’Brien, E. J., J. M. W. Brownjohn, D. Hester, F. Huseynov, and M. Casero. 2021a. “Identifying damage on a bridge using rotation-based bridge weigh-in-motion.” J. Civ. Struct. Health Monit. 11 (1): 175–188. https://doi.org/10.1007/s13349-020-00445-w.
O’Brien, E. J., D. McCrum, and M. A. Khan. 2021b. “Bridge damage detection using acceleration influence line calibrated without access to a pre-weighed vehicle.” In Bridge maintenance, safety, management, life-cycle sustainability and innovations, edited by H. Yokota, and D. M. Frangopol, 1615–1620. Boca Raton, FL: CRC Press.
Quqa, S., A. Antolini, E. Franchi Scarselli, A. Gnudi, A. Lico, M. Carissimi, M. Pasotti, R. Canegallo, L. Landi, and P. P. Diotallevi. 2022a. “Phase change memories in smart sensing solutions for structural health monitoring.” J. Comput. Civil Eng. 36 (4): 04022013. https://doi.org/10.1061/(ASCE)CP.1943-5487.0001027.
Quqa, S., L. Landi, and P. P. Diotallevi. 2021. “Automatic identification of dense damage-sensitive features in civil infrastructure using sparse sensor networks.” Autom. Constr. 128: 103740. https://doi.org/10.1016/j.autcon.2021.103740.
Quqa, S., L. Landi, and P. P. Diotallevi. 2022b. “Instantaneous identification of densely instrumented structures using line topology sensor networks.” Struct. Control Health Monit. 29 (3). https://doi.org/10.1002/stc.2891.
Quqa, S., L. Landi, and P. P. Diotallevi. 2020. “Instantaneous modal identification under varying structural characteristics: A decentralized algorithm.” Mech. Syst. Sig. Process. 142: 106750. https://doi.org/10.1016/j.ymssp.2020.106750.
Sabato, A., C. Niezrecki, and G. Fortino. 2017. “Wireless MEMS-based accelerometer sensor boards for structural vibration monitoring: A review.” IEEE Sens. J. 17 (2): 226–235. https://doi.org/10.1109/JSEN.2016.2630008.
Sekiya, H., K. Kubota, and C. Miki. 2018. “Simplified portable bridge weigh-in-motion system using accelerometers.” J. Bridge Eng. 23 (1). https://doi.org/10.1061/(ASCE)BE.1943-5592.0001174.
Toksoy, T., and A. E. Aktan. 1994. “Bridge-condition assessment by modal flexibility.” Exp. Mech. 34 (3): 271–278. https://doi.org/10.1007/BF02319765.
Tronci, E. M., M. De Angelis, R. Betti, and V. Altomare. 2022. “Multi-stage semi-automated methodology for modal parameters estimation adopting parametric system identification algorithms.” Mech. Syst. Sig. Process. 165: 108317. https://doi.org/10.1016/j.ymssp.2021.108317.
Vetterli, M., and J. Kovačević. 1995. Wavelets and subband coding. Hoboken, NJ: Prentice Hall.
Wang, N.-B., L.-X. He, W.-X. Ren, and T.-L. Huang. 2017. “Extraction of influence line through a fitting method from bridge dynamic response induced by a passing vehicle.” Eng. Struct. 151: 648–664. https://doi.org/10.1016/j.engstruct.2017.06.067.
Wu, B., G. Wu, C. Yang, and Y. He. 2018. “Damage identification method for continuous girder bridges based on spatially-distributed long-gauge strain sensing under moving loads.” Mech. Syst. Sig. Process. 104: 415–435. https://doi.org/10.1016/j.ymssp.2017.10.040.
Wu, D., and S. S. Law. 2004. “Damage localization in plate structures from uniform load surface curvature.” J. Sound Vib. 276 (1–2): 227–244. https://doi.org/10.1016/j.jsv.2003.07.040.
Zaurin, R., and F. Necati Catbas. 2011. “Structural health monitoring using video stream, influence lines, and statistical analysis.” Struct. Health Monit. 10 (3): 309–332. https://doi.org/10.1177/1475921710373290.
Zhang, Z., and A. E. Aktan. 1998. “Application of modal flexibility and its derivatives in structural identification.” Res. Nondestr. Eval. 10 (1): 43–61. https://doi.org/10.1080/09349849809409622.
Zheng, X., D.-H. Yang, T.-H. Yi, and H.-N. Li. 2019. “Development of bridge influence line identification methods based on direct measurement data: A comprehensive review and comparison.” Eng. Struct. 198: 109539. https://doi.org/10.1016/j.engstruct.2019.109539.
Information & Authors
Information
Published In
Journal of Bridge Engineering
Volume 28 • Issue 4 • April 2023
Copyright
© 2023 American Society of Civil Engineers.
History
Received: Jul 27, 2022
Accepted: Dec 16, 2022
Published online: Feb 14, 2023
Published in print: Apr 1, 2023
Discussion open until: Jul 14, 2023
ASCE Technical Topics:
- Architectural engineering
- Bridge engineering
- Bridges
- Bridges (by material)
- Bridges (by type)
- Building management
- Engineering materials (by type)
- Highway transportation
- Infrastructure
- Maintenance and operation
- Materials engineering
- Metals (material)
- Steel
- Steel bridges
- Structural engineering
- Structural health monitoring
- Structural members
- Structural steel
- Structural systems
- Transportation engineering
- Truss bridges
- Trusses
- Vehicles
- Wood bridges
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