Bridge Damage Localization Using Axle Weight Time History Data Obtained through a Bridge Weigh-in-Motion System
Publication: Journal of Performance of Constructed Facilities
Volume 35, Issue 5
Abstract
With an increase in bridge service time, damage will inevitably occur. Accurately identifying damage is of great significance to the operation and maintenance of a bridge. To realize a frequent identification of local stiffness reduction caused by structural damage in operation, this paper proposes a new method for damage localization of short-span bridges based on the axle weight time history identified by bridge weigh-in-motion technology. This method is based on a least squares QR decomposition recursive algorithm, and the orthogonal matrix is updated by the newly added measured rotation response and the axle weight identified in the previous step to recursively obtain the current axle weight. Then, the axle weight time history is obtained and the damage is located according to the mutation of axle weight time history caused by the first axle passing through the damage. The accuracy and applicability of the proposed method are verified by a numerical model of a simply supported beam. The results show that the axle weight time history has a strong sensitivity to damage, and the average normalized axle weight time history can accurately locate the damage and significantly reduce the impact of environmental noise. This method can be used for long-term health monitoring of bridge structures.
Get full access to this article
View all available purchase options and get full access to this article.
Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
This research work was jointly supported by the National Natural Science Foundation of China (Grants Nos. 52050050, 51978128, and 52078102), and the LiaoNing Revitalization Talents Program (Grant No. XLYC1802035).
References
Alamdari, M. M., L. Ge, K. Kildashti, Y. Zhou, B. Harvey, and Z. Du. 2019a. “Non-contact structural health monitoring of a cable-stayed bridge: Case study.” Struct. Infrastruct. Eng. 15 (8): 1119–1136. https://doi.org/10.1080/15732479.2019.1609529.
Alamdari, M. M., K. Kildashti, B. Samali, and H. V. Goudarzi. 2019b. “Damage diagnosis in bridge structures using rotation influence line: Validation on a cable-stayed bridge.” Eng. Struct. 185 (Apr): 1–14. https://doi.org/10.1016/j.engstruct.2019.01.124.
Bobrow, J. E., and W. Murray. 1993. “An algorithm for RLS identification parameters that vary quickly with time.” IEEE Trans. Autom. Control 38 (2): 351–354. https://doi.org/10.1109/9.250491.
Cantero, D., and A. González. 2015. “Bridge damage detection using weigh-in-motion technology.” J. Bridge Eng. 20 (5): 04014078. https://doi.org/10.1061/(ASCE)BE.1943-5592.0000674.
Cantero, D., R. Karoumi, and A. González. 2015. “The virtual axle concept for detection of localised damage using bridge weigh-in-motion data.” Eng. Struct. 89 (Apr): 26–36. https://doi.org/10.1016/j.engstruct.2015.02.001.
Chen, B., Z. Zhong, X. Xie, and P. Lu. 2014. “Measurement-based vehicle load model for urban expressway bridges.” Math. Probl. Eng. 2014 (12): 1–10. https://doi.org/10.1155/2014/340896.
Chen, Z.-W., Q.-L. Cai, and S. Zhu. 2018. “Damage quantification of beam structures using deflection influence lines.” Struct. Control Health Monit. 25 (11): e2242. https://doi.org/10.1002/stc.2242.
Dimitris, S. 2003. “Confidence intervals of in the case of stationary random noise measurements.” Environ. Monit. Assess. 85 (1): 55–67. https://doi.org/10.1023/A:1023353000071.
Ding, Y.-L., H.-W. Zhao, and A.-Q. Li. 2017. “Temperature effects on strain influence lines and dynamic load factors in a steel-truss arch railway bridge using adaptive fir filtering.” J. Perform. Constr. Facil. 31 (4): 04017024. https://doi.org/10.1061/(ASCE)CF.1943-5509.0001026.
Getachew, A., and E. J. O’Brien. 2007. “Simplified site-specific traffic load models for bridge assessment.” Struct. Infrastruct. Eng. 3 (4): 303–311. https://doi.org/10.1080/15732470500424245.
González, A., J. Dowling, E. J. O’Brien, and A. Žnidarič. 2012. “Testing of a bridge weigh-in-motion algorithm utilising multiple longitudinal sensor locations.” J. Test. Eval. 40 (6): 961–974. https://doi.org/10.1520/JTE104576.
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 (Aug): 70–82. https://doi.org/10.1016/j.engstruct.2017.05.009.
Helmi, K., T. Taylor, and F. Ansari. 2015. “Shear force–based method and application for real-time monitoring of moving vehicle weights on bridges.” J. Intell. Mater. Syst. Struct. 26 (5): 505–516. https://doi.org/10.1177/1045389X14529612.
Hester, D., J. Brownjohn, F. Huseynov, E. O’Brien, A. Gonzalez, and M. Casero. 2020. “Identifying damage in a bridge by analysing rotation response to a moving load.” Struct. Infrastruct. Eng. 16 (7): 1050–1065. https://doi.org/10.1080/15732479.2019.1680710.
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 (Jan): 106380. https://doi.org/10.1016/j.ymssp.2019.106380.
Jalinoos, F., M. Amjadian, A. K. Agrawal, C. Brooks, and D. Banach. 2020. “Experimental evaluation of unmanned aerial system for measuring bridge movement.” J. Bridge Eng. 25 (1): 04019132. https://doi.org/10.1061/(ASCE)BE.1943-5592.0001508.
Lansdell, A., W. Song, and B. Dixon. 2017. “Development and testing of a bridge weigh-in-motion method considering nonconstant vehicle speed.” Eng. Struct. 152 (Dec): 709–726. https://doi.org/10.1016/j.engstruct.2017.09.044.
Liu, Y., and S. Zhang. 2018. “Damage localization of beam bridges using quasi-static strain influence lines based on the BOTDA technique.” Sensors 18 (12): 1–26. https://doi.org/10.3390/s18124446.
Liu, Z., T. Guo, M. H. Hebdon, and Z. Zhang. 2018. “Corrosion fatigue analysis and reliability assessment of short suspenders in suspension and arch bridges.” J. Perform. Constr. Facil. 32 (5): 04018060. https://doi.org/10.1061/(ASCE)CF.1943-5509.0001203.
Martinez, D., A. Malekjafarian, and E. J. O’Brien. 2020. “Bridge health monitoring using deflection measurements under random traffic.” Struct. Control Health Monit. 27 (9): 1–17. https://doi.org/10.1002/stc.2593.
Moses, F. 1979. “Weigh-in-motion system using instrumented bridge.” Transp. Eng. J. 105 (3): 233–249. https://doi.org/10.1061/TPEJAN.0000783.
O’Brien, E., C. Carey, and J. Keenahan. 2015. “Bridge damage detection using ambient traffic and moving force identification.” Struct. Control Health Monit. 22 (12): 1396–1407. https://doi.org/10.1002/stc.1749.
O’Brien, E., M. A. Khan, D. P. McCrum, and A. Žnidarič. 2020. “Using statistical analysis of an acceleration-based bridge weigh-in-motion system for damage detection.” Appl. Sci. 10 (2): 1–20. https://doi.org/10.3390/app10020663.
Oskoui, E. A., T. Taylor, and F. Ansari. 2020. “Method and sensor for monitoring weight of trucks in motion based on bridge girder end rotations.” Struct. Infrastruct. Eng. 16 (3): 481–494. https://doi.org/10.1080/15732479.2019.1668436.
Paeglitis, A., and A. Paeglitis. 2014. “Traffic load models for Latvian road bridges with span length up to 30 meters.” Balt. J. Road Bridge Eng. 9 (2): 139–145. https://doi.org/10.3846/bjrbe.2014.18.
Quilligan, M., R. Karoumi, and E. J. O’Brien. 2002. “Development and testing of a 2-dimensional multi-vehicle bridge-WIM algorithm.” In Proc., 3rd Int. Conf. on Weigh-in-Motion, edited by B. Jacob, B. McCall, and E. OBrien, 199–208. Dübendorf, Switzerland: International Society for Weigh-In-Motion.
Shahsavari, V., M. Mehrkash, and S.-B. Santini-Bell. 2020. “Damage detection and decreased load-carrying capacity assessment of a vertical-lift steel truss bridge.” J. Perform. Constr. Facil. 34 (2): 04019123. https://doi.org/10.1061/(ASCE)CF.1943-5509.0001400.
Yang, D.-H., T.-H. Yi, and H.-N. Li. 2017. “Coupled fatigue-corrosion failure analysis and performance assessment of RC bridge deck slabs.” J. Bridge Eng. 22 (10): 04017077. https://doi.org/10.1061/(ASCE)BE.1943-5592.0001108.
Zeinali, Y., and B. A. Story. 2018. “Impairment localization and quantification using noisy static deformation influence lines and iterative multi-parameter tikhonov regularization.” Mech. Syst. Sig. Process. 109 (Sep): 399–419. https://doi.org/10.1016/j.ymssp.2018.02.036.
Zhang, L., G. Wu, H.-L. Li, and S.-Z. Chen. 2020. “Synchronous identification of damage and vehicle load on simply supported bridges based on long-gauge fiber Bragg grating sensors.” J. Perform. Constr. Facil. 34 (1): 04019097. https://doi.org/10.1061/(ASCE)CF.1943-5509.0001376.
Zheng, X., D.-H. Yang, T.-H. Yi, and H.-N. Li. 2019a. “Development of bridge influence line identification methods based on direct measurement data: A comprehensive review and comparison.” Eng. Struct. 198 (Nov): 109539. https://doi.org/10.1016/j.engstruct.2019.109539.
Zheng, X., D.-H. Yang, T.-H. Yi, H.-N. Li, and Z.-W. Chen. 2019b. “Bridge influence line identification based on regularized least-squares QR decomposition method.” J. Bridge Eng. 24 (8): 06019004. https://doi.org/10.1061/(ASCE)BE.1943-5592.0001458.
Zolghadri, N., M. W. Halling, N. Johnson, and P. J. Barr. 2016. “Field verification of simplified bridge weigh-in-motion techniques.” J. Bridge Eng. 21 (10): 04016063. https://doi.org/10.1061/(ASCE)BE.1943-5592.0000930.
Information & Authors
Information
Published In
Journal of Performance of Constructed Facilities
Volume 35 • Issue 5 • October 2021
Copyright
© 2021 American Society of Civil Engineers.
History
Received: Jan 7, 2021
Accepted: Jun 7, 2021
Published online: Aug 5, 2021
Published in print: Oct 1, 2021
Discussion open until: Jan 5, 2022
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.
Cited by
- Yun-Tao Wei, Ting-Hua Yi, Dong-Hui Yang, Hua Liu, Yang Deng, Unsupervised Stiffness Evaluation of High-Speed Railway Bridges Using Periodic Monitoring Data, Journal of Bridge Engineering, 10.1061/JBENF2.BEENG-6561, 29, 3, (2024).
- Han-Wen Zheng, Ting-Hua Yi, Xu Zheng, Yun-Tao Wei, Hong-Nan Li, Operational Influence Line Identification of High-Speed Railway Bridge Considering Uncertainty of Train Load, ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering, 10.1061/AJRUA6.RUENG-1247, 10, 4, (2024).
- Debojyoti Paul, Koushik Roy, Application of bridge weigh-in-motion system in bridge health monitoring: a state-of-the-art review, Structural Health Monitoring, 10.1177/14759217231154431, (147592172311544), (2023).
- Longwei Zhang, Eugene J. OBrien, Donya Hajializadeh, Lu Deng, Shiding Yin, Bridge Damage Identification Using Rotation Measurement, Journal of Bridge Engineering, 10.1061/JBENF2.BEENG-5891, 28, 5, (2023).
- Ananta Sinha, Mi G. Chorzepa, Jidong J. Yang, Sung-Hee Sonny Kim, Stephan Durham, Deep-Learning-Based Temporal Prediction for Mitigating Dynamic Inconsistency in Vehicular Live Loads on Roads and Bridges, Infrastructures, 10.3390/infrastructures7110150, 7, 11, (150), (2022).