Bridge Cable Anomaly Detection Based on Local Variability in Feature Vector of Monitoring Group Cable Forces
Publication: Journal of Bridge Engineering
Volume 28, Issue 6
Abstract
The cable is the key supporting component of the cable-stayed bridges, and damaged cables will directly affect the safety and stability of the bridge in operation. Therefore, this paper proposes a bridge cable anomaly detection and localization method based on the variation in the group cable force feature vector. First, mechanical analysis was carried out on the correlation of cable forces between two cables on the same side induced by the single-vehicle case and a cable force feature vector was established to reflect the mechanical characteristics of the group cable forces. Second, a bridge cable anomaly detection method was proposed based on the variation in the group cable force feature vector. Third, the isolation of abnormal components of the cable force feature vector, which can accurately localize abnormal cables, was presented. The long-term monitoring data of an in-service cable-stayed bridge were utilized to validate the effectiveness of the proposed method. The results demonstrated that the proposed method could effectively detect abnormal cable conditions and accurately localize abnormal cables. In addition, the proposed bridge cable feature index and anomaly detection method were robust to the situation of partially missing data due to sensor fault.
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Acknowledgments
This research work was jointly supported by the National Natural Science Foundation of China (Grants Nos. 52078102 and 52250011); the Fundamental Research Funds for the Central Universities (Grant Nos. DUT21JC38, DUT22ZD213, and DUT22QN235); and the Key Laboratory of Performance Evolution and Control for Engineering Structures (Tongji University), Ministry of Education (Grant No. 2022KF-1). The authors thank the organizers of the 1st International Project Competition for SHM (IPC-SHM, 2020) for providing the invaluable data used in this paper.
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.
Alcala, C. F., and S. J. Qin. 2011. “Analysis and generalization of fault diagnosis methods for process monitoring.” J. Process Control 21 (3): 322–330. https://doi.org/10.1016/j.jprocont.2010.10.005.
Avci, O., O. Abdeljaber, S. Kiranyaz, M. Hussein, M. Gabbouj, and D. J. Inman. 2021. “A review of vibration-based damage detection in civil structures: From traditional methods to machine learning and deep learning applications.” Mech. Syst. Signal Process. 147: 107077. https://doi.org/10.1016/j.ymssp.2020.107077.
Bao, Y., J. Li, T. Nagayama, Y. Xu, B. F. Spencer Jr., and H. Li. 2021. “The 1st international project competition for structural health monitoring (IPC-SHM, 2020), a summary and benchmark problem.” Struct. Health Monit. 20 (4): 2229–2239. https://doi.org/10.1177/14759217211006485.
Bhowmik, B., M. Krishnan, B. Hazra, and V. Pakrashi. 2019. “Real-time unified single- and multi-channel structural damage detection using recursive singular spectrum analysis.” Struct. Health Monit. 18 (2): 563–589. https://doi.org/10.1177/1475921718760483.
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.
Breunig, M. M., H. P. Kriegel, R. T. Ng, and J. Sander. 2000. “LOF: Identifying density-based local outliers.” In Proc. 2000 ACM SIGMOD Int. Conf. on Management of Data, 93–104. Dallas, TX: ACM Press.
CEN (European Committee for Standardization). 2003. Actions on structures—Part 2: Traffic loads on bridges. Eurocode 1. EN1991-2. Brussels, Belgium: CEN.
Chen, C. C., W. H. Wu, C. Y. Liu, and G. Lai. 2016. “Damage detection of a cable-stayed bridge based on the variation of stay cable forces eliminating environmental temperature effects.” Smart Struct. Syst. 17 (6): 859–880. https://doi.org/10.12989/sss.2016.17.6.859.
Christen, R., A. Bergamini, and M. Motavalli. 2003. “Three-dimensional localization of defects in stay cables using magnetic flux leakage methods.” J. Nondestr. Eval. 22 (3): 93–101. https://doi.org/10.1023/B:JONE.0000010736.74285.b6.
Donoho, D. L. 2000. “High-dimensional data analysis: The curses and blessings of dimensionality.” Lect. Math Challenges Century 13: 178–183.
Entezami, A., H. Shariatmadar, and A. Karamodin. 2019. “Data-driven damage diagnosis under environmental and operational variability by novel statistical pattern recognition methods.” Struct. Health Monit. 18 (5–6): 1416–1443. https://doi.org/10.1177/1475921718800306.
Fan, Z. Y., Q. Huang, Y. Ren, Z. Y. Zhu, and X. Xu. 2020. “A cointegration approach for cable anomaly warning based on structural health monitoring data: An application to cable-stayed bridges.” Adv. Struct. Eng. 23 (13): 2789–2802. https://doi.org/10.1177/1369433220924793.
Farrar, C. R., and K. Worden. 2013. Structural health monitoring: A machine learning perspective. Chichester, UK: Wiley.
Goldstein, M., and S. Uchida. 2016. “A comparative evaluation of unsupervised anomaly detection algorithms for multivariate data.” PLoS One 11 (4): e0152173. https://doi.org/10.1371/journal.pone.0152173.
Hirachan, J., and M. Chajes. 2005. “Experimental influence lines for bridge evaluation.” Bridge Struct. 1 (4): 405–412. https://doi.org/10.1080/15732480600578485.
Huang, Y., Y. Wang, J. Fu, A. Liu, and W. Gao. 2018. “Measurement of the real-time deflection of cable-stayed bridge based on cable tension variations.” Measurement 119: 218–228. https://doi.org/10.1016/j.measurement.2018.01.070.
Li, J. X., T. H. Yi, C. X. Qu, H. Liu, G. H. Zhang, and J. G. Han. 2022. “Performance alarming for stay cables based on a frequency–deformation relationship model.” J. Bridge Eng. 27 (9): 04022078. https://doi.org/10.1061/(ASCE)BE.1943-5592.0001926.
Li, S., S. Wei, Y. Bao, and H. Li. 2018. “Condition assessment of cables by pattern recognition of vehicle-induced cable tension ratio.” Eng. Struct. 155: 1–15. https://doi.org/10.1016/j.engstruct.2017.09.063.
Mehrabi, A. B. 2006. “In-service evaluation of cable-stayed bridges, overview of available methods and findings.” J. Bridge Eng. 11 (6): 716–724. https://doi.org/10.1061/(ASCE)1084-0702(2006)11:6(716).
Mehrabi, A. B. 2016. “Performance of cable-stayed bridges: Evaluation methods, observations, and a rehabilitation case.” J. Perform. Constr. Facil. 30 (1): C4014007. https://doi.org/10.1061/(ASCE)CF.1943-5509.0000715.
Nazarian, E., F. Ansari, X. Zhang, and T. Taylor. 2016. “Detection of tension loss in cables of cable-stayed bridges by distributed monitoring of bridge deck strains.” J. Struct. Eng. 142 (6): 04016018. https://doi.org/10.1061/(ASCE)ST.1943-541X.0001463.
Pan, H., M. Azimi, F. Yan, and Z. Lin. 2018. “Time-frequency-based data-driven structural diagnosis and damage detection for cable-stayed bridges.” J. Bridge Eng. 23 (6): 04018033. https://doi.org/10.1061/(ASCE)BE.1943-5592.0001199.
Panda, S., T. Tripura, and B. Hazra. 2021. “First-order error-adapted eigen perturbation for real-time modal identification of vibrating structures.” J. Vib. Acoust. 143 (5): 051001. https://doi.org/10.1115/1.4049268.
Peng, Z., J. Li, and H. Hao. 2022. “Long-term condition monitoring of cables for in-service cable-stayed bridges using matched vehicle-induced cable tension ratios.” Smart Struct. Syst. 29: 167–179. https://doi.org/10.12989/sss.2022.29.1.167.
Ren, Y., X. Xu, Q. Huang, D. Y. Zhao, and J. Yang. 2019. “Long-term condition evaluation for stay cable systems using dead load-induced cable forces.” Adv. Struct. Eng. 22 (7): 1644–1656. https://doi.org/10.1177/1369433218824486.
Sarmadi, H., and A. Karamodin. 2020. “A novel anomaly detection method based on adaptive Mahalanobis-squared distance and one-class kNN rule for structural health monitoring under environmental effects.” Mech. Syst. Signal Process. 140: 106495. https://doi.org/10.1016/j.ymssp.2019.106495.
Sun, H., J. Xu, W. Chen, and J. Yang. 2018. “Time-dependent effect of corrosion on the mechanical characteristics of stay cable.” J. Bridge Eng. 23 (5): 04018019. https://doi.org/10.1061/(ASCE)BE.1943-5592.0001229.
Sun, L., Z. Shang, Y. Xia, S. Bhowmick, and S. Nagarajaiah. 2020. “Review of bridge structural health monitoring aided by big data and artificial intelligence: From condition assessment to damage detection.” J. Struct. Eng. 146 (5): 04020073. https://doi.org/10.1061/(ASCE)ST.1943-541X.0002535.
Tang, J., Z. Chen, A. W. Fu, and D. W. Cheung. 2007. “Capabilities of outlier detection schemes in large datasets, framework and methodologies.” Knowl. Inf. Syst. 11 (1): 45–84. https://doi.org/10.1007/s10115-005-0233-6.
Timoshenko, S. P., and D. H. Young. 1968. Theory of structures. New York: McGraw-Hill.
Wang, G., and J. Ye. 2019. “Localization and quantification of partial cable damage in the long-span cable-stayed bridge using the abnormal variation of temperature-induced girder deflection.” Struct. Control Health Monit. 26 (1): e2281. https://doi.org/10.1002/stc.2281.
Wu, G. M., T. H. Yi, D. H. Yang, and H. N. Li. 2021. “Damage identification of tie-down cables in cable-stayed bridges using vehicle-induced displacement.” J. Perform. Constr. Facil. 35 (3): 04021011. https://doi.org/10.1061/(ASCE)CF.1943-5509.0001590.
Yang, D. H., H. L. Gu, T. H. Yi, and Z. J. Wu. 2022a. “Cable anomaly detection driven by spatiotemporal correlation dissimilarity measurements of bridge grouped cable forces.” Smart Struct. Syst. 30: 661–671. https://doi.org/10.12989/sss.2022.30.6.661.
Yang, D. H., Z. X. Guan, T. H. Yi, H. N. Li, and Y. S. Ni. 2022b. “Fatigue evaluation of bridges based on strain influence line loaded by elaborate stochastic traffic flow.” J. Bridge Eng. 27 (9): 04022082. https://doi.org/10.1061/(ASCE)BE.1943-5592.0001929.
Yang, D. H., T. H. Yi, H. N. Li, and Y. F. Zhang. 2018. “Correlation-based estimation method for cable-stayed bridge girder deflection variability under thermal action.” J. Perform. Constr. Facil. 32 (5): 04018070. https://doi.org/10.1061/(ASCE)CF.1943-5509.0001212.
Zejli, H., L. Gaillet, A. Laksimi, and S. Benmedakhene. 2012. “Detection of the presence of broken wires in cables by acoustic emission inspection.” J. Bridge Eng. 17 (6): 921–927. https://doi.org/10.1061/(ASCE)BE.1943-5592.0000404.
Information & Authors
Information
Published In
Journal of Bridge Engineering
Volume 28 • Issue 6 • June 2023
Copyright
© 2023 American Society of Civil Engineers.
History
Received: Sep 25, 2022
Accepted: Feb 10, 2023
Published online: Apr 10, 2023
Published in print: Jun 1, 2023
Discussion open until: Sep 10, 2023
ASCE Technical Topics:
- Architectural engineering
- Bridge components
- Bridge engineering
- Bridge management
- Bridges
- Bridges (by type)
- Building management
- Cable stayed bridges
- Cables
- Detection methods
- Engineering fundamentals
- Equipment and machinery
- Maintenance and operation
- Mathematics
- Measurement (by type)
- Methodology (by type)
- Sensors and sensing
- Structural engineering
- Vector analysis
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Cited by
- Ze-Xin Guan, Ting-Hua Yi, Dong-Hui Yang, Hong-Nan Li, Dynamic Calibrating of Multiscale Bridge Model Using Long-Term Stochastic Vehicle-Induced Responses, Journal of Bridge Engineering, 10.1061/JBENF2.BEENG-6783, 29, 9, (2024).