Browse > Article
http://dx.doi.org/10.12989/sss.2022.29.1.167

Long-term condition monitoring of cables for in-service cable-stayed bridges using matched vehicle-induced cable tension ratios  

Peng, Zhen (Guangzhou University-Curtin University Joint Research Centre for Structural Monitoring and Protection against Multi-Dynamic Hazards, School of Civil Engineering, Guangzhou University)
Li, Jun (Guangzhou University-Curtin University Joint Research Centre for Structural Monitoring and Protection against Multi-Dynamic Hazards, School of Civil Engineering, Guangzhou University)
Hao, Hong (Centre for Infrastructural Monitoring and Protection, School of Civil and Mechanical Engineering, Curtin University)
Publication Information
Smart Structures and Systems / v.29, no.1, 2022 , pp. 167-179 More about this Journal
Abstract
This article develops a long-term condition assessment method for stay cables in cable stayed bridges using the monitored cable tension forces under operational condition. Based on the concept of influence surface, the matched cable tension ratio of two cables located at the same side (either in the upstream side or downstream side) is theoretically proven to be related to the condition of stay cables and independent of the positions of vehicles on the bridge. A sensor grouping scheme is designed to ensure that reliable damage detection result can be obtained even when sensor fault occurs in the neighbor of the damaged cable. Cable forces measured from an in-service cable-stayed bridge in China are used to demonstrate the accuracy and effectiveness of the proposed method. Damage detection results show that the proposed approach is sensitive to the rupture of wire damage in a specific cable and is robust to environmental effects, measurement noise, sensor fault and different traffic patterns. Using the damage sensitive feature in the proposed approach, the metrics such as accuracy, precision, recall and F1 score, which are used to evaluate the performance of damage detection, are 97.97%, 95.08%, 100% and 97.48%, respectively. These results indicate that the proposed approach can reliably detect the damage in stay cables. In addition, the proposed approach is efficient and promising with applications to the field monitoring of cables in cable-stayed bridges.
Keywords
cable damage; cable-stayed bridges; cable tension ratio; damage detection; influence surface; sensor fault;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
연도 인용수 순위
1 Chen, Z.W., Zhu, S., Xu, Y.L., Li, Q. and Cai, Q.L. (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   DOI
2 Chen, C.C., Wu, W.H., Liu, C.Y. and Lai, G. (2016), "Damage detection of a cable-stayed bridge based on the variation of stay cable forces eliminating environmental temperature effects", Smart Struct. Syst., Int. J., 17(6), 859-880. https://doi.org/10.12989/sss.2016.17.6.859   DOI
3 Kangas, S., Helmicki, A., Hunt, V., Sexton, R. and Swanson, J. (2012), "Cable-stayed bridges: case study for ambient vibration-based cable tension estimation", J. Bridge Eng., 17(6), 839-846. https://doi.org/10.1061/(ASCE)BE.1943-5592.0000364   DOI
4 Xu, Z.D. and Wu, Z. (2007), "Simulation of the effect of temperature variation on damage detection in a long-span cable-stayed bridge", Struct. Health Monitor, 6(3), 177-189. https://doi.org/10.1177/1475921707081107   DOI
5 Zhang, L.X., Qiu, G.Y. and Chen, Z.S. (2020), "Structural health monitoring methods of cables in cable-stayed bridge: A review", Measurement, 108343. https://doi.org/10.1016/j.measurement.2020.108343   DOI
6 Alamdari, M.M., Kildashti, K., Samali, B. and Goudarzi, H.V. (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   DOI
7 Nghia, N.T. and Samec, V. (2016), "Cable stay bridges investigation of cable rupture". Int. J. Civil Eng. Archit., 10, 270-279. https://doi.org/10.17265/1934-7359/2016.05.006.   DOI
8 Kim, S.W., Jeon, B.G., Cheung, J.H., Kim, S.D. and Park, J.B. (2017), "Stay cable tension estimation using a vision-based monitoring system under various weather conditions", J. Civil Struct. Health Monitor., 7(3), 343-357. https://doi.org/10.1007/s13349-017-0226-7   DOI
9 Bao, Y., Li, H., Chen, Z., Zhang, F. and Guo, A. (2016), "Sparse l1 optimization-based identification approach for the distribution of moving heavy vehicle loads on cable-stayed bridges", Struct. Control Health Monitor., 23(1), 144-155. https://doi.org/10.1002/stc.1763   DOI
10 Bao, Y., Li, J., Nagayama, T., Xu, Y., Spencer Jr, B.F. and Li, H. (2021), "The 1st International Project Competition for Structural Health Monitoring (IPC-SHM, 2020): A summary and benchmark problem", Struct. Health Monitor., 14759217211006485. https://doi.org/10.1177/14759217211006485   DOI
11 Kullaa, J. (2013), "Detection, identification, and quantification of sensor fault in a sensor network", Mech. Syst. Signal Process, 40(1), 208-221. https://doi.org/10.1016/j.ymssp.2013.05.007   DOI
12 Li, H., Li, S., Ou, J. and Li, H. (2012), "Reliability assessment of cable-stayed bridges based on structural health monitoring techniques", Struct. Infrastruct. Eng., 8(9), 829-845. https://doi.org/10.1080/15732479.2010.496856   DOI
13 Li, S., Wei, S., Bao, Y. and Li, H. (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   DOI
14 Liu, C., Teng, J. and Peng, Z. (2020), "Optimal sensor placement for bridge damage detection using deflection influence line". Smart Struct. Syst., Int. J., 25(2), 169-181. https://doi.org/10.12989/sss.2020.25.2.169   DOI
15 Martins, A.M., Simoes, L.M. and Negrao, J.H. (2015), "Cable stretching force optimization of concrete cable-stayed bridges including construction stages and time-dependent effects", Struct. Multidiscip. Optim., 51(3), 757-772. https://doi.org/10.1007/s00158-014-1153-4   DOI
16 Stromquist-LeVoir, G., McMullen, K.F., Zaghi, A.E. and Christenson, R. (2018), "Determining time variation of cable tension forces in suspended bridges using time-frequency analysis", Adv. Civil Eng., 2018. https://doi.org/10.1155/2018/1053232   DOI
17 Vivo-Truyols, G., Torres-Lapasio, J.R., Van Nederkassel, A.M., Vander Heyden, Y. and Massart, D.L. (2005), "Automatic program for peak detection and deconvolution of multi-overlapped chromatographic signals: Part I: Peak detection", J. Chromatogr. A, 1096(1-2), 133-145. https://doi.org/10.1016/j.chroma.2005.03.092   DOI
18 Li, H. and Ou, J.P. (2016), "The state of the art in structural health monitoring of cable-stayed bridges", J. Civil Struct. Health Monitor., 6(1), 43-67. https://doi.org/10.1007/s13349-015-0115-x   DOI
19 Li, J., Hao, H. and Zhu, H.P. (2014), "Dynamic assessment of shear connectors in composite bridges with ambient vibration measurements", Adv. Struct. Eng., 17(5), 617-637. https://doi.org/10.1260/1369-4332.17.5.617   DOI
20 Davalos, E. (2000), "Structural behaviour of cable-stayed bridges", Ph.D. Dissertation; Massachusetts Institute of Technology.
21 Fan, G., Li, J. and Hao, H. (2019), "Lost data recovery for structural health monitoring based on convolutional neural networks", Struct. Control Health Monitor., 26(10), e2433. https://doi.org/10.1002/stc.2433   DOI
22 Fan, Z.Y., Huang, Q., Ren, Y., Zhu, Z.Y. and Xu, X. (2020a), "A cointegration approach for cable anomaly warning based on structural health monitoring data: An application to cable-stayed bridges", Int. J. Adv. Struct. Eng., 23(13), 2789-2802. https://doi.org/10.1177/1369433220924793   DOI
23 Feng, D., Scarangello, T., Feng, M.Q. and Ye, Q. (2017), "Cable tension force estimate using novel noncontact vision-based sensor", Measurement, 99, 44-52. https://doi.org/10.1016/j.measurement.2016.12.020   DOI
24 Guo, W.H. and Xu, Y.L. (2001), "Fully computerized approach to study cable-stayed bridge-vehicle interaction", J. Sound Vib., 248(4), 745-761. https://doi.org/10.1006/jsvi.2001.3828   DOI
25 Fan, G., Li, J. and Hao, H. (2020b), "Vibration signal denoising for structural health monitoring by residual convolutional neural networks", Measurement, 157, 107651. https://doi.org/10.1016/j.measurement.2020.107651   DOI