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http://dx.doi.org/10.12989/sss.2022.29.1.105

Condition assessment of stay cables through enhanced time series classification using a deep learning approach  

Zhang, Zhiming (School for Engineering of Matter, Transport and Energy, Arizona State University)
Yan, Jin (Palo Alto Research Center)
Li, Liangding (Department of Computer Science, University of Central Florida)
Pan, Hong (Department of Civil and Environmental Engineering, North Dakota State University)
Dong, Chuanzhi (Department of Civil, Environmental, and Construction Engineering, University of Central Florida)
Publication Information
Smart Structures and Systems / v.29, no.1, 2022 , pp. 105-116 More about this Journal
Abstract
Stay cables play an essential role in cable-stayed bridges. Severe vibrations and/or harsh environment may result in cable failures. Therefore, an efficient structural health monitoring (SHM) solution for cable damage detection is necessary. This study proposes a data-driven method for immediately detecting cable damage from measured cable forces by recognizing pattern transition from the intact condition when damage occurs. In the proposed method, pattern recognition for cable damage detection is realized by time series classification (TSC) using a deep learning (DL) model, namely, the long short term memory fully convolutional network (LSTM-FCN). First, a TSC classifier is trained and validated using the cable forces (or cable force ratios) collected from intact stay cables, setting the segmented data series as input and the cable (or cable pair) ID as class labels. Subsequently, the classifier is tested using the data collected under possible damaged conditions. Finally, the cable or cable pair corresponding to the least classification accuracy is recommended as the most probable damaged cable or cable pair. A case study using measured cable forces from an in-service cable-stayed bridge shows that the cable with damage can be correctly identified using the proposed DL-TSC method. Compared with existing cable damage detection methods in the literature, the DL-TSC method requires minor data preprocessing and feature engineering and thus enables fast and convenient early detection in real applications.
Keywords
bridge cable; damage detection; deep learning; time series classification;
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1 Yang, Y., Li, S., Nagarajaiah, S., Li, H. and Zhou, P. (2016), "Real-time output-only identification of time-varying cable tension from accelerations via complexity pursuit", J. Struct. Eng., 142(1), 04015083. https://doi.org/10.1061/(ASCE)ST.1943-541X.0001337   DOI
2 Zhang, Z. and Sun, C. (2020a), "Multi-site structural damage identification using a multi-label classification scheme of machine learning", Measurement, 154, 107473. https://doi.org/10.1016/j.measurement.2020.107473   DOI
3 Teimouri, N., Dyrmann, M. and Jorgensen, R.N. (2019), "A novel spatio-temporal fcn-lstm network for recognizing various crop types using multi-temporal radar images", Remote Sensing, 11(8), 990. https://doi.org/10.3390/rs11080990   DOI
4 Xu, H., Gao, Y., Yu, F. and Darrell, T. (2017), "End-to-end learning of driving models from large-scale video datasets", Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2174-2182.
5 Zhang, Z. (2020), "Data-Driven and Model-Based Methods with Physics-Guided Machine Learning for Damage Identification", Ph.D. Thesis; Louisiana State University.
6 Zhang, Z. and Sun, C. (2020c), "A numerical study on multi-site damage identification: A data-driven method via constrained independent component analysis", Struct. Control Health Monitor., 27(10), e2583. https://doi.org/10.1002/stc.2583   DOI
7 Tang, Z., Chen, Z., Bao, Y. and Li, H. (2019), "Convolutional neural network-based data anomaly detection method using multiple information for structural health monitoring", Struct. Control Health Monitor., 26(1), e2296. https://doi.org/10.1002/stc.2296   DOI
8 Wang, F. and Chan, T. (2009), "Review of vibration-based damage detection and condition assessment of bridge structures using structural health monitoring", Proceedings of the 2nd Infrastructure Theme Postgraduate Conference: Rethinking Sustainable Development-Planning, Infrastructure Engineering, Design and Managing Urban Infrastructure, Brisbane, Australia, pp. 35-47.
9 Wang, Z., Yan, W. and Oates, T. (2017), "Time series classification from scratch with deep neural networks: A strong baseline", Proceedings of 2017 International Joint Conference on Neural Networks (IJCNN), Anchorage, AK, USA, May, pp. 1578-1585. https://doi.org/10.1109/IJCNN.2017.7966039   DOI
10 Zhang, Z. and Sun, C. (2020b), "Structural damage identification via physics-guided machine learning: a methodology integrating pattern recognition with finite element model updating", Struct. Health Monitor., 20(4), 1675-1688. https://doi.org/10.1177/1475921720927488   DOI
11 Zhang, Z., Sun, C., Li, C. and Sun, M. (2019a), "Vibration based bridge scour evaluation: A data-driven method using support vector machines", Struct. Monitor. Maint., Int. J., 6(2), 125-145. https://doi.org/10.12989/smm.2019.6.2.125   DOI
12 Zheng, S., Ristovski, K., Farahat, A. and Gupta, C. (2017), "Long short-term memory network for remaining useful life estimation", Proceedings of 2017 IEEE International Conference on Prognostics and Health Management (ICPHM), Dallas, TX, USA, June, pp. 88-95.
13 Xing, Z., Pei, J. and Keogh, E. (2010), "A brief survey on sequence classification", ACM Sigkdd Explorations Newsletter, 12(1), 40-48. https://doi.org/10.1145/1882471.1882478   DOI
14 Xu, Y., Bao, Y., Chen, J., Zuo, W. and Li, H. (2019), "Surface fatigue crack identification in steel box girder of bridges by a deep fusion convolutional neural network based on consumer-grade camera images", Struct. Health Monitor., 18(3), 653-674. https://doi.org/10.1177/1475921718764873   DOI
15 Ye, X.W., Dong, C.Z. and Liu, T. (2016), "Force monitoring of steel cables using vision-based sensing technology: methodology and experimental verification", Smart Struct. Syst., Int. J., 18(3), 585-599. https://doi.org/10.12989/sss.2016.18.3.585   DOI
16 Zhang, Z., Sun, C. and Guo, B. (2021), "Transfer-learning guided Bayesian model updating for damage identification considering modeling uncertainty", Mech. Syst. Signal Process., 166, 108426. https://doi.org/10.1016/j.ymssp.2021.108426   DOI
17 Zheng, Y., Liu, Q., Chen, E., Ge, Y. and Zhao, J.L. (2014), "Time series classification using multi-channels deep convolutional neural networks", Proceedings of International Conference on Web-Age Information Management, pp. 298-310. https://doi.org/10.1007/978-3-319-08010-9_33   DOI
18 Zhang, R., Liu, Y. and Sun, H. (2020), "Physics-informed multi-LSTM networks for metamodeling of nonlinear structures", Comput. Methods Appl. Mech. Eng., 369, 113226. https://doi.org/10.1016/j.cma.2020.113226   DOI
19 Ko, J.M. and Ni, Y.Q. (2005), "Technology developments in structural health monitoring of large-scale bridges", Eng. Struct., 27(12), 1715-1725. https://doi.org/10.1016/j.engstruct.2005.02.021   DOI
20 Langkvist, M., Karlsson, L. and Loutfi, A. (2014), "A review of unsupervised feature learning and deep learning for time-series modeling", Pattern Recogn. Lett., 42, 11-24. https://doi.org/10.1016/j.patrec.2014.01.008   DOI
21 Macdonald, J.H. and Daniell, W.E. (2005), "Variation of modal parameters of a cable-stayed bridge identified from ambient vibration measurements and FE modelling", Eng. Struct., 27(13), 1916-1930. https://doi.org/10.1016/j.engstruct.2005.06.007   DOI
22 Li, H. and Ou, J. (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
23 Li, H., Ou, J. and Zhou, Z. (2009), "Applications of optical fibre bragg gratings sensing technology-based smart stay cables", Optics Lasers Eng., 47(10), 1077-1084. https://doi.org/10.1016/j.optlaseng.2009.04.016   DOI
24 Ma, T., Xiao, C. and Wang, F. (2018), "Health-atm: A deep architecture for multifaceted patient health record representation and risk prediction", Proceedings of the 2018 SIAM International Conference on Data Mining, San Diego, CA, USA, pp. 261-269. https://doi.org/10.1137/1.9781611975321.30   DOI
25 McInnes, L., Healy, J. and Melville, J. (2018), "UMAP: Uniform manifold approximation and projection for dimension reduction", arXiv preprint arXiv:1802.03426.
26 Miyashita, T. and Nagai, M. (2008), "Vibration-based structural health monitoring for bridges using laser doppler vibrometers and mems-based technologies", Int. J. Steel Struct., 8(4), 325-331.
27 Orsenigo, C. and Vercellis, C. (2010), "Combining discrete svm and fixed cardinality warping distances for multivariate time series classification", Pattern Recog., 43(11), 3787-3794. https://doi.org/10.1016/j.patcog.2010.06.005   DOI
28 Pan, H., Azimi, M., Yan, F. and Lin, Z. (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   DOI
29 Abdeljaber, O., Avci, O., Kiranyaz, M.S., Boashash, B., Sodano, H. and Inman, D.J. (2018), "1-D CNNs for structural damage detection: Verification on a structural health monitoring benchmark data", Neurocomputing, 275, 1308-1317. https://doi.org/10.1016/j.neucom.2017.09.069   DOI
30 Abdelfattah, S.M., Abdelrahman, G.M. and Wang, M. (2018), "Augmenting the size of eeg datasets using generative adversarial networks", Proceedings of 2018 International Joint Conference on Neural Networks (IJCNN), Rio de Janeiro, Brazil, July, pp. 1-6. https://doi.org/10.1109/IJCNN.2018.8489727   DOI
31 Bao, Y., Chen, Z., Wei, S., Xu, Y., Tang, Z. and Li, H. (2019a), "The state of the art of data science and engineering in structural health monitoring", Engineering, 5(2), 234-242. https://doi.org/10.1016/j.eng.2018.11.027   DOI
32 Bao, Y., Tang, Z., Li, H. and Zhang, Y. (2019b), "Computer vision and deep learning-based data anomaly detection method for structural health monitoring", Struct. Health Monitor., 18(2), 401-421. https://doi.org/10.1177/1475921718757405   DOI
33 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., 20(4), 2229-2239. https://doi.org/10.1177/14759217211006485   DOI
34 Karim, F., Majumdar, S., Darabi, H. and Harford, S. (2019b), "Multivariate LSTM-FCNs for time series classification", Neural Networks, 116, 237-245. https://doi.org/10.1016/j.neunet.2019.04.014   DOI
35 Santos, T. and Kern, R. (2016), "A literature survey of early time series classification and deep learning", In: SamI40 workshop at i-KNOW'16, Graz, Austria, October.
36 Spencer Jr, B.F., Hoskere, V. and Narazaki, Y. (2019), "Advances in computer vision-based civil infrastructure inspection and monitoring", Engineering, 5(2), 199-222. https://doi.org/10.1016/j.eng.2018.11.030   DOI
37 Bao, Y. and Li, H. (2020), "Machine learning paradigm for structural health monitoring", Struct. Health Monitor., 20(4), 1353-1372. https://doi.org/10.1177/1475921720972416   DOI
38 Cayton, L. (2005), "Algorithms for manifold learning", Univ. of California at San Diego Tech. Rep, 12(1-17), p. 1.
39 Dau, H.A., Bagnall, A., Kamgar, K., Yeh, C.C.M., Zhu, Y., Gharghabi, S., Ratanamahatana, C.A. and Keogh, E. (2019), "The UCR time series archive", IEEE/CAA J. Automatica Sinica, 6(6), 1293-1305. https://doi.org/10.1109/JAS.2019.1911747   DOI
40 Seto, S., Zhang, W. and Zhou, Y. (2015), "Multivariate time series classification using dynamic time warping template selection for human activity recognition", Proceedings of 2015 IEEE Symposium Series on Computational Intelligence, Cape Town, South Africa, December, pp. 1399-1406. https://doi.org/10.1109/SSCI.2015.199   DOI
41 Duan, Y.F., Zhang, R., Dong, C.Z., Luo, Y.Z., Or, S.W., Zhao, Y. and Fan, K.Q. (2016), "Development of Elasto-Magneto-Electric (EME) sensor for in-service cable force monitoring", Int. J. Struct. Stabil. Dyn., 16(04), 1640016. https://doi.org/10.1142/S0219455416400162   DOI
42 Higdon, B.P., El Mokhtari, K. and Basar, A. (2019), "Time-series-based classification of financial forecasting discrepancies", Proceedings of International Conference on Innovative Techniques and Applications of Artificial Intelligence, pp. 474-479. https://doi.org/10.1007/978-3-030-34885-4_39   DOI
43 Xu, N. and Liu, Y. (2021), "Fractal-based manifold learning for structure health monitoring", In: AIAA Scitech 2021 Forum, p. 1167. https://doi.org/10.2514/6.2021-1167   DOI
44 Li, H., Zhang, F. and Jin, Y. (2014b), "Real-time identification of time-varying tension in stay cables by monitoring cable transversal acceleration", Struct. Control Health Monitor., 21(7), 1100-1117. https://doi.org/10.1002/stc.1634   DOI
45 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
46 Talwalkar, A., Kumar, S. and Rowley, H. (2008), "Large-scale manifold learning", Proceedings of 2008 IEEE Conference on Computer Vision and Pattern Recognition, Anchorage, AK, USA, June, pp. 1-8. https://doi.org/10.1109/CVPR.2008.4587670   DOI
47 Zhang, W., Jin, F., Zhang, G., Zhao, B. and Hou, Y. (2019b), "Aero-engine remaining useful life estimation based on 1- dimensional FCN-LSTM neural networks", Proceedings of 2019 Chinese Control Conference (CCC), Guangzhou, China, July, pp. 4913-4918. https://doi.org/10.23919/ChiCC.2019.8866118   DOI
48 Lin, Y.Z., Nie, Z.H. and Ma, H.W. (2017), "Structural damage detection with automatic feature-extraction through deep learning", Comput.-Aided Civil Infrastr. Eng., 32(12), 1025-1046. https://doi.org/10.1111/mice.12313   DOI
49 Luo, C., Jiang, Z. and Zhang, Y. (2019), "A novel reconstructed training-set svm with roulette cooperative coevolution for financial time series classification", Expert Syst. Applicat., 123, 283-298. https://doi.org/10.1016/j.eswa.2019.01.022   DOI
50 Li, S., Huang, W., Wang, Z. and Lei, J. (2014a), "Design and aerodynamic investigation of a parallel vehicle on a wide-speed range", Sci. China Inform. Sci., 57(12), 1-10. https://doi.org/10.1007/s11432-014-5225-2   DOI
51 Sun, L., Shang, Z., Xia, Y., Bhowmick, S. and Nagarajaiah, S. (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   DOI
52 Gui, G., Pan, H., Lin, Z., Li, Y. and Yuan, Z. (2017), "Data-driven support vector machine with optimization techniques for structural health monitoring and damage detection", KSCE J. Civil Eng., 21(2), 523-534. https://doi.org/10.1007/s12205-017-1518-5   DOI
53 Fawaz, H.I., Forestier, G., Weber, J., Idoumghar, L. and Muller, P.A. (2018), "Evaluating surgical skills from kinematic data using convolutional neural networks", Proceedings of International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 214-21. https://doi.org/10.1007/978-3-030-00937-3_25   DOI
54 Fawaz, H.I., Forestier, G., Weber, J., Idoumghar, L. and Muller, P.A. (2019), "Deep learning for time series classifcation: a review", Data Min. Knowl. Discov., 33(4), 917-963. https://doi.org/10.1007/s10618-019-00619-1   DOI
55 Gao, M., Li, J., Hong, F. and Long, D. (2019), "Day-ahead power forecasting in a large-scale photovoltaic plant based on weather classification using LSTM", Energy, 187, 115838. https://doi.org/10.1016/j.energy.2019.07.168   DOI
56 Karim, F., Majumdar, S., Darabi, H. and Chen, S. (2017), "LSTM fully convolutional networks for time series classification", IEEE Access, 6, 1662-1669. https://doi.org/10.1109/ACCESS.2017.2779939   DOI
57 Brownjohn, J.M., De Stefano, A., Xu, Y.L., Wenzel, H. and Aktan, A.E. (2011), "Vibration-based monitoring of civil infrastructure: challenges and successes", J. Civil Struct. Health Monitor., 1(3-4), 79-95. https://doi.org/10.1007/s13349-011-0009-5   DOI
58 Guo, L., Li, N., Jia, F., Lei, Y. and Lin, J. (2017), "A recurrent neural network based health indicator for remaining useful life prediction of bearings", Neurocomputing, 240, 98-109. https://doi.org/10.1016/j.neucom.2017.02.045   DOI
59 He, K., Zhang, X., Ren, S. and Sun, J. (2015), "Delving deep into rectifiers: Surpassing human-level performance on imagenet classification", Proceedings of the IEEE International Conference on Computer Vision, pp. 1026-1034.
60 IPC-SHM (2020), http://www.schm.org.cn/#/IPC-SHM,2020
61 Karim, F., Majumdar, S. and Darabi, H. (2019a), "Insights into LSTM fully convolutional networks for time series classification", IEEE Access, 7, 67718-67725. https://doi.org/10.1109/ACCESS.2019.2916828   DOI