Smart Structures and Systems

Techno-Press (테크노프레스)
권호 표지
DOI QR코드

DOI QR Code

Damaged cable detection with statistical analysis, clustering, and deep learning models

  • Son, Hyesook (Department of Computer Engineering and Convergence Engineering for Intelligent Drone, Sejong University) ;
  • Yoon, Chanyoung (Department of Computer Engineering and Convergence Engineering for Intelligent Drone, Sejong University) ;
  • Kim, Yejin (Department of Computer Engineering and Convergence Engineering for Intelligent Drone, Sejong University) ;
  • Jang, Yun (Department of Computer Engineering and Convergence Engineering for Intelligent Drone, Sejong University) ;
  • Tran, Linh Viet (Department of Civil and Environmental Engineering, Sejong University) ;
  • Kim, Seung-Eock (Department of Civil and Environmental Engineering, Sejong University) ;
  • Kim, Dong Joo (Department of Civil and Environmental Engineering, Sejong University) ;
  • Park, Jongwoong (School of Civil and Environmental Engineering, Urban Design and Studies, Chung-Ang University)
  • Received : 2021.03.21
  • Accepted : 2021.10.04
  • Published : 2022.01.25
⟨ Previous Next ⟩

Abstract

The cable component of cable-stayed bridges is gradually impacted by weather conditions, vehicle loads, and material corrosion. The stayed cable is a critical load-carrying part that closely affects the operational stability of a cable-stayed bridge. Damaged cables might lead to the bridge collapse due to their tension capacity reduction. Thus, it is necessary to develop structural health monitoring (SHM) techniques that accurately identify damaged cables. In this work, a combinational identification method of three efficient techniques, including statistical analysis, clustering, and neural network models, is proposed to detect the damaged cable in a cable-stayed bridge. The measured dataset from the bridge was initially preprocessed to remove the outlier channels. Then, the theory and application of each technique for damage detection were introduced. In general, the statistical approach extracts the parameters representing the damage within time series, and the clustering approach identifies the outliers from the data signals as damaged members, while the deep learning approach uses the nonlinear data dependencies in SHM for the training model. The performance of these approaches in classifying the damaged cable was assessed, and the combinational identification method was obtained using the voting ensemble. Finally, the combination method was compared with an existing outlier detection algorithm, support vector machines (SVM). The results demonstrate that the proposed method is robust and provides higher accuracy for the damaged cable detection in the cable-stayed bridge.

Keywords

Acknowledgement

This work was supported in part by the Basic Research Program through the National Research Foundation of Korea (NRF) funded by the MSIT under Grant 2019R1A4A1021702, and in part by Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No.2021-0-02076, Developing Reasoning AI Engine in Complex Systems (REX) and its Applications).

References

  1. Alamdari, M.M., Rakotoarivelo, T. and Khoa, N.L.D. (2017), "A spectral-based clustering for structural health monitoring of the Sydney Harbour Bridge", Mech. Syst. Signal Process., 87, 384-400. https://doi.org/10.1016/j.ymssp.2016.10.033
  2. 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
  3. Braga, P.L., Oliveira, A.L., Ribeiro, G.H. and Meira, S.R. (2007), "Bagging predictors for estimation of software project effort", Proceedings of International Joint Conference on Neural Networks, pp. 1595-1600. https://doi.org/10.1109/IJCNN.2007.4371196
  4. Catbas, F.N., Gokce, H.B. and Gul, M. (2012), "Nonparametric analysis of structural health monitoring data for identification and localization of changes: Concept, lab, and real-life studies", Struct. Health Monitor., 11(5), 613-626. https://doi.org/10.1177/1475921712451955
  5. Chae, J., Thom, D., Bosch, H., Jang, Y., Maciejewski, R., Ebert, D. and Ertl, T. (2013), "Spatiotemporal social media analytics for abnormal event detection and examination using seasonal-trend decomposition", Proceedings of IEEE Conference on Visual Analytics Science and Technology (VAST), Seattle, WA, USA, October, pp. 143-152. https://doi.org/10.1109/VAST.2012.6400557
  6. Cleveland, R.B., Cleveland, W.S., McRae, J.E. and Terpenning, I. (1990), "STL: A seasonal-trend decomposition procedure based on loess", J. Official Statist., 6, 3-73.
  7. Diez, A., Khoa, N.L.D., Alamdari, M.M., Wang, Y., Chen, F. and Runcie, P. (2016), "A clustering approach for structural health monitoring on bridges", J. Civil Struct. Health Monitor., 6, 429-445. https://doi.org/10.1007/s13349-016-0160-0
  8. Duan, L., Xu, L., Liu, Y. and Lee, J. (2009), "Cluster-based outlier detection", Ann. Oper. Res., 168, 151-168. https://doi.org/10.1007/s10479-008-0371-9
  9. Entezami, A., Sarmadi, H., Behkamal, B. and Mariani, S. (2020), "Big data analytics and structural health monitoring: a statistical pattern recognition-based approach", Sensors, 20(8), p. 2328. https://doi.org/10.3390/s20082328
  10. Gu, J., Gul, M. and Wu, X. (2017), "Damage detection under varying temperature using artificial neural networks", Struct. Control Health Monitor., 24, e1998. https://doi.org/10.1002/stc.1998
  11. Hochreiter, S., and Schmidhuber, J. (1997), "Long short-term memory", Neural Comput., 9(8), 1735-1780. 10.1162/neco.1997.9.8.1735
  12. Jiang, S.Y. and An, Q.B. (2008), "Clustering-based outlier detection method", Proceedings of the 5th International Conference on Fuzzy Systems and Knowledge Discovery, Jinan, China, October, Volume 2, pp. 429-433. https://doi.org/10.1109/FSKD.2008.244
  13. Jo, H., Sim, S.H., Mechitov, K.A., Kim, R., Li, J., Moinzadeh, P., Spencer Jr, B.F., Park, J.W., Cho, S., Jung, H.J. and Yun, C.B. (2011), "Hybrid wireless smart sensor network for full-scale structural health monitoring of a cable-stayed bridge", Proceedings of Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2011, San Diego, CA, USA, March, Volume 7981, 798105. https://doi.org/10.1117/12.880513
  14. Kingma, D.P. and Ba, J. (2015), "Adam: A method for stochastic optimization", Proceedings of the 3rd International Conference on Learning Representations, San Diego, CA, USA.
  15. Lee, S. and Kim, H.K. (2018), "ADSaS: Comprehensive real-time anomaly detection system", Lecture Notes in Computer Science; In: International Workshop on Information Security Applications, pp. 29-41. https://doi.org/10.1007/978-3-030-17982-3_3
  16. 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
  17. Malhotra, P., Ramakrishnan, A., Anand, G., Vig, L., Agarwal, P. and Shroff, G. (2016), "LSTM-based encoder-decoder for multi-sensor anomaly detection", Proceedings of the ICML Anomaly Detection Workshop, New York, NY, USA.
  18. Mei, Q. and Gul, M. (2015), "Novel sensor clustering-based approach for simultaneous detection of stiffness and mass changes using output-only data", J. Struct. Eng., 141, p. 04014237. https://doi.org/10.1061/(ASCE)ST.1943-541X.0001218
  19. Nair, K.K., Kiremidjian, A.S. and Law, K.H. (2006), "Time series-based damage detection and localization algorithm with application to the ASCE benchmark structure", J. Sound Vib., 291, 349-368. https://doi.org/10.1016/j.jsv.2005.06.016
  20. Pamula, R., Deka, J.K. and Nandi, S. (2011), "An outlier detection method based on clustering", Proceedings of the 2nd International Conference on Emerging Applications of Information Technology, Kolkata, India, February, pp. 253-256. https://doi.org/10.1109/EAIT.2011.25
  21. Paszke, A., Gross, S., Chintala, S., Chanan, G., Yang, E., DeVito, Z., Lin, Z., Desmaison, A., Antiga, L. and Lerer, A. (2017), "Automatic differentiation in pytorch", In: NIPS 2017 Workshop Autodiff.
  22. Pathirage, C.S.N., Li, J., Li, L., Hao, H. and Liu, W. (2018), "Application of deep autoencoder model for structural condition monitoring", J. Syst. Eng. Electro., 29, 873-880. https://doi.org/10.21629/JSEE.2018.04.22
  23. Sutskever, I., Vinyals, O. and Le, Q.V. (2014), "Sequence to sequence learning with neural networks", In: Advances in Neural Information Processing Systems, pp. 3104-3112.
  24. Theodosiou, M. (2011), "Forecasting monthly and quarterly time series using STL decomposition", Int. J. Forecast., 27(4), 1178-1195. https://doi.org/10.1016/j.ijforecast.2010.11.002
  25. Tian, Y., Xu, Y., Zhang, D. and Li, H. (2020), "Relationship modeling between vehicle-induced girder vertical deflection and cable tension by BiLSTM using field monitoring data of a cable-stayed bridge", Struct. Control Health Monitor., 28(4), p. e2667. https://doi.org/10.1002/stc.2667
  26. Xingjian, S.H.I., Chen, Z., Wang, H., Yeung, D.Y., Wong, W.K. and Woo, W.C. (2015), "Convolutional LSTM network: A machine learning approach for precipitation nowcasting", In: Advances in Neural Information Processing Systems, pp. 802-810.
  27. Yu, L. and Lin, J.C. (2017), "Cloud computing-based time series analysis for structural damage detection", J. Eng. Mech., 143, C4015002. https://doi.org/10.1061/(ASCE)EM.1943-7889.0000982
  28. Zhang, Y., Meratnia, N. and Havinga, P. (2010), "Outlier detection techniques for wireless sensor networks: A survey", IEEE Commun. Surveys Tutorials, 2, 159-170. https://doi.org/10.1109/SURV.2010.021510.00088
  29. Zhang, C., Song, D., Chen, Y., Feng, X., Lumezanu, C., Cheng, W., Ni, J., Zong, B., Chen, H. and Chawla, N.V. (2019), "A deep neural network for unsupervised anomaly detection and diagnosis in multivariate time series data", Proceedings of the AAAI Conference on Artificial Intelligence, Honolulu, HI, USA, January-February, Volume 33, pp. 1409-1416. https://doi.org/10.1609/aaai.v33i01.33011409