Structural Engineering and Mechanics

  • 제77권4호
  • /
  • Pages.495-508
  • /
  • 2021
  • /
  • 1225-4568(pISSN)
  • /
  • 1598-6217(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Connection stiffness reduction analysis in steel bridge via deep CNN and modal experimental data

  • Dang, Hung V. (Faculty of Science and Technology, Middlesex University) ;
  • Raza, Mohsin (Faculty of Science and Technology, Middlesex University) ;
  • Tran-Ngoc, H. (Department of Bridge and Tunnel Engineering, Faculty of Civil Engineering, University of Transport and Communications) ;
  • Bui-Tien, T. (Department of Bridge and Tunnel Engineering, Faculty of Civil Engineering, University of Transport and Communications) ;
  • Nguyen, Huan X. (Faculty of Science and Technology, Middlesex University)
  • 투고 : 2019.10.17
  • 심사 : 2020.11.21
  • 발행 : 2021.02.25
⟨ 이전 논문 다음 논문 ⟩

초록

This study devises a novel approach, namely quadruple 1D convolutional neural network, for detecting connection stiffness reduction in steel truss bridge structure using experimental and numerical modal data. The method is developed based on expertise in two domains: firstly, in Structural Health Monitoring, the mode shapes and its high-order derivatives, including second, third, and fourth derivatives, are accurate indicators in assessing damages. Secondly, in the Machine Learning literature, the deep convolutional neural networks are able to extract relevant features from input data, then perform classification tasks with high accuracy and reduced time complexity. The efficacy and effectiveness of the present method are supported through an extensive case study with the railway Nam O bridge. It delivers highly accurate results in assessing damage localization and damage severity for single as well as multiple damage scenarios. In addition, the robustness of this method is tested with the presence of white noise reflecting unavoidable uncertainties in signal processing and modeling in reality. The proposed approach is able to provide stable results with data corrupted by noise up to 10%.

키워드

참고문헌

  1. 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", Neurocomput., 275, 1308-1317. https://doi.org/10.1016/j.neucom.2017.09.069.
  2. Adeli, H. (1988), Expert Systems in Construction and Structural Engineering, CRC Press.
  3. American Association of State Highway and Transportation Officials (2017), AASHTO LRFD Bridge Design Specifications.
  4. Avci, O., Abdeljaber, O., Kiranyaz, S., Hussein, M. and Inman, D.J. (2018), "Wireless and real-time structural damage detection: A novel decentralized method for wireless sensor networks", J. Sound Vib., 424, 158-172. https://doi.org/10.1016/j.jsv.2018.03.008.
  5. Bakhary, N., Hao, H. and Deeks, A.J. (2007), "Damage etection using artificial neural network with consideration of uncertainties", Eng. Struct., 29(11), 2806-2815. https://doi.org/10.1016/j.engstruct.2007.01.013.
  6. Benaissa, B., Koppen, M., Wahab, M.A. and Khatir, S. (2017), "Application of proper orthogonal decomposition and radial basis functions for crack size estimation using particle swarm optimization.", J. Phys. Conf. Ser., 842(1), 012014. https://doi.org/10.5755/j01.mech.21.6.12526.
  7. Boonlong, K. (2014), "Vibration-based damage detection in beams by cooperative coevolutionary genetic algorithm", Adv. Mech. Eng., 6, 624949. https://doi.org/10.1155/2014/624949.
  8. Brownlee, J. (2018), How to Develop 1D Convolutional Neural Network Models for Human Activity Recognition.
  9. Chen, J. and Young, B. (2005), "Stress-strain curves for stainless steel at elevated temperatures", Eng. Struct., 28(2), 229-239. https://doi.org/10.1016/j.engstruct.2005.07.005.
  10. Csebfalvi, A. (2007), "Optimal design of frame structures with semi-rigid joints", Period Polytech.-Civil, 51(1), 9-15. https://doi.org/10.3311/pp.ci.2007-1.02.
  11. Dackermann, U., Smith, W.A. and Randall, R.B. (2014), "Damage identification based on responseonly measurements using cepstrum analysis and artificial neural networks", Struct. Health Monit., 13(4), 430-444. https://doi.org/10.1177/1475921714542890.
  12. Ding. Z., Huang. M. and Lu. Z. (2016), "Structural damage detection using artificial bee colony algorithm with hybrid search strategy", Swarm Evol. Comput., 28, 1-13. https://doi.org/10.1016/j.swevo.2015.10.010.
  13. Dinh, T.D. (2015), Nam O Railroad Bridge.
  14. Elshafey, A.A., Haddara, M.R. and Marzouk, H. (2010), "Damage detection in offshore structures using neural networks", Marine Struct., 23(1), 131-145. https://doi.org/10.1016/j.marstruc.2010.01.005.
  15. Fredenslund, K. (2018), Computational Complexity of Neural Networks.
  16. Hibbitt, H., Karlsson, B. and Sorensen C. (2016), Abaqus Analysis User's Manual Version, Dassault Systemes Simulia Corp.
  17. Hua, X., Ni, Y., Ko, J. and Wong, K. (2007), "Modeling of temperature-frequency correlation using combined principal component analysis and support vector regression technique", J. Comput. Civil Eng., 21(2), 122-135. https://doi.org/10.1061/(ASCE)0887-3801(2007)21:2(122).
  18. Kayser, J.R. and Nowak, A.S. (1989), "Capacity loss due to corrosion in steel-girder bridges", J. Struct. Eng., 115(6), 1525-1537. https://doi.org/10.1061/(ASCE)0733-9445(1989)115:6(1525).
  19. Khatir, S. and Wahab, M.A., (2019), "Fast simulations for solving fracture mechanics inverse problems using POD-RBF XIGA and Jaya algorithm", Eng. Fract. Mech., 205, 285-300. https://doi.org/10.1016/j.engfracmech.2018.09.032.
  20. Khatir, S., Belaidi, I., Khatir, T., Hamrani, A., Zhou, Y.L. and Wahab, M.A. (2017), "Multiple damage detection in composite beams using Particle Swarm Optimization and Genetic Algorithm", Mechanika, 23(4), 514-521. https://doi.org/10.5755/j01.mech.23.4.15254.
  21. Khatir, S., Belaidi, I., Serra, R., Wahab, M.A. and Khatir, T. (2015), "Damage detection and localization in composite beam structures based on vibration analysis", Mech., 21(6), 472-479. https://doi.org/10.5755/j01.mech.21.6.12526.
  22. Khatir, S., Dekemele, K., Loccufier, M., Khatir, T. and Wahab, M. A. (2018), "Crack identification method in beam-like structures using changes in experimentally measured frequencies and Particle Swarm Optimization", CR Mecanique, 346(2), 110-120. https://doi.org/10.1016/j.crme.2017.11.008.
  23. Khatir, S., Tiachacht, S., Thanh, C.L., Bui, T.Q. and Wahab, M.A. (2019), "Damage assessment in composite laminates using ANN-PSO-IGA and Cornwell indicator", Compos. Struct., 230, 111509. https://doi.org/10.1016/j.compstruct.2019.111509.
  24. Li, J., Dackermann, U., Xu, Y.L. and Samali, B. (2011), "Damage identification in civil engineering structures utilizing PCA-compressed residual frequency response functions and neural network ensembles", Struct. Control. Hlth. Monit., 18(2), 207-226. https://doi.org/10.1002/stc.369.
  25. Lin, Y.Z., Nie, Z.H. and Ma, H.W. (2017), "Structural damage detection with automatic feature extraction through deep learning", Comput. Aid. Civil Inf., 32(12), 1025-1046. https://doi.org/10.1111/mice.12313.
  26. Luong, H.T., Zabel, V., Lorenz, W. and Rohrmann, R.G. (2017), "Vibration-based model updating and identification of multiple axial forces in truss structures", Procedia Eng., 188, 385-392. https://doi.org/10.1016/j.proeng.2017.04.499.
  27. Magalhaes, F. and Cunha, A. (2011), "Explaining operational modal analysis with data from an arch bridge", Mech. Syst. Signal Pr., 25(5), 1431-1450. https://doi.org/10.1016/j.ymssp.2010.08.001.
  28. Masri, S., Nakamura, M., Chassiakos, A. and Caughey, T. (1996), "Neural network approach to detection of changes in structural parameters", J. Eng. Mech., 122(4), 350-360. https://doi.org/10.1061/(ASCE)0733-9399(1996)122:4(350).
  29. Nanda, B., Maity, D. and Maiti, D.K. (2014), "Modal parameter based inverse approach for structural joint damage assessment using unified particle swarm optimization", Appl. Math. Comput., 242, 407-422. https://doi.org/10.1016/j.amc.2014.05.115.
  30. Nguyen, D.H., Bui, T.T., De Roeck, G. and Wahab, M.A. (2019), "Damage detection in Ca-Non Bridge using transmissibility and artificial neural networks", Struct. Eng. Mech., 71(2), 175-183. https://doi.org/10.12989/sem.2019.71.2.175.
  31. Park, C. and Nowak, A. (1998), "Time-varying reliability model of steel girder bridges", Structural Reliability in Bridge Engineering: Design, Inspection, Assessment, Rehabilitation and Management. Proceedings of the Workshop National Science Foundation, Federal Highway Administration.
  32. Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E. (2015), "Scikit-learn: Machine learning in Python", J. Mach. Learn Res., 12, 2825-2830. https://doi: 10.1016/j.patcog.2011.04.006.
  33. Reynders, E., Houbrechts, J. and De Roeck, G. (2012), "Fully automated (operational) modal analysis", Mech. Syst. Signal Pr., 29(4), 228-250. https://doi.org/10.1016/j.ymssp.2012.01.007.
  34. Reynders, E., Pintelon, R. and De Roeck, G. (2008), "Uncertainty bounds on modal parameters obtained from stochastic subspace identification", Mech. Syst. Signal Pr., 22(4), 948-969. https://doi.org/10.1016/j.ymssp.2007.10.009.
  35. Samali, B., Dackermann, U. and Li, J. (2012), "Location and severity identification of notch-type damage in a two-storey steel framed structure utilising frequency response functions and artificial neural network", Adv. Struct. Eng., 15(5), 743-757. https://doi.org/10.1260/1369-4332.15.5.743.
  36. Samir, K., Brahim, B., Capozucca, R. and Wahab, M.A. (2018), "Damage detection in CFRP composite beams based on vibration analysis using proper orthogonal decomposition method with radial basis functions and cuckoo search algorithm", Compos. Struct., 187, 344-353. https://doi.org/10.1016/j.compstruct.2017.12.058.
  37. Santos, A., Figueiredo, E., Silva, M., Sales, C. and Costa, J. (2016), "Machine learning algorithms for damage detection: Kernel-based approaches", J. Sound Vib., 363, 584-599. https://doi.org/10.1016/j.jsv.2015.11.008.
  38. Sun, M., Makki Alamdari, M. and Kalhori, H. (2016), "Automated operational modal analysis of a cable-stayed bridge", J. Sound Vib., 22(12), 505017012. https://doi.org/10.1061/(ASCE)BE.1943-5592.0001141.
  39. Tiachacht, S., Bouazzouni, A., Khatir, S., Wahab, M.A., Behtani, A. and Capozucca, R. (2018), "Damage assessment in structures using combination of a modified Cornwell indicator and genetic algorithm", Eng. Struct., 177, 421-430. https://doi.org/10.1016/j.engstruct.2018.09.070.
  40. Tran-Ngoc, H., Khatir, S., De Roeck, G., Bui-Tien, T., Nguyen-Ngoc, L. and Wahab, M.A. (2018), "Model updating for Nam O Bridge using Particle Swarm Optimization algorithm and Genetic algorithm", Sensor., 18(12), 4131. https://doi.org/10.3390/s18124131.
  41. Vafaei, M. and Alih, S. (2018), "Adequacy of first mode shape differences for damage identification of cantilever structures using neural networks", Neural. Comput. Appl., 30(8), 2509-2518. https://doi.org/10.1007/s00521-017-2846-6.
  42. Whalen, T.M. (2008), "The behavior of higher order mode shape derivatives in damaged, beam-like structures", J. Sound Vib., 309(3), 426-464. https://doi.org/10.1016/j.jsv.2007.07.054.
  43. Xiong, C., Lu, H. and Zhu, J. (2017), "Operational modal analysis of bridge structures with data from GNSS/accelerometer measurements", Sensor., 17(3), 436. https://doi.org/10.3390/s17030436.
  44. Xu, B., Wu, Z., Chen, G. and Yokoyama, K. (2004), "Direct identification of structural parameters from dynamic responses with neural networks", Eng. Appl. Artif. Intell., 17(8), 931-943. https://doi.org/10.1016/j.engappai.2004.08.010.
  45. Yan, A.M., De Boe, P. and Golinval, J.C. (2003), "Structural integral monitoring by vibration measurements", Structural Integrity Materials Aging, 363-370.
  46. Yang, X. and Chen, X. (2019), "Test verification of damage identification method based on statistical properties of structural dynamic displacement", J. Civil Struct. Hlth. Monit., 9(2), 263-269. https://doi.org/10.1007/s13349-019-00331-0.
  47. Yeung, W. and Smith, J. (2005), "Damage detection in bridges using neural networks for pattern recognition of vibration signatures", Eng. Struct., 27(5), 685-698. https://doi.org/10.1016/j.engstruct.2004.12.006.
  48. Yuen, K.V. and Lam, H.F. (2006), "On the complexity of artificial neural networks for smart structures monitoring", Eng. Struct., 28(7), 977-984. https://doi.org/10.1016/j.engstruct.2005.11.002.
  49. Yun, C.B., Yi, J.H. and Bahng, E.Y. (2001), "Joint damage assessment of framed structures using a neural networks technique", Eng. Struct., 23(5), 425-435. https://doi.org/10.1016/S0141-0296(00)00067-5.
  50. Zenzen, R., Belaidi, I., Khatir, S. and Wahab, M.A. (2018), "A damage identification technique for beam-like and truss structures based on FRF and Bat Algorithm", CR Mecanique, 346(12), 1253-1266. https://doi.org/10.1016/j.crme.2018.09.003.
  51. Zhang, D., Qian, L., Mao, B., Huang, C., Huang B. and Si, Y. (2018), "A data-driven design for fault detection of wind turbines using random forests and XGboost", IEEE Access, 6, 21020-21031. https://doi.org/10.1109/access.2018.2818678.
  52. Zhang, Y., Miyamori, Y., Mikami, S. and Saito, T. (2019), "Vibration-based structural state identification by a 1-dimensional convolutional neural network", Comput. Aid. Civil Inf., 34(9), 822-839. https://doi.org/10.1111/mice.12447.
  53. Zhou, Q., Ning, Y., Zhou, Q., Luo, L. and Lei, J. (2013), "Structural damage detection method based on random forests and data fusion", Struct. Heth. Monit., 12(1), 48-58. https://doi.org/10.1177/1475921712464572.

피인용 문헌

  1. Damage detection in structures using Particle Swarm Optimization combined with Artificial Neural Network vol.28, pp.1, 2021, https://doi.org/10.12989/sss.2021.28.1.001