DOI QR코드

DOI QR Code

A novel WOA-based structural damage identification using weighted modal data and flexibility assurance criterion

  • Chen, Zexiang (MOE Key Laboratory of Disaster Forecast and Control in Engineering, School of Mechanics and Construction Engineering, Jinan University) ;
  • Yu, Ling (MOE Key Laboratory of Disaster Forecast and Control in Engineering, School of Mechanics and Construction Engineering, Jinan University)
  • Received : 2020.02.27
  • Accepted : 2020.06.03
  • Published : 2020.08.25

Abstract

Structural damage identification (SDI) is a crucial step in structural health monitoring. However, some of the existing SDI methods cannot provide enough identification accuracy and efficiency in practice. A novel whale optimization algorithm (WOA) based method is proposed for SDI by weighting modal data and flexibility assurance criterion in this study. At first, the SDI problem is mathematically converted into a constrained optimization problem. Unlike traditional objective function defined using frequencies and mode shapes, a new objective function on the SDI problem is formulated by weighting both modal data and flexibility assurance criterion. Then, the WOA method, due to its good performance of fast convergence and global searching ability, is adopted to provide an accurate solution to the SDI problem, different predator mechanisms are formulated and their probability thresholds are selected. Finally, the performance of the proposed method is assessed by numerical simulations on a simply-supported beam and a 31-bar truss structures. For the given multiple structural damage conditions under environmental noises, the WOA-based SDI method can effectively locate structural damages and accurately estimate severities of damages. Compared with other optimization methods, such as particle swarm optimization and dragonfly algorithm, the proposed WOA-based method outperforms in accuracy and efficiency, which can provide a more effective and potential tool for the SDI problem.

Keywords

Acknowledgement

This project is jointly supported by the National Natural Science Foundation of China with Grant Numbers 51678278 and 51278226.

References

  1. Alireza, G., Adel, A. and Armin, J. (2018), "Accelerated cuckoo optimization algorithm for capacitated vehicle routing problem in competitive conditions", J. Artifical Int., 16(1), 88-112.
  2. Arturo, G. and Omar, M. (2019), "Damage detection in bridges based on patterns of dynamic amplification", Struct. Control Health Monitor., 26(7), e2361. https://doi.org/10.1002/stc.2361.
  3. Cha, Y.J. (2015), "Structural damage detection using modal strain energy and hybrid multi-objective Optimization", J. Comput. Aid. Civil Infrastruct. Eng., 30, 347-358. https://doi.org/10.1111/mice.12122.
  4. Chang, C.C. and Chen, L.W. (2003), "Vibration damage detection of a Timoshenko beam by spatial wavelet based approach", Appl. Acousitcs, 64(12), 1217-1240. https://doi.org/10.1016/S0003-682X(03)00070-7.
  5. Chen, C.B. and Yu, L. (2020), "A hybrid ant lion optimizer with improved Nelder-Mead algorithm for structural damage detection by improving weighted trace lasso regularization", Adv. Struct. Eng., 23(3), 468-484. https://doi.org/10.1177/1369433219872434.
  6. Chen, Z.P. and Yu, L. (2017), "A novel PSO-based algorithm for structural damage detection using Bayesian multi-sample objective function", Struct. Eng. Mech., 63(6), 825-835. https://doi.org/10.12989/sem.2017.63.6.825.
  7. Chen, Z.P. and Yu, L. (2018), "A new structural damage detection strategy of hybrid PSO with Monte Carlo simulations and experimental verifications", Measurement, 122, 658-669. https://doi.org/10.1016/j.measurement.2018.01.068.
  8. Ding, Z.H., Huang, M. and Lu, Z.R. (2016), "Structural damage detection using artificial bee colony algorithm with hybrid search strategy", Swarm Evolution. Comput., 28, 1-13. https://doi.org/10.1016/j.swevo.2015.10.010.
  9. Ding, Z.H., Li, J. and Hao, H. (2019), "Structural damage identification using improved Jaya algorithm based on sparse regularization and Bayesian inference", Mech. Syst. Signal Process., 132, 211-231. https://doi.org/10.1016/j.ymssp.2019.06.029.
  10. Gao, Y.Q. and Khalid, M.M. (2018), "Deep transfer learning for image-based structural damage recognition", J. Comput. Aid. Civil Infrastruct. Eng., 33, 748-768. https://doi.org/10.1111/mice.12363.
  11. Ghannadi, P. and Kourehli, S.S. (2019), "Structural damage detection based on MAC flexibility and frequency using moth-flame algorithm", Struct. Eng. Mech., 70(6), 649-659. https://doi.org/10.12989/sem.2019.70.6.649.
  12. Guo, H.Y. (2017), "Structural multi-damage identification based on strain energy and micro-search artificial fish swarm algorithm", J. Vibroeng., 19(5), 3255-3270. https://doi.org/10.21595/jve.2017.17503.
  13. Hou, R.R., Xia, Y., Zhou, X.Q. and Huang, Y. (2019), "Sparse Bayesian learning for structural damage detection using expectation-maximization technique", Struct. Control Health Monitor., 26(5), e2343. https://doi.org/10.1002/stc.2343.
  14. Huang, J.L. and Lu, Z.R. (2017), "BB-BC optimization algorithm for structural damage detection using measured acceleration responses", Struct. Eng. Mech., 64(3), 353-360. https://doi.org/10.12989/sem.2017.64.3.353.
  15. Huang, M.S., Cheng, S.X., Zhang, H.Y., Gul, M. and Lu, H. (2019), "Structural Damage Identification under Temperature Variations Based on PSO-CS Hybrid Algorithm", J. Struct. Stability Dynam., 19(11), 1950139. https://doi.org/10.1142/S0219455419501396.
  16. Huang, M.S., Gul, M. and Zhu, H.P. (2018), "Vibration-Based Structural Damage Identification under Varying Temperature Effects", J. Aerosp. Eng., 31(3), 04018014. https://doi.org/10.1061/(ASCE)AS.1943-5525.0000829.
  17. Huang, M.S., Lei, Y.Z. and Li, X.F. (2019), "Structural Damage Identification based on L-1 Regularization and Bare Bones Particle Swarm Optimization with Double Jump Strategy", Math. Problems Eng., 5954104. https://doi.org/10.1155/2019/5954104.
  18. Kang, F., Li, J.J. and Xu, Q. (2012), "Damage detection based on improved particle swarm optimization using vibration data", Apply. Soft Comput., 12(8), 2329-2335. https://doi.org/10.1016/j.asoc.2012.03.050.
  19. Kao, C.Y. (2003), "Detection of structural damage via free vibration responses generated by approximating artificial neural networks", Comput. Struct., 81(28-29), 2631-2644. https://doi.org/10.1016/S0045-7949(03)00323-7.
  20. Yu, L. and Li, C. (2014), "A Global Artificial Fish Swarm Algorithm for Structural Damage Detection", Adv. Struct. Eng., 17(3), 331-346. https://doi.org/10.1260/1369-4332.17.3.331.
  21. Mafarja, M. and Mirjalili, S. (2018), "Whale optimization approaches for wrapper feature selection", Appl. Soft Comput., 62, 441-453. https://doi.org/10.1016/j.asoc.2017.11.006.
  22. Mirjalili, S. (2015), "Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm", Knowledge Based Syst., 89, 228-249. https://doi.org/10.1016/j.knosys.2015.07.006.
  23. Mirjalili, S. (2016), "Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems", Neural Comput. Appl., 27(4), 1053-1073. https://doi.org/10.1007/s00521-015-1920-1.
  24. Mirjalili, S. and Lewis, A. (2016), "The whale optimization algorithm", Adv. Eng. Software, 95, 51-67. https://doi.org/10.1016/j.advengsoft.2016.01.008.
  25. Mirjalili, S., Mirjalili, S.M. and Lewis, A. (2014), "Grey Wolf Optimizer", Adv. Eng. Software, 69, 46-61. https://doi.org/10.1016/j.advengsoft.2013.12.007.
  26. Oliv, D., El Aziz, M.A. and Hassanien, A.E. (2017), "Parameter estimation of photovoltaic cells using an improved chaotic whale optimization algorithm", Appl. Energy, 200, 141-154. https://doi.org/10.1016/j.apenergy.2017.05.029.
  27. Pan, C.D., Yu, L., Chen, Z.P., et al. (2016), "A hybrid self-adaptive Firefly-Nelder-Mead algorithm for structural damage detection", Smart Struct. Syst., 17(6), 957-980. https://doi.org/10.12989/sss.2016.17.6.957.
  28. Perera, R. and Torres, R. (2006), "Structural damage detection via modal data with genetic algorithms", J. Struct. Eng. ASCE, 132(9), 1491-1501. https://doi.org/10.1061/(ASCE)0733-9445(2006)132:9(1491).
  29. Precup, R.E., David, R.C. and Petriu, E.M. (2016), "Grey wolf optimizer-based approach to the tuning of PI-fuzzy controllers with a reduced process parametric sensitivity", IFAC-PapersOnLine, 49(5), 55-60. https://doi.org/10.1016/j.ifacol.2016.07.089.
  30. Seo, J., Hu, J.W. and Lee J. (2016), "Summary review of structural health monitoring applications for highway bridges", J. Performance Construct. Facilities, 30(4), 04015072. https://doi.org/10.1061/(ASCE)CF.1943-5509.0000824.
  31. Su, C., Liao, W., Tan, L. and Chen, R. (2016), "Reliability-based damage identification using dynamic signatures", J. Bridge Eng., 21(3), 04015058. https://doi.org/10.1061/(ASCE)BE.1943-5592.0000819.
  32. Xu, H.J., Liu, J.K. and Lv, Z.R. (2016), "Structural damage identification based on modified Cuckoo Search algorithm", Struct. Eng. Mech., 58(1), 163-179. https://doi.org/10.12989/sem.2016.58.1.163.
  33. Yu, L., Xu, P. (2011), "Structural health monitoring based on continuous ACO method", Microelectronics Reliability, 51(2), 270-278. https://doi.org/10.1016/j.microrel.2010.09.011.
  34. Yuen, K.V. (2010), "Efficient model correction method with modal measurement", J. Struct. Eng. ASCE, 136(1), 91-99. https://doi.org/10.1061/(ASCE)EM.1943-7889.0000068.
  35. Zheng, T.Y., Liu, J.K. and Luo, W.L. (2018), "Structural damage identification using cloud model based fruit fly optimization algorithm", Struct. Eng. Mech., 67(3), 245-254. https://doi.org/10.12989/sem.2018.67.3.245.
  36. Zhu, F.T. and Wu, Y.J. (2014), "A rapid structural damage detection method using integrated ANFIS and interval modeling technique", Appl. Soft Comput., 25, 473-484. https://doi.org/10.1016/j.asoc.2014.08.043.