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Bayesian ballast damage detection utilizing a modified evolutionary algorithm

  • Hu, Qin (School of Civil Engineering and Mechanics, Huazhong University of Science and Technology) ;
  • Lam, Heung Fai (Department of Architecture and Civil Engineering, City University of Hong Kong) ;
  • Zhu, Hong Ping (School of Civil Engineering and Mechanics, Huazhong University of Science and Technology) ;
  • Alabi, Stephen Adeyemi (Department of Architecture and Civil Engineering, City University of Hong Kong)
  • 투고 : 2017.06.03
  • 심사 : 2018.03.01
  • 발행 : 2018.04.25

초록

This paper reports the development of a theoretically rigorous method for permanent way engineers to assess the condition of railway ballast under a concrete sleeper with the potential to be extended to a smart system for long-term health monitoring of railway ballast. Owing to the uncertainties induced by the problems of modeling error and measurement noise, the Bayesian approach was followed in the development. After the selection of the most plausible model class for describing the damage status of the rail-sleeper-ballast system, Bayesian model updating is adopted to calculate the posterior PDF of the ballast stiffness at various regions under the sleeper. An obvious drop in ballast stiffness at a region under the sleeper is an evidence of ballast damage. In model updating, the model that can minimize the discrepancy between the measured and model-predicted modal parameters can be considered as the most probable model for calculating the posterior PDF under the Bayesian framework. To address the problems of non-uniqueness and local minima in the model updating process, a two-stage hybrid optimization method was developed. The modified evolutionary algorithm was developed in the first stage to identify the important regions in the parameter space and resulting in a set of initial trials for deterministic optimization to locate all most probable models in the second stage. The proposed methodology was numerically and experimentally verified. Using the identified model, a series of comprehensive numerical case studies was carried out to investigate the effects of data quantity and quality on the results of ballast damage detection. Difficulties to be overcome before the proposed method can be extended to a long-term ballast monitoring system are discussed in the conclusion.

키워드

과제정보

연구 과제 주관 기관 : Council of the Hong Kong Special Administrative Region, National Nature Science Foundation of China (NSFC), HUST

참고문헌

  1. An, D., Choi, J.H. and Kim, N.H. (2011), "Identification of correlated damage parameters under noise and bias using Bayesian inference", Struct. Health Monit., 11(3), 293-303.
  2. Au, S.K. (2011), "Fast Bayesian FFT method for ambient modal identification with separated modes", J. Eng. Mech., 137(3), 214-226. https://doi.org/10.1061/(ASCE)EM.1943-7889.0000213
  3. Back, T. (1996), Evolutionary Algorithms in Theory and Practice: Evolution Strategies, Evolutionary Programming, Genetic Algorithms, Oxford university press, 1st Ed., New York, USA.
  4. Beck, J.L. (1978), "Determining models of structures from earthquake records", Ph.D. Dissertation, California Institution of Technology, USA.
  5. Beck, J.L. and Katafygiotis, L.S. (1998), "Updating models and their uncertainties I: Bayesian statistical framework", J. Eng. Mech. - ASCE, 124(4), 455-461. https://doi.org/10.1061/(ASCE)0733-9399(1998)124:4(455)
  6. Beck, J.L. and Yuen, K.V. (2004), "Model selection using response measurement: Bayesian probabilistic approach", J. Eng. Mech. - ASCE, 130(2), 192-203. https://doi.org/10.1061/(ASCE)0733-9399(2004)130:2(192)
  7. Berggren, E. (2009), "Railway track stiffness-dynamic measurements and evaluation for efficient maintenance", Ph.D. Dissertation, Royal Institute of Technology (KTH), Sweden.
  8. Box, G.E.P. and Tiao, G.C. (1973), Bayesian inference in statistical analysis, Addision-Wesley, Reading, Mass.
  9. Cao, Z.J. and Wang, Y. (2014), "Bayesian model comparison and selection of spatial correlation functions for soil parameters", Struct. Saf., 49, 10-17. https://doi.org/10.1016/j.strusafe.2013.06.003
  10. Fujino, Y., Siringoringo, D.M. and Abe, M. (2009), "The needs for advanced sensor technologies in risk assessment of civil infrastructures", Smart Struct. Syst., 5(5), 173-191. https://doi.org/10.12989/sss.2009.5.2.173
  11. Fan, W. and Qiao, P.Z. (2011), "Vibration-based damage identification methods: a review and comparative study", Struct. Health Monit., 10(1), 83-111. https://doi.org/10.1177/1475921710365419
  12. Hu, Q. (2015), "Bayesian ballast damage detection in consideration of uncertainties from measurement noise and modelling error", Ph.D. Dissertation, City University of Hong Kong, Hong Kong.
  13. Hu, Q. and Lam, H.F. (2012), "Model updating of the rail-sleeper-ballast system and its application in ballast damage detection". Proceedings of the 7th Australasian Congress on Applied Mechanics (ACAM 7), Adelaide, Australia, 9-12 December.
  14. Hu, Q., Lam, H.F. and Alabi, S.A. (2015), "Use of measured vibration of in-situ sleeper for detecting underlying railway ballast damage", Int. J. Struct. Stab. Dynam., 15(8), 1540026(1-14).
  15. Jeffreys, H. (1961), Theory of probability. 3rd ed. Clarendon, Oxford, U.K..
  16. Jo, H.K., Park, J.W., Spencer, B.F. and Jung, H.J. (2013), "Development of high-sensitivity wireless strain sensor for structural health monitoring", Smart Struct. Syst., 11(5), 477-496. https://doi.org/10.12989/sss.2013.11.5.477
  17. Kaewunruen, S. and Remennikov, A.M. (2009), "Application of vibration measurements and finite element model updating for structural health monitoring of ballasted railtrack sleepers with voids and pockets", Faculty of Engineering-Papers.
  18. Krenk, S. (2001), Mechanics and Analysis of Beams, Columns and Cables: a Modern Introduction to the Classic Theories. Springer Science & Business Media.
  19. Kuok, S.C. and Yuen, K.V. (2012), "Structural health monitoring of Canton Tower using Bayesian framework", Smart Struct. Syst., 10(4-5), 375-391. https://doi.org/10.12989/sss.2012.10.4_5.375
  20. Lam, H.F., Hu, Q. and Wong, M.T. (2014), "The Bayesian methodology for the detection of railway ballast damage under a concrete sleeper", Eng. Struct., 81, 289-301. https://doi.org/10.1016/j.engstruct.2014.08.035
  21. Lam, H.F., Ng, C.T. and Leung, A.Y.T. (2008), "Multicrack detection on semirigidly connected beams utilizing dynamic data", J. Eng. Mech., 134(1), 90-99. https://doi.org/10.1061/(ASCE)0733-9399(2008)134:1(90)
  22. Lam, H.F., Ng, C.T. and Veidt, M. (2007), "Experimental characterization of multiple cracks in a cantilever beam utilizing transient vibration data following a probabilistic approach", J. Sound Vib., 305(1-2), 34-49. https://doi.org/10.1016/j.jsv.2007.03.028
  23. Lam, H.F., Wong, M.T. and Keefe, R.M. (2010), "Detection of ballast damage by in-situ vibration measurement of sleepers", Proceedings of the 2nd International Symposium on Computational Mechanics and 12th International Conference on Enhancement and Promotion of Computational methods in Engineering and Science, 1233(1), 1648-1653.
  24. AIP Publishing. Lam, H.F., Wong, M.T. and Yang, Y.B. (2012), "A feasibility study on railway ballast damage detection utilizing measured vibration of in situ concrete sleeper", Eng. Struct., 45, 284-298. https://doi.org/10.1016/j.engstruct.2012.06.022
  25. Lam, H.F. and Yin, T. (2010), "Statistical detection of multiple cracks on thin plates utilizing dynamic response", Eng. Struct., 32(10), 3145-3152. https://doi.org/10.1016/j.engstruct.2010.06.002
  26. Muto, M. and Beck, J.L. (2008), "Bayesian updating and model class selection for hysteretic structural models using stochastic simulation", J. Vib. Control, 14(1-2), 7-34. https://doi.org/10.1177/1077546307079400
  27. Meyer, J., Bischoff, R., Feltrin, G. and Motavalli, M. (2010), "Wireless sensor networks for long-term structural health monitoring", Smart Struct. Syst., 6(3), 263-275. https://doi.org/10.12989/sss.2010.6.3.263
  28. Ni, Y.C., Zhang, F.L., Lam, H.F. and Au, S.K. (2015), "Fast Bayesian approach for modal identification using free vibration data, Pat II-Posterior uncertainty and application", Mech. Syst. Signal. Pr., 70-71, 221-244.
  29. Ni, Y.Q., Li, B., Lam, K.H., Zhu, D.P., Wang, Y., Lynch, J.P. and Law, K.H. (2011), "In-construction vibration monitoring of a super-tall structure using a long-range wireless sensing system", Smart Struct. Syst., 7(2), 83-102. https://doi.org/10.12989/sss.2011.7.2.083
  30. Ni, Y.Q., Xia, Y., Lin, W., Chen, W.H. and Ko, J.M. (2012), "SHM benchmark for high-rise structures: a reduced-order finite element model and field measurement data", Smart Struct. Syst., 10 (4-5), 411-426. https://doi.org/10.12989/sss.2012.10.4_5.411
  31. Nagayama, T., Sim, S.H., Miyamori, Y. and Spencer, B.F. (2007), "Issues in structural health monitoring employing smart sensors", Smart Struct. Syst., 3(3), 299-320. https://doi.org/10.12989/sss.2007.3.3.299
  32. Papadimitriou, C., Beck, J.L. and Katafygiotis, L.S. (1997), "Asymptotic expansions for reliability and moments of uncertain systems", J. Eng. Mech., 123(12), 1219-1229. https://doi.org/10.1061/(ASCE)0733-9399(1997)123:12(1219)
  33. Przemieniecki, J.S. (1985), Theory of Matrix Structural Analysis. Courier Corporation.
  34. Selig, E.T. and Waters, J.M. (1994), Track Geotechnology and Substructure Management. Thomas Telford.
  35. Salawu, O.S. (1997), "Detection of structural damage through changes in frequency: a review", Eng. Struct., 19(9), 718-723. https://doi.org/10.1016/S0141-0296(96)00149-6
  36. Vanik, M.W., Beck, J.L. and Au, S.K. (2000), "Bayesian probabilistic approach to structural health monitoring", J. Eng. Mech., 126(7), 738-745. https://doi.org/10.1061/(ASCE)0733-9399(2000)126:7(738)
  37. Worden, K. and Hensman, J.J. (2012), "Parameter estimation and model selection for a class of hysteretic systems using Bayesian inference", Mech. Syst. Signal. Pr., 32, 153-169. https://doi.org/10.1016/j.ymssp.2012.03.019
  38. Xia, Y., Chen, B. and Weng, S., Ni, Y.Q. and Xu, Y.L. (2012), "Temperature effect on vibration properties of civil structures: a literature review and case studies", Civ. Struct. Health Monit., 2(1), 29-46. https://doi.org/10.1007/s13349-011-0015-7
  39. Xia, Y., Hao, H., Zanardo, G. and Deeks, A. (2006), "Long term vibration monitoring of an RC slab: temperature and humidity effect", Eng. Struct., 28(3), 441-452. https://doi.org/10.1016/j.engstruct.2005.09.001
  40. 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
  41. Yuen, K.V. (2010), Bayesian methods for structural dynamics and civil engineering. John Wiley& Sons, New York.
  42. Zhang, F.L., Ni, Y.C. and Au, S.K. and Lam, H.F.(2015), "Fast Bayesian approach for modal identification using free vibration data, Part I - Most probable value", Mech. Syst. Signal. Pr., 70-71, 209-220.