Smart Structures and Systems

  • 제22권5호
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  • Pages.561-574
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  • 2018
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  • 1738-1584(pISSN)
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  • 1738-1991(eISSN)
테크노프레스 (Techno-Press)
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DOI QR코드

DOI QR Code

Optimization-based method for structural damage detection with consideration of uncertainties- a comparative study

  • Ghiasi, Ramin (Department of Civil Engineering, Faculty of Engineering, University of Sistan and Baluchestan) ;
  • Ghasemi, Mohammad Reza (Department of Civil Engineering, Faculty of Engineering, University of Sistan and Baluchestan)
  • 투고 : 2018.02.09
  • 심사 : 2018.10.30
  • 발행 : 2018.11.25
⟨ 이전 논문 다음 논문 ⟩

초록

In this paper, for efficiently reducing the computational cost of the model updating during the optimization process of damage detection, the structural response is evaluated using properly trained surrogate model. Furthermore, in practice uncertainties in the FE model parameters and modelling errors are inevitable. Hence, an efficient approach based on Monte Carlo simulation is proposed to take into account the effect of uncertainties in developing a surrogate model. The probability of damage existence (PDE) is calculated based on the probability density function of the existence of undamaged and damaged states. The current work builds a framework for Probability Based Damage Detection (PBDD) of structures based on the best combination of metaheuristic optimization algorithm and surrogate models. To reach this goal, three popular metamodeling techniques including Cascade Feed Forward Neural Network (CFNN), Least Square Support Vector Machines (LS-SVMs) and Kriging are constructed, trained and tested in order to inspect features and faults of each algorithm. Furthermore, three wellknown optimization algorithms including Ideal Gas Molecular Movement (IGMM), Particle Swarm Optimization (PSO) and Bat Algorithm (BA) are utilized and the comparative results are presented accordingly. Furthermore, efficient schemes are implemented on these algorithms to improve their performance in handling problems with a large number of variables. By considering various indices for measuring the accuracy and computational time of PBDD process, the results indicate that combination of LS-SVM surrogate model by IGMM optimization algorithm have better performance in predicting the of damage compared with other methods.

키워드

참고문헌

  1. Al-jarrah, O.Y., Yoo, P.D., Muhaidat, S. and Karagiannidis, G.K. (2015), "Efficient machine learning for big data : A review", Big Data Res., 2(3), 87-93. https://doi.org/10.1016/j.bdr.2015.04.001
  2. An, D., Kim, N.H. and Choi, J. (2015), "Practical options for selecting data-driven or physics-based prognostics algorithms with reviews", Reliab. Eng. Syst. Saf., 133, 223-236. https://doi.org/10.1016/j.ress.2014.09.014
  3. Bakhary, N., Hao, H. and Deeks, A.J. (2007), "Damage detection using artificial neural network with consideration of uncertainties", Eng. Struct., 29(11), 2806-2815. https://doi.org/10.1016/j.engstruct.2007.01.013
  4. Boller, C., Chang, F.K. and Fujino, Y. (2009), Encyclopedia of structural health monitoring. John Wiley & Sons.
  5. Cortes, C. and Vapnik, V. (1995), "Support-vector networks", Mach. Learn., 20(3), 273-297. https://doi.org/10.1007/BF00994018
  6. Dizangian, B. and Ghasemi, M.R. (2016), "A fast decoupled reliability-based design optimization of structures using Bspline interpolation curves", J. Braz. Soc. Mech. Sci. Eng., 38(6), 1817-1829. https://doi.org/10.1007/s40430-015-0423-4
  7. Dubourg, V. and Sudret, B. (2011), "Reliability-based design optimization using kriging surrogates and subset simulation", Struct. Multidiscip. O., 44(5), 673-690. https://doi.org/10.1007/s00158-011-0653-8
  8. Eberhart, R. and Kennedy, J. (1995), "A new optimizer using particle swarm theory", In Micro Machine and Human Science, 1995. MHS'95., Proceedings of the 6th International Symposium on (pp. 39-43). IEEE.
  9. Fan, W. and Qiao, P. (2011), "Vibration-based damage identification methods: a review and comparative study", Struct. Health. Monit., 10(1), 83-111. https://doi.org/10.1177/1475921710365419
  10. Farrar, C.R. and Worden, K. (2012), Structural health monitoring: a machine learning perspective, John Wiley & Sons.
  11. Fathnejat, H., Torkzadeh, P., Salajegheh, E. and Ghiasi, R. (2014), "Structural damage detection by model updating method based on cascade feed-forward neural network as an efficient approximation mechanism", Int. J. Optim. Civ. Eng., 4(4), 451-472.
  12. Ghasemi, M.R., Ghiasi, R. and Varaee, H. (2017a), "Probabilitybased damage detection of structures using Kriging surrogates and enhanced ideal gas molecular movement algorithm", Int. J. Mech. Aerosp. Ind. Mechatron. Manuf. Eng., 11, 628-636.
  13. Ghasemi, M.R., Ghiasi, R. and Varaee, H. (2017b), "Probability-based damage detection of structures using model updating with enhanced ideal gas molecular movement algorithm", Proceedings of the 19th International Conference on Reliability and Structural Safety (ICRSS 2017), London, United Kingdom (to be published).
  14. Ghasemi, M.R., Ghiasi, R. and Varaee, H. (2018), "Probabilitybased damage detection of structures using surrogate model and enhanced ideal gas molecular movement algorithm", (Eds., A. Schumacher, T. Vietor, S. Fiebig, K.U. Bletzinger and K. Maute), Advances in Structural and Multidisciplinary Optimization: Proceedings of the 12th World Congress of Structural and Multidisciplinary Optimization (WCSMO12) (pp. 1657-1674). Cham: Springer International Publishing.
  15. Ghasemi, M.R. and Varaee, H. (2017a), "A fast multi-objective optimization using an efficient ideal gas molecular movement algorithm", Eng. Comput., 33(3), 477-496. https://doi.org/10.1007/s00366-016-0485-7
  16. Ghasemi, M.R. and Varaee, H. (2017b), "Damping vibrationbased IGMM optimization algorithm: fast and significant", Soft Comput., 1-31.
  17. Ghiasi, R., Ghasemi, M.R. and Noori, M. (2018), "Comparative studies of metamodeling and AI-Based techniques in damage detection of structures", Adv. Eng. Softw., 125, 101-112. https://doi.org/10.1016/j.advengsoft.2018.02.006
  18. Ghiasi, R., Ghasemi, M.R. and Sohrabi, M.R. (2017), "Structural damage detection using frequency response function index and surrogate model based on optimized extreme learning machine algorithm", J. Comput. Method. Eng., 36(1), 1-17. https://doi.org/10.18869/acadpub.jcme.36.1.1
  19. Ghiasi, R., Torkzadeh, P. and Noori, M. (2014), "Structural damage detection using artificial neural networks and least square support vector machine with particle swarm harmony search algorithm", Int. J. Sustain. Mater. Struct. Syst., 1(4), 303-320. https://doi.org/10.1504/IJSMSS.2014.068798
  20. Ghiasi, R., Torkzadeh, P. and Noori, M. (2016), "A machinelearning approach for structural damage detection using least square support vector machine based on a new combinational kernel function", Struct. Health. Monit., 15(3), 302-316. https://doi.org/10.1177/1475921716639587
  21. Hakim, S.J.S. and Razak, H.A. (2014), "Modal parameters based structural damage detection using artificial neural networks-a review", Smart Struct. Syst., 14(2), 159-189. https://doi.org/10.12989/sss.2014.14.2.159
  22. Hao, H. and Xia, Y. (2002), "Vibration-based damage detection of structures by genetic algorithm", J. Comput. Civ. Eng., 16(3), 222-229. https://doi.org/10.1061/(ASCE)0887-3801(2002)16:3(222)
  23. Hedayat, A., Davilu, H., Abdollahzadeh, A. and Sepanloo, K. (2009), "Progress in nuclear energy estimation of research reactor core parameters using cascade feed forward artificial neural networks", Prog. Nucl. Energ., 51(6-7), 709-718. https://doi.org/10.1016/j.pnucene.2009.03.004
  24. Hua, X.G., Ni, Y.Q., Chen, Z.Q. and Ko, J.M. (2008), "An improved perturbation method for stochastic finite element model updating." Int. J. Numer. Meth. Eng., 73(13), 1845-1864. https://doi.org/10.1002/nme.2151
  25. Iman, R.L. (2008), Latin hypercube sampling. Wiley Online Library.
  26. Kaveh, A., Javadi, S.M. and Maniat, M. (2014), "Damage assessment via modal data with a mixed particle swarm strategy, ray optimizer, and harmony search", Asian J. Civ. Eng., 15(1), 95-106.
  27. Kaveh, A. and Talatahari, S. (2009), "Particle swarm optimizer , ant colony strategy and harmony search scheme hybridized for optimization of truss structures", Comput. Struct., 87(5-6), 267-283. https://doi.org/10.1016/j.compstruc.2009.01.003
  28. Kaveh, A. and Zolghadr, A. (2014), "Comparison of nine metaheuristic algorithms for optimal design of truss structures with frequency constraints", Adv. Eng. Softw., 76, 9-30. https://doi.org/10.1016/j.advengsoft.2014.05.012
  29. Kennedy, J. (2010), "Particle swarm optimization", In Encyclopedia of Machine Learning (pp. 760-766). Springer.
  30. Kottegoda, N.T. and Rosso, R. (1997), Probability, Statistics, and Reliability for Civil and Environmental Engineers, The McGraw-Hill Companies.
  31. Liu, Y., Ju, Y., Duan, C. and Zhao, X. (2011), "Structure damage diagnosis using neural network and feature fusion", Eng. Appl. Artif. Intel., 24(1), 87-92. https://doi.org/10.1016/j.engappai.2010.08.011
  32. Mahmoudi, S., Trivaudey, F. and Bouhaddi, N. (2016), "Benefits of metamodel-reduction for nonlinear dynamic response analysis of damaged composite structures", Finite Elem. Anal. Des., 119, 1-14. https://doi.org/10.1016/j.finel.2016.05.001
  33. Malekzadeh, M., Atia, G. and Catbas, F.N. (2015), "Performance-based structural health monitoring through an innovative hybrid data interpretation framework", J. Civ. Struct. Health. Monit., 5(3), 287-305. https://doi.org/10.1007/s13349-015-0118-7
  34. Malekzadeh, M. and Catbas, F.N. (2016), "A machine learning framework for automated functionality monitoring of movable bridges", In Dynamics of Civil Structures, Volume 2 (57-63). Springer.
  35. Mazzoni, S., McKenna, F., Scott, M.H. and Fenves, G.L. (2006), "OpenSees command language manual", Pacific Earthq. Eng. Res. Cent.
  36. Nobahari, M., Ghasemi, M.R. and Shabakhty, N. (2017a), "A novel heuristic search algorithm for optimization with application to structural damage identification", Smart Struct. Syst., 19(4), 449-461. https://doi.org/10.12989/sss.2017.19.4.449
  37. Nobahari, M., Ghasemi, M.R. and Shabakhty, N. (2017b), "Truss structure damage identification using residual force vector and genetic algorithm", Steel Compos. Struct., 25(4), 485-496. https://doi.org/10.12989/SCS.2017.25.4.485
  38. Padil, K.H., Bakhary, N. and Hao, H. (2017), "The use of a nonprobabilistic artificial neural network to consider uncertainties in vibration-based-damage detection", Mech. Syst. Signal Pr., 83, 194-209. https://doi.org/10.1016/j.ymssp.2016.06.007
  39. Papadopoulos, L. and Garcia, E. (1998), "Structural damage identification : A probabilistic approach", AIAA J., 36(11), 2137-2145. https://doi.org/10.2514/2.318
  40. Seyedpoor, S.M. (2012), "A two stage method for structural damage detection using a modal strain energy based index and particle swarm optimization", Int. J. Nonlinear. Mech., 47(1), 1-8.
  41. Shirazi, M.R.N., Mollamahmoudi, H. and Seyedpoor, S.M. (2013), "Structural damage identification using an adaptive multi-stage optimization method based on a modified particle swarm algorithm", J. Optimz. Theory Appl., 1-11.
  42. Simoen, E., Roeck, G.De and Lombaert, G. (2015), "Dealing with uncertainty in model updating for damage assessment : A review", Mech. Syst. Signal Pr., 56-57, 123-149. https://doi.org/10.1016/j.ymssp.2014.11.001
  43. Spiridonakos, M.D. and Chatzi, E.N. (2015), "Metamodeling of dynamic nonlinear structural systems through polynomial chaos NARX models", Comput. Struct., 157, 99-113. https://doi.org/10.1016/j.compstruc.2015.05.002
  44. Suykens, J.A.K. and Vandewalle, J. (1999), "Least squares support vector machine classifiers", Neural Process. Lett., 9(3), 293-300. https://doi.org/10.1023/A:1018628609742
  45. Torkzadeh, P., Fathnejat, H. and Ghiasi, R. (2016), "Damage detection of plate-like structures using intelligent surrogate model", Smart Struct. Syst., 18(6), 1233-1250. https://doi.org/10.12989/sss.2016.18.6.1233
  46. Torkzadeh, P., Goodarzi, Y., and Salajegheh, E, (2013). "A twostage damage detection method for large-scale structures by kinetic and modal strain energies using heuristic particle swarm optimization." Int. J. Optim. Civ. Eng., 3(3), 465-482.
  47. Varaee, H. and Ghasemi, M.R. (2017), "Engineering optimization based on ideal gas molecular movement algorithm", Eng. Comput., 33(1), 71-93. https://doi.org/10.1007/s00366-016-0457-y
  48. Wang, X.J., Zhou, X.Q., Xia, Y. and Weng, S. (2013), "Comparisons between modal-parameter-based and flexibility-based damage identification methods", Adv. Struct. Eng., 16(9), 1611-1619. https://doi.org/10.1260/1369-4332.16.9.1611
  49. Xia, Y., Hao, H., Brownjohn, J.M.W. and Xia, P. (2002), "Damage identification of structures with uncertain frequency and mode shape data", Earthq. Eng. Struct. D., 31(5), 1053-1066. https://doi.org/10.1002/eqe.137
  50. Xia, Y., Hao, H., Deeks, A.J. and Zhu, X. (2008), "Condition assessment of shear connectors in slab-girder bridges via vibration measurements", J. Bridg. Eng., 13(1), 43-54. https://doi.org/10.1061/(ASCE)1084-0702(2008)13:1(43)
  51. Xu, Q., Wehrle, E. and Baier, H. (2013), "Knowledge-based surrogate modeling in engineering design optimization", In Surrogate-Based Modeling and Optimization (313-336). Springer.
  52. Yang, X.S. and Gandomi, A.H. (2012), "Bat algorithm: a novel approach for global engineering optimization", Eng. Comput., 29(5), 464-483. https://doi.org/10.1108/02644401211235834