Advances in Computational Design

  • 제1권4호
  • /
  • Pages.315-327
  • /
  • 2016
  • /
  • 2383-8477(pISSN)
  • /
  • 2466-0523(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Structural health monitoring through meta-heuristics - comparative performance study

  • Pholdee, Nantiwat (Sustainable and Infrastructure Research and Development Center, Department of Mechanical Engineering, Faculty of Engineering, Khon Kaen University) ;
  • Bureerat, Sujin (Sustainable and Infrastructure Research and Development Center, Department of Mechanical Engineering, Faculty of Engineering, Khon Kaen University)
  • 투고 : 2016.06.09
  • 심사 : 2016.08.18
  • 발행 : 2016.10.25
⟨ 이전 논문 다음 논문 ⟩

초록

Damage detection and localisation in structures is essential since it can be a means for preventive maintenance of those structures under service conditions. The use of structural modal data for detecting the damage is one of the most efficient methods. This paper presents comparative performance of various state-of-the-art meta-heuristics for use in structural damage detection based on changes in modal data. The metaheuristics include differential evolution (DE), artificial bee colony algorithm (ABC), real-code ant colony optimisation (ACOR), charged system search (ChSS), league championship algorithm (LCA), simulated annealing (SA), particle swarm optimisation (PSO), evolution strategies (ES), teaching-learning-based optimisation (TLBO), adaptive differential evolution (JADE), evolution strategy with covariance matrix adaptation (CMAES), success-history based adaptive differential evolution (SHADE) and SHADE with linear population size reduction (L-SHADE). Three truss structures are used to pose several test problems for structural damage detection. The meta-heuristics are then used to solve the test problems treated as optimisation problems. Comparative performance is carried out where the statistically best algorithms are identified.

키워드

과제정보

연구 과제 주관 기관 : Thailand Research Fund (TRF)

참고문헌

  1. Abdeljaber, O. and Avci, O. (2016), "Nonparametric structural damage detection algorithm for ambient vibration response: utilizing artificial neural networks and self-organizing maps", J. Architec. Eng., 22(2), 04016004. https://doi.org/10.1061/(ASCE)AE.1943-5568.0000205
  2. Agarwalla, D.K., Dash, A.K., Bhuyan, S.K. and Nayak, P.S.K. (2015), "Damage detection of fixed-fixed beam: a fuzzy neuro hybrid system based approach", Proceedings of the 5th International Conference on Swarm, Evolutionary, and Memetic Computing, SEMCCO 2014, Bhubaneswar, India, December 18-20.
  3. Alavi, A.H., Hasni, H., Lajnef, N., Chatti, K. and Faridazar, F. (2016), "An intelligent structural damage detection approach based on self-powered wireless sensor data", Auto. Constr., 62, 24-44. https://doi.org/10.1016/j.autcon.2015.10.001
  4. Back, T. (1996), Evolutionary Algorithms in Theory and Practice, Oxford: Oxford University Press.
  5. Buchholz, M., Pecheur, G., Niemeyer, J. and Krebs, V. (2007), "Fault detection and isolation for PEM fuel cell stacks using fuzzy cluster", Proceedings of the Control Conference (ECC), 2007 European.
  6. Bureerat, S. and Limtragool, J. (2008), "Structural topology optimisation using simulated annealing with multiresolution design variables", Finite Elements Anal. Des., 44(12-13), 738-747. https://doi.org/10.1016/j.finel.2008.04.002
  7. Chen, B. and Zang, C. (2009), "Artificial immune pattern recognition for structure damage classification", Comput. Struct., 87(21-22), 1394-1407. https://doi.org/10.1016/j.compstruc.2009.08.012
  8. Chou, J.-H. and Ghaboussi, J. (2001), "Genetic algorithm in structural damage detection", Comput. Struct., 79(14), 1335-1353. https://doi.org/10.1016/S0045-7949(01)00027-X
  9. Ding, Z.H., Huang, M. and Lu, Z.R. (2016), "Structural damage detection using artificial bee colony algorithm with hybrid search strategy", Swarm Evolut. Comput., 28, 1-13. https://doi.org/10.1016/j.swevo.2015.10.010
  10. Fu, Y.M. and Yu, L. (2014), "A DE-based algorithm for structural damage detection", Adv. Mater. Res., 919, 303-307.
  11. Hansen, N., Muller, S.D. and Koumoutsakos, P. (2003), "Reducing the time complexity of the derandomized evolution strategy with covariance matrix adaptation (CMA-ES)", Evol. Comput., 11(1), 1-18. https://doi.org/10.1162/106365603321828970
  12. Hearn, G. and Testa, R.B. (1991), "Modal analysis for damage detection in structures", J. Struct. Eng., 117(10), 3042-3063. https://doi.org/10.1061/(ASCE)0733-9445(1991)117:10(3042)
  13. Husseinzadeh Kashan, A. (2011), "An efficient algorithm for constrained global optimization and application to mechanical engineering design: League Championship Algorithm (LCA)", Comput. Aid. Des., 43(12), 1769-1792. https://doi.org/10.1016/j.cad.2011.07.003
  14. Jafarkhani, R. and Masri, S.F. (2011), "Finite element model updating using evolutionary strategy for damage detection", Comput. Aid. Civ. Infrastruct. Eng., 26(3), 207-224. https://doi.org/10.1111/j.1467-8667.2010.00687.x
  15. Jiao, Y.-B., Liu, H.-B., Cheng, Y.-C. and Gong, Y.-F. (2015), "Damage identification of bridge based on chebyshev polynomial fitting and fuzzy logic without considering baseline model parameters", Shock Vib., 2015, 187956.
  16. Karaboga, D. and Basturk, B. (2007), "A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm", J. Global Optim., 39(3), 459-471. https://doi.org/10.1007/s10898-007-9149-x
  17. Kaveh, A. and Talatahari, S. (2010), "A novel heuristic optimization method: charged system search", Acta Mech., 213(3), 267-289. https://doi.org/10.1007/s00707-009-0270-4
  18. Kaveh, A. and Zolghadr, A. (2015), "An improved CSS for damage detection of truss structures using changes in natural frequencies and mode shapes", Adv. Eng. Softw., 80, 93-100. https://doi.org/10.1016/j.advengsoft.2014.09.010
  19. Koh, B.H. and Dyke, S.J. (2007), "Structural health monitoring for flexible bridge structures using correlation and sensitivity of modal data", Comput. Struct., 85(3-4), 117-130. https://doi.org/10.1016/j.compstruc.2006.09.005
  20. Lifshitz, J.M. and Rotem, A. (1969), "Determination of Reinforcement Unbonding of Composites by a Vibration Technique", J. Compos. Mater., 3(3), 412-423. https://doi.org/10.1177/002199836900300305
  21. Majumdar, A., Maiti, D.K. and Maity, D. (2012), "Damage assessment of truss structures from changes in natural frequencies using ant colony optimization", Appl. Math. Comput., 218(19), 9759-9772.
  22. Messina, A., Williams, E.J. and Contursi, T. (1998), "Structural damage detection by a sensitivity and statistical-based method", J. Sound Vib., 216(5), 791-808. https://doi.org/10.1006/jsvi.1998.1728
  23. Pal, J. and Banerjee, S. (2015), "A combined modal strain energy and particle swarm optimization for health monitoring of structures", J. Civ. Struct. Hlth. Monit., 5(4), 353-363. https://doi.org/10.1007/s13349-015-0106-y
  24. Pholdee, N., Park, W.-W., Kim, D.-K., Im, Y.-T., Bureerat, S., Kwon, H.-C. and Chun M.-S. (2015), "Efficient hybrid evolutionary algorithm for optimization of a strip coiling process", Eng. Optimiz., 47(4), 521-532. https://doi.org/10.1080/0305215X.2014.905551
  25. Rao, R.V., Savsani, V.J. and Vakharia, D.P. (2011), "Teaching-learning-based optimization: A novel method for constrained mechanical design optimization problems", Comput. Aid. Des., 43(3), 303-315. https://doi.org/10.1016/j.cad.2010.12.015
  26. Sidibe, Y., Druaux, F., Lefebvre, D., Maze, G. and Leon, F. (2016), "Signal processing and Gaussian neural networks for the edge and damage detection in immersed metal plate-like structures", Artif. Intel. Rev., in press.
  27. Sinou, J.-J. (2009), "A review of damage detection and health monitoring of mechanical systems from changes in the measurement of linear and non-linear vibrations", Ed., R.C. Sapri, Mechanical Vibrations: Measurement, Effects and Control, Nova Science Publishers, Inc.
  28. Socha, K. and Dorigo, M. (2008), "Ant colony optimization for continuous domains", Eur. J. Oper. Res., 185(3), 1155-1173. https://doi.org/10.1016/j.ejor.2006.06.046
  29. Storn, R. and Price, K. (1997), "Differential Evolution-A Simple and Efficient Heuristic for global Optimization over Continuous Spaces", J. Global Optim., 11(4), 341-359. https://doi.org/10.1023/A:1008202821328
  30. Tanabe, R. and Fukunaga, A. (2013), "Evaluating the performance of SHADE on CEC 2013 benchmark problems", IEEE Congress on Evolutionary Computation (CEC), Cancun, Mexico, 20-23 June.
  31. Tanabe, R. and Fukunaga, A.S. (2014), "Improving the search performance of SHADE using linear population size reduction", IEEE Congress on Evolutionary Computation (CEC), Beijing, China, 6-11 July.
  32. Xu, H., Ding, Z., Lu, Z. and Liu, J. (2015), "Structural damage detection based on Chaotic Artificial Bee Colony algorithm", Struct. Eng. Mech., 55(6), 1223-1239. https://doi.org/10.12989/sem.2015.55.6.1223
  33. Zhang, J. and Sanderson, A.C. (2009), "JADE: adaptive differential evolution with optional external archive", IEEE T. Evolut. Comput., 13(5), 945-958. https://doi.org/10.1109/TEVC.2009.2014613

피인용 문헌

  1. An effective method for damage assessment based on limited measured locations in skeletal structures vol.24, pp.1, 2016, https://doi.org/10.1177/1369433220947193