Smart Structures and Systems

  • 제11권1호
  • /
  • Pages.69-86
  • /
  • 2013
  • /
  • 1738-1584(pISSN)
  • /
  • 1738-1991(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Multi-stage approach for structural damage identification using particle swarm optimization

  • Tang, H. (State Key Laboratory for Disaster Reduction in Civil Engineering, Tongji University) ;
  • Zhang, W. (Fujian Academy of Building Research) ;
  • Xie, L. (Research Institute of Structural Engineering and Disaster Reduction, Tongji University) ;
  • Xue, S. (Research Institute of Structural Engineering and Disaster Reduction, Tongji University)
  • 투고 : 2012.06.11
  • 심사 : 2012.11.30
  • 발행 : 2013.01.25
⟨ 이전 논문 다음 논문 ⟩

초록

An efficient methodology using static test data and changes in natural frequencies is proposed to identify the damages in structural systems. The methodology consists of two main stages. In the first stage, the Damage Signal Match (DSM) technique is employed to quickly identify the most potentially damaged elements so as to reduce the number of the solution space (solution parameters). In the second stage, a particle swarm optimization (PSO) approach is presented to accurately determine the actual damage extents using the first stage results. One numerical case study by using a planar truss and one experimental case study by using a full-scale steel truss structure are used to verify the proposed hybrid method. The identification results show that the proposed methodology can identify the location and severity of damage with a reasonable level of accuracy, even when practical considerations limit the number of measurements to only a few for a complex structure.

키워드

참고문헌

  1. Bakhtiari-Nejada, F., Rahai, A. and Esfandiari. A. (2005), "A structural damage detection method using static noisy data", Eng. Struct., 27(12), 1784-1793. https://doi.org/10.1016/j.engstruct.2005.04.019
  2. Begambre, O. and Laier, J.E. (2009), "A hybrid Particle Swarm Optimization-Simplex algorithm (PSOS) for structural damage identification", Adv. Eng. Softw., 40(9), 883-891. https://doi.org/10.1016/j.advengsoft.2009.01.004
  3. Boeringer, D.W. and Werner, D.H. (2003), "A comparison of particle swarm optimization and genetic algorithms for a phased array synthesis problem", Proceedings of the IEEE Antennas and Propagation Society International Symposium.
  4. Chiang, D.Y. and Lai, W.Y. (1999), "Structural damage detection using the simulated evolution method", AIAA J., 37(10), 1331-1333. https://doi.org/10.2514/2.607
  5. 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
  6. Doebling, S.W., Farrar, C.R. and Prime, M.B. (1998), "A summary review of vibration-based damage identification methods", Shock Vib., 30(2), 91-105. https://doi.org/10.1177/058310249803000201
  7. Eberhart, R.C. and Kennedy, J. (1995), "A new optimiser using particle swarm theory", Proceedings of the 6th International Symposium on Micro Machine and Human Science, Nagoya, Japan.
  8. Eberhart, R.C. and Shi, Y. (2001), "Particle swarm optimization: developments, applications and resources", Proceedings of the 2001 Congress Evolutionary Computation, Seoul, South Korea.
  9. Franco, G., Betti, R., and Lus, H. (2004), "Identification of structural systems using an evolutionary strategy", J. Eng. Mech.- ASCE, 130(10), 1125-1139. https://doi.org/10.1061/(ASCE)0733-9399(2004)130:10(1125)
  10. Guo, H.Y. and Li, Z.L. (2009), "A two-stage method to identify structural damage sites and extents by using evidence theory and micro-search genetic algorithm", Mech. Syst. Signal. Pr., 23(3), 769-82. https://doi.org/10.1016/j.ymssp.2008.07.008
  11. Hajela, P. and Soeiro, F.J. (1990), "Recent developments in damage detection based on system identification methods", Struct. Opt., 2(1), 1-10. https://doi.org/10.1007/BF01743515
  12. He, Q. and Wang, L. (2007), "An effective co-evolutionary particle swarm optimization for constrained engineering design problems", Eng. Appl. Artif. Intell., 20(1), 89-99. https://doi.org/10.1016/j.engappai.2006.03.003
  13. Hu, X.H., Shi, Y.H. and Eberhart, R. (2004), "Recent advances in particle swarm", Proceedings of the 2004 Congress on Evolutionary Computation, West Lafayette, USA.
  14. Hjelmstad, K.D. and Shin, S. (1997), "Damage detection and assessment of structures from static response", J. Eng. Mech.- ASCE, 123(6), 568-576. https://doi.org/10.1061/(ASCE)0733-9399(1997)123:6(568)
  15. Kennedy, J. and Eberhart, R.C. (1995), "Particle swarm optimization", Proceedings of the IEEE International Conference on Neural Networks, Piscataway.
  16. Kennedy, J., Eberhart, R.C. and Shi, Y. (2001), Swarm intelligence, Morgan Kaufmann Publishers, San Francisco.
  17. Koh, C.G., Chen, Y.F. and Liaw, C.Y. (2003), "A hybrid computational strategy for identification of structural parameters", Comput. Struct., 81(2), 107-117. https://doi.org/10.1016/S0045-7949(02)00344-9
  18. Koh, C.G. and Perry, M.J. (2009), Structural Identification and Damage Detection using Genetic Algorithms, Taylor and Francis.
  19. Lam H.F., Ko J.M. and Wong C.W. (1998), "Localization of damaged structural connections based on experimental modal and sensitivity analysis", J. Sound Vib., 210(1), 91-115. https://doi.org/10.1006/jsvi.1997.1302
  20. Levin, R.I. and Lieven, N.A.J. (1998), "Dynamic finite element model updating using simulated annealing and genetic algorithm", Mech. Syst. Signal. Pr., 12(1), 91-120. https://doi.org/10.1006/mssp.1996.0136
  21. Liang, J.J., Qin, A.K., Suganthan, P.N. and Baskar S. (2006), "Comprehensive learning particle swarm optimizer for global optimization of multimodal functions", IEEE Trans. Evolut. Comput., 10(3), 281-295. https://doi.org/10.1109/TEVC.2005.857610
  22. Mares, C. and Surace , C. (1996), "An application of genetic algorithms to identify damage in elastic structures", J. Sound Vib., 195(2), 195-215. https://doi.org/10.1006/jsvi.1996.0416
  23. Meissner, M., Schmuker, M. and Schneider, G. (2006), "Optimized Particle Swarm Optimization (OPSO) and its application to artificial neural network training", BMC Bioinf., 7(125).
  24. Mouser, C.R. and Dunn, S.A. (2005), "Comparing genetic algorithms and particle swarm optimisation for an inverse problem exercise", Aust. Nz. Industr. Appl. Math. J., 46(E), C89-C101.
  25. Moslem, K. and Nafaspour, R. (2002), "Structural damage detection by genetic algorithms", AIAA J., 40(7), 1395-1401. https://doi.org/10.2514/2.1800
  26. Ostachowicz, W., Krawczuk, M. and Cartmell, M. (2002), "The location of a concentrated mass on rectangular plates from measurements of natural vibrations", Comput. Struct., 80(16-17), 1419-1428. https://doi.org/10.1016/S0045-7949(02)00084-6
  27. Perera, R., Fang, S.E. and Ruiz, A. (2010), "Application of particle swarm optimization and genetic algorithms to multiobjective damage identification inverse problems with modelling errors", Meccanica, 45(5), 723-734. https://doi.org/10.1007/s11012-009-9264-5
  28. Perry, M.J., Koh, C.G., and Choo, Y.S. (2006), "Modified genetic algorithm strategy for structural identification", Comput. Struct., 84(8-9), 529-540. https://doi.org/10.1016/j.compstruc.2005.11.008
  29. Peter Carden, E. and Fanning, P. (2004), "Vibration based condition monitoring: a review", Struct. Health Monit., 3(4), 355-377. https://doi.org/10.1177/1475921704047500
  30. Raich, M. and Liszkai, R. (2007), "Improving the performance of structural damage detection methods using advanced genetic algorithms", J. Struct. Eng.- ASCE, 133(3), 449-461. https://doi.org/10.1061/(ASCE)0733-9445(2007)133:3(449)
  31. Srinivas, V., Ramanjaneyulu, K. and Jeyasehar, C.A. (2010), "Multi-stage approach for structural damage identification using modal strain energy and evolutionary optimization techniques", Struct. Health Monit., 10(2), 219-230.
  32. Sohn, H., Farrar, C.R., Hemez, F.M, Shunk, D.D, Stinemates, D.W and Nadler, B.R. (2003), A review of structural health monitoring literature: 1996-2001, Los Alamos National Laboratory Report LA-13976-MS.
  33. Tang, H. , Fukuda, M. and Xue, S. (2007), "Particle swarm optimization for structural system identification", Proceedings of the 6th International Workshop on Structural Health Monitoring, Stanford University, USA.
  34. Tang, H., Xue, S. and Fan, C. (2008), "Differential evolution strategy for structural system identification", Compu. Struct., 86(21-22), 2004-2012. https://doi.org/10.1016/j.compstruc.2008.05.001
  35. Viola, E. and Bocchini, P. (2011), Non-destructive parametric system identification and damage detection in truss structures by static tests, Structure and Infrastructure Engineering: Maintenance, Management, Life-Cycle Design and Performance, Taylor and Francis.
  36. Wang, X, Hu, N., Fukunaga, H. and Yao, Z.H. (2001), "Structural damage identification using static test data and changes in frequencies", Eng. Struct., 23(6), 610-621. https://doi.org/10.1016/S0141-0296(00)00086-9

피인용 문헌

  1. A new PSRO algorithm for frequency constraint truss shape and size optimization vol.52, pp.3, 2014, https://doi.org/10.12989/sem.2014.52.3.445
  2. A hybrid self-adaptive Firefly-Nelder-Mead algorithm for structural damage detection vol.17, pp.6, 2016, https://doi.org/10.12989/sss.2016.17.6.957
  3. Structural damage and force identification under moving load vol.53, pp.2, 2015, https://doi.org/10.12989/sem.2015.53.2.261
  4. Truss optimization with dynamic constraints using UECBO vol.1, pp.2, 2016, https://doi.org/10.12989/acd.2016.1.2.119
  5. Structural damage identification using an iterative two-stage method combining a modal energy based index with the BAS algorithm vol.36, pp.1, 2020, https://doi.org/10.12989/scs.2020.36.1.031
  6. An Investigation of Particle Swarm Optimization Topologies in Structural Damage Detection vol.11, pp.11, 2021, https://doi.org/10.3390/app11115144