Smart Structures and Systems

  • 제27권4호
  • /
  • Pages.623-640
  • /
  • 2021
  • /
  • 1738-1584(pISSN)
  • /
  • 1738-1991(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Optimum feature selection for SHM of benchmark structures using efficient AI mechanism

  • 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) ;
  • Chan, Tommy H.T. (Civil Engineering and Built Environment School, Queensland University of Technology (QUT))
  • 투고 : 2020.03.09
  • 심사 : 2020.12.08
  • 발행 : 2021.04.25
⟨ 이전 논문 다음 논문 ⟩

초록

Structural Health Monitoring (SHM) is rapidly developing as a multi-disciplinary technology solution for condition assessment and performance evaluation of civil infrastructures. It consists of three parts: data collection, data processing (feature extraction/selection), and decision-making (feature classification). In this research, for effectively reducing a dimension of SHM data, various methods are proposed such as advanced feature extraction, feature subset selection using optimization algorithm, and effective surrogate model based on artificial intelligence methods. These frameworks enhance the capability of the SHM process to tackle with uncertainties and big data problem. To reach such goals, a framework based on three main blocks are proposed here: feature extraction block using wavelet pocket relative energy (WPRE), feature selection block using improved version of binary harmony search algorithm and finally feature classification block using wavelet weighted least square support vector machine (WWLS-SVM). The capability of the proposed framework is compared with various well known methods for each block. Results will be presented using metrics of precision, recall, accuracy and feature-reduction. Furthermore, to show the robustness of the proposed methods, six well-known benchmark datasets of SHM domain are studied. The results validate the suitability of the proposed methods in providing data reduction and accelerating damage detection process.

키워드

참고문헌

  1. Afkhami, S., Ma, O.R. and Soleimani, A. (2013), "A binary harmony search algorithm for solving the maximum clique problem", Int. J. Comput. Appl., 69.
  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. Beheshti, H., Mahmoud, N., Mohammad, M. and Ghasemi, R. (2017), "New neural network-based response surface method for reliability analysis of structures", Neural Comput. Appl. https://doi.org/10.1007/s00521-017-3109-2
  4. Bishop, C.M. (1995), Neural Networks for Pattern Recognition, Oxford University Press.
  5. Boller, C., Chang, F.-K. and Fujino, Y. (2009), Encyclopedia of Structural Health Monitoring, John Wiley & Sons.
  6. Cai, J., Luo, J., Wang, S. and Yang, S. (2018), "Feature selection in machine learning: A new perspective", Neurocomputing, 300, 70-79. https://doi.org/https://doi.org/10.1016/j.neucom.2017.11.077
  7. Chan, T.H.T., Wong, K., Li, Z. and Ni, Y. (2011), "Structural health monitoring for long span bridges-Hong Kong experience and continuing into Australia", In: Chan, Tommy H T, Thambiratnam, D.P. (Eds.), Structural Health Monitoring in Australia, NOVA Science Publishers, Inc., New York.
  8. Chuang, L.-Y., Yang, C.-H. and Li, J.-C. (2011), "Chaotic maps based on binary particle swarm optimization for feature selection", Appl. Soft Comput., 11, 239-248. https://doi.org/10.1016/j.asoc.2009.11.014
  9. Clough, R.W. and Penzien, J. (1993), Dynamics of Structures, 2nd ed., New York McGraw-Hill.
  10. Coifman, R.R. and Wickerhauser, M.V. (1992), "Entropy-based algorithms for best basis selection", Inf. Theory, IEEE Trans., 38, 713-718. https://doi.org/10.1109/18.119732
  11. Cortes, C. and Vapnik, V. (1995), "Support-vector networks", Mach. Learn., 20, 273-297. https://doi.org/10.1007/BF00994018
  12. Cremona, C. and Santos, J. (2018), "Structural health monitoring as a big-data problem", Struct. Eng. Int., 28, 243-254. https://doi.org/10.1080/10168664.2018.1461536
  13. Das, S. and Saha, P. (2018), "Structural health monitoring techniques implemented on IASC-ASCE benchmark problem: a review", J. Civ. Struct. Heal. Monit., 8, 689-718. https://doi.org/10.1007/s13349-018-0292-5
  14. Daubechies, I. (1992), Ten lectures on wavelets. Siam.
  15. Figueiredo, E., Park, G., Figueiras, J., Farrar, C. and Worden, K. (2009), "Structural health monitoring algorithm comparisons using standard data sets", Los Alamos National Laboratory (LANL), Los Alamos, NM, USA.
  16. Geem, Z.W. (2005), "Harmony search in water pump switching problem", Proceedings of International Conference on Natural Computation, Springer, pp. 751-760.
  17. Geem, Z.W., Kim, J.H. and Loganathan, G.V. (2001), "A new heuristic optimization algorithm: harmony search", Simulation, 76, 60-68. https://doi.org/10.1177/003754970107600201
  18. Ghiasi, R. and Ghasemi, M.R. (2018a), "Optimization-based method for structural damage detection with consideration of uncertainties-a comparative study", Smart Struct. Syst. 22, 561-574. https://doi.org/10.1007/s00366-018-0636-0
  19. Ghiasi, R. and Ghasemi, M.R. (2018b), "An intelligent health monitoring method for processing data collected from the sensor network of structure", Steel Compos. Struct., Int. J., 29(6), 703-716. https://doi.org/10.12989/scs.2018.29.6.703
  20. Ghiasi, R., Torkzadeh, P. and Noori, M. (2016), "A machine-learning approach for structural damage detection using least square support vector machine based on a new combinational kernel function", Struct. Heal. Monit., 15, 302-316. https://doi.org/10.1177/1475921716639587
  21. 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
  22. Ghiasi, R., Fathnejat, H. and Torkzadeh, P. (2019), "A three-stage damage detection method for large-scale space structures using forward substructuring approach and enhanced bat optimization algorithm", Eng. Comput., 35, 857-874. https://doi.org/10.1007/s00366-018-0636-0
  23. Gomes, G.F., Mendez, Y.A.D., Alexandrino, P. da S.L., da Cunha, S.S. and Ancelotti, A.C. (2018), "A review of vibration based inverse methods for damage detection and identification in mechanical structures using optimization algorithms and ANN", Arch. Comput. Methods Eng., 26(4), 883-897. https://doi.org/10.1007/s11831-018-9273-4
  24. Gu, S., Cheng, R. and Jin, Y. (2018), "Feature selection for high-dimensional classification using a competitive swarm optimizer", Soft Comput., 22, 811-822. https://doi.org/10.1007/s00500-016-2385-6
  25. Gui, G., Pan, H., Lin, Z., Li, Y. and Yuan, Z. (2017), "Data-driven support vector machine with optimization techniques for structural health monitoring and damage detection", KSCE J. Civ. Eng., 21, 523-534. https://doi.org/10.1007/s12205-017-1518-5
  26. Guyon, I. and Elisseeff, A. (2003), "An introduction to variable and feature selection", J. Mach. Learn. Res., 3, 1157-1182.
  27. Han, J.-G., Ren, W.-X. and Sun, Z.-S. (2005), "Wavelet packet based damage identification of beam structures", Int. J. Solids Struct., 42, 6610-6627. https://doi.org/10.1016/j.ijsolstr.2005.04.031
  28. Huang, C.-L. (2009), "ACO-based hybrid classification system with feature subset selection and model parameters optimization", Neurocomputing, 73, 438-448. https://doi.org/10.1016/j.neucom.2009.07.014
  29. Huang, G.-B., Zhu, Q.-Y. and Siew, C.-K. (2006), "Extreme learning machine: theory and applications", Neurocomputing, 70, 489-501. https://doi.org/10.1016/j.neucom.2005.12.126
  30. Ivakhnenko, A.G. and Ivakhnenko, G.A. (1995), "The review of problems solvable by algorithms of the group method of data handling (GMDH)", Pattern Recognit. Image Anal. C/C Raspoznavaniye Obraz. I Anal. Izobr., 5, 527-535.
  31. Jensen, R. (2005), "Combining rough and fuzzy sets for feature selection", Ph.D. Dissertation; University of Edinburgh, UK.
  32. Johnson, E.A., Lam, H.F., Katafygiotis, L.S. and Beck, J.L. (2003), "Phase I IASC-ASCE structural health monitoring benchmark problem using simulated data", J. Eng. Mech., 130, 3-15. https://doi.org/10.1061/(ASCE)0733-9399(2004)130:1(3)
  33. Kabir, M.M., Shahjahan, M. and Murase, K. (2011), "A new local search based hybrid genetic algorithm for feature selection", Neurocomputing, 74, 2914-2928. https://doi.org/10.1016/j.neucom.2011.03.034
  34. Kashef, S. and Nezamabadi-pour, H. (2015), "An advanced ACO algorithm for feature subset selection", Neurocomputing, 147, 271-279. https://doi.org/10.1016/j.neucom.2014.06.067
  35. Kashef, S., Nezamabadi-pour, H. and Nikpour, B. (2018), "Multilabel feature selection: A comprehensive review and guiding experiments", Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 8, e1240. https://doi.org/10.1002/widm.1240
  36. Khatibinia, M., Javad Fadaee, M., Salajegheh, J. and Salajegheh, E. (2013), "Seismic reliability assessment of RC structures including soil-structure interaction using wavelet weighted least squares support vector machine", Reliab. Eng. Syst. Saf., 110, 22-33. https://doi.org/10.1016/j.ress.2012.09.006
  37. Kullaa, J. (2009), "Eliminating environmental or operational influences in structural health monitoring using the missing data analysis", J. Intell. Mater. Syst. Struct., 20, 1381-1390. https://doi.org/10.1177/1045389X08096050
  38. Kullaa, J. (2011), "Distinguishing between sensor fault, structural damage, and environmental or operational effects in structural health monitoring", Mech. Syst. Signal Process., 25, 2976-2989. https://doi.org/10.1016/j.ymssp.2011.05.017
  39. Kullaa, J. (2014), "Benchmark data for structural health monitoring", In: EWSHM-7th European Workshop on Structural Health Monitoring.
  40. Kullaa, J., Santaoja, K. and Eymery, A. (2013), "Vibration-based structural health monitoring of a simulated beam with a breathing crack", In: Key Engineering Materials, Trans Tech Publ, pp. 1093-1100. https://doi.org/10.4028/www.scientific.net/KEM.569-570.1093
  41. Liu, H. and Yu, L. (2005), "Toward integrating feature selection algorithms for classification and clustering", IEEE Trans. Knowl. Data Eng., 17(4), 491-502. https://doi.org/10.1109/TKDE.2005.66
  42. Liu, Y., Ju, Y., Duan, C. and Zhao, X. (2011), "Structure damage diagnosis using neural network and feature fusion", Eng. Appl. Artif. Intell., 24, 87-92. https://doi.org/10.1016/j.engappai.2010.08.011
  43. Mahdavi, M., Fesanghary, M. and Damangir, E. (2007), "An improved harmony search algorithm for solving optimization problems", Appl. Math. Comput., 188, 1567-1579. https://doi.org/10.1016/j.amc.2006.11.033
  44. Mallat, S.G. (1989), "A theory for multiresolution signal decomposition: the wavelet representation", Pattern Anal. Mach. Intell. IEEE Trans., 11, 674-693. https://doi.org/10.1109/34.192463
  45. Manjarres, D., Landa-torres, I., Gil-lopez, S., Ser, J. Del, Bilbao, M.N., Salcedo-sanz, S. and Geem, Z.W. (2013), "A survey on applications of the harmony search algorithm", Eng. Appl. Artif. Intell., 1-14. https://doi.org/10.1016/j.engappai.2013.05.008
  46. Moh'd Alia, O. and Mandava, R. (2011), "The variants of the harmony search algorithm: an overview", Artif. Intell. Rev., 36, 49-68. https://doi.org/10.1007/s10462-010-9201-y
  47. Monavari, B., Chan, T.H.T., Nguyen, A. and Thambiratnam, D.P. (2018), "Structural deterioration detection using enhanced autoregressive residuals", Int. J. Struct. Stab. Dyn., 18, 1850160. https://doi.org/10.1142/S0219455418501602
  48. Moyo, P. and Brownjohn, J.M.W. (2002), "Application of Box-Jenkins models for assessing the effect of unusual events recorded by structural health monitoring systems", Struct. Heal. Monit., 1, 149-160. https://doi.org/10.1177/1475921702001002003
  49. Nguyen, N.-T., Lee, H.-H. and Kwon, J.-M. (2008), "Optimal feature selection using genetic algorithm for mechanical fault detection of induction motor", J. Mech. Sci. Technol., 22, 490-496. https://doi.org/10.1007/s12206-007-1036-3
  50. Oh, I.-S., Lee, J.-S. and Moon, B.-R. (2004), "Hybrid genetic algorithms for feature selection", IEEE Trans. Pattern Anal. Mach. Intell., 26, 1424-1437. https://doi.org/10.1109/TPAMI.2004.105
  51. Onwuegbuzie, A.J., Daniel, L. and Leech, N.L. (2007), "Pearson product-moment correlation coefficient", Encycl. Meas. Stat., 2, 751-756.
  52. Peng, H., Long, F. and Ding, C. (2005), "Feature selection based on mutual information: criteria of max-dependency, maxrelevance, and min-redundancy", IEEE Trans. Pattern Anal. Mach. Intell., 1226-1238. https://doi.org/10.1109/TPAMI.2005.159
  53. Powers, D.M. (2011), "Evaluation: from precision, recall and Fmeasure to ROC, informedness, markedness and correlation", J. Mach. Learn. Technol., 2(1), 37-63.
  54. Rashedi, E. and Nezamabadi-pour, H. (2014), "Feature subset selection using improved binary gravitational search algorithm", J. Intell. Fuzzy Syst., 26, 1211-1221. https://doi.org/10.3233/IFS-130807
  55. Ravanfar, S.A. (2017), "Vibration-based structural damage detection and system identification using wavelet multiresolution analysis", PhD. Dissertation, University of Malaya, Kuala Lumpur, Malaysia.
  56. Salehi, H. and Burgueno, R. (2018), "Emerging artificial intelligence methods in structural engineering", Eng. Struct., 171, 170-189. https://doi.org/10.1016/j.engstruct.2018.05.084
  57. Santos, A., Figueiredo, E., Silva, M.F.M., Sales, C.S. and Costa, J.C.W.A. (2015), "Machine learning algorithms for damage detection : Kernel-based approaches", J. Sound Vib., 363, 584-599. https://doi.org/10.1016/j.jsv.2015.11.008
  58. Suykens, J.A.K. and Vandewalle, J. (1999), "Least squares support vector machine classifiers", Neural Process. Lett., 9, 293-300. https://doi.org/10.1023/A:1018628609742
  59. Tedesco, J.W., McDougal, W.G. and Ross, C.A. (1999), Structural Dynamics: Theory and Application, Addison-Wesley, Menlo Park, CA, USA.
  60. Wang, Z. and Ong, K.C.G. (2009), "Structural damage detection using autoregressive-model-incorporating multivariate exponentially weighted moving average control chart", Eng. Struct., 31, 1265-1275. https://doi.org/10.1016/j.engstruct.2009.01.023
  61. Wang, L., Zhou, P., Fang, J. and Niu, Q. (2011), "A hybrid binary harmony search algorithm inspired by ant system", Proceedings of the 2011 IEEE 5th International Conference on Cybernetics and Intelligent Systems (CIS), IEEE, pp. 153-158.
  62. Widodo, A., Yang, B.-S. and Han, T. (2007), "Combination of independent component analysis and support vector machines for intelligent faults diagnosis of induction motors", Expert Syst. Appl., 32, 299-312. https://doi.org/10.1016/j.eswa.2005.11.031
  63. Wu, R.-T. and Jahanshahi, M.R. (2018), "Data fusion approaches for structural health monitoring and system identification: Past, present, and future", Struct. Heal. Monit., 19(2), 552-586. https://doi.org/10.1177/1475921718798769
  64. Zhong, L., Song, H. and Han, B. (2006), "Extracting structural damage features: Comparison between PCA and ICA", In: Intelligent Computing in Signal Processing and Pattern Recognition, Springer, pp. 840-845.