Structural Damage Detection using Frequency Response Function Index and Surrogate Model Based on Optimized Extreme Learning Machine Algorithm
Author
Abstract
Utilizing surrogate models based on artificial intelligence methods for detecting structural damages has attracted the attention of many researchers in recent decades. In this study, a new kernel based on Littlewood-Paley Wavelet (LPW) is proposed for Extreme Learning Machine (ELM) algorithm to improve the accuracy of detecting multiple damages in structural systems. ELM is used as metamodel (surrogate model) of exact finite element analysis of structures in order to efficiently reduce the computational cost through updating process. In the proposed two-step method, first a damage index, based on Frequency Response Function (FRF) of the structure, is used to identify the location of damages. In the second step, the severity of damages in identified elements is detected using ELM. In order to evaluate the efficacy of ELM, the results obtained from the proposed kernel were compared with other kernels proposed for ELM as well as Least Square Support Vector Machine algorithm. The solved numerical problems indicated that ELM algorithm accuracy in detecting structural damages is increased drastically in case of using LPW kernel.
Keywords
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2. Xu, Y., Qian, Y., Chen, J., and Song, G.,“Probability-Based Damage Detection using Model Updating with Efficient Uncertainty Propagation”, Mechanical Systems and Signal Processing, Vol. 60, pp. 958-970, 2015.
3. Zang, C., and Imregun, M., “Structural Damage Detection using Artificial Neural Networks and Measured FRF Data Reduced Via Principal Component Projection”, Journal of Sound and Vibration, Vol. 242, No. 5, pp. 813-827, 2001.
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5. Torkzadeh, P, and Khamseh, M., “Structural Engineering a Two-Stage Damage Detection Method for Truss Structures using FRF Data and LMPSO Algorithm”, Iranian Journal of Structural Engineering, Vol. 1, No. 2, pp. 114-125, 2014.
6. Link, R. J., and Zimmerman, D. C., “Structural Damage Diagnosis using Frequency Response Functions and Orthogonal Matching Pursuit: Theoretical Development”, Structural Control and Health Monitoring, Vol. 22, No. 6, pp. 889-902, 2015.
7. Hakim, S. J. S., and Razak, H. A., “Modal Parameters Based Structural Damage Detection using Artificial Neural Networks-A Review”, Smart Structures and Systems, Vol. 14, No. 2, pp. 159-189, 2014.
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11. Khatibinia, M., Javad Fadaee, M., Salajegheh, J., and Salajegheh, E., “Seismic Reliability Assessment of RC Structures Including Soil-Structure Interaction using Wavelet Weighted least Squares Support Vector Machine”, Reliability Engineering & System Safety, Vol. 110, pp. 22-33, 2013.
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14. Craig, R. R., and Kurdila, A. J., Fundamentals of Structural Dynamics, John Wiley & Sons, 2006.
15. Girard, A., and Nicolas, R., Structural Dynamics in Industry ,Vol. 7, John Wiley & Sons, 2010.
16. Sánchez, J. C. H., and Carlos, J. “Evaluation of Structural Damage Identification Methods Based on Dynamic Characteristics”, Ph.D Thesis, University of Puerto Rico, Mayagüez, 2005.
17. Huang, G.-B., Zhu, Q.-Y., and Siew, C.-K., “Extreme Learning Machine: Theory and Applications”, Neurocomputing, Vol. 70, No. 1-3, pp. 489-501, 2006.
18. Sahoo, S., Mohapatra, S. K., and Panda, B., “Classification Using Extreme Learning Machine”, Compusoft, An International Journal of Advanced Computer Technology, Vol. 2, No. 12, p. 415-421, 2013.
19. Huang, G., Member, S., Zhou, H., Ding, X., and Zhang, R., “Extreme Learning Machine for Regression and Multiclass Classification”, IEEE Transactions on Systems, Man, and Cybernetics, Part B, Vol. 42, No. 2, pp. 513-529, 2012.
20. Huang, G.-B., “An Insight Into Extreme Learning Machines: Random Neurons, Random Features and Kernels”, Cognitive Computation, Vol. 6, No. 3, pp. 376-390, 2014.
21. Xing, Y., Wu, X., and Xu, Z., “Multiclass Least Squares Wavelet Support Vector Machines”. Proceedings of the IEEE International Conference on Networking, Sensing and Control, Sanya, China, pp. 498-502, 2008.
22. Cortes, C., and Vapnik, V., “Support-Vector Networks”, Machine learning, Vol. 20, No. 3, pp. 273-297,1995.
23. Suykens, J. A., and Vandewalle, J. “Least Squares Support Vector Machine Classifiers”, Neural Processing Letters, Vol. 9, No. 3, pp. 293-300, 1999.
24. Yazdanpanah, O., Seyedpoor, S. M., and Bengar, H. A., “A New Damage Detection Indicator for Beams Based on Mode Shape Data”, Structural Engineering and Mechanics, Vol. 53, No. 4, pp. 725-744, 2015.
25. Gholizadeh, S., Salajegheh, E., and Torkzadeh, P., “Structural Optimization with Frequency Constraints by Genetic Algorithm using Wavelet Radial Basis Function Neural Network”, Journal of Sound and Vibration, Vol. 312, No. 1-2, pp. 316-331, 2008
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