عیب‌یابی سازه‌ها با استفاده از شاخص تابع پاسخ فرکانسی و مدل جایگزین مبتنی بر الگوریتم ماشین یادگیری حداکثر بهینه شده
نویسنده
دانشکده مهندسی عمران، دانشگاه سیستان و بلوچستان
چکیده
امروزه استفاده از مدل‌های جایگزین مبتنی بر الگوریتم‌های هوش مصنوعی در زمینه عیب‌یابی سازه‌ها مورد توجه بسیاری از محققین قرار گرفته است. در این تحقیق، جهت افزیش دقت الگوریتم تشخیص عیوب چندگانه در سیستم‌های سازه‌ای، کرنل جدیدی مبتنی بر موجک لیتلود پالی برای الگویتم هوش مصنوعی ماشین یادگیری حداکثر، توسعه داده می‌شود. به‌منظور کاهش حجم محاسبات به‌روزرسانی مدل سازه از ماشین یادگیری حداکثر به‌عنوان مدل جایگزین برای تحلیل اجزای محدود دقیق سازه استفاده می‌شود. در روش دو مرحله‌ای پیشنهادی در مرحله اول با استفاده از شاخص مبتنی بر تابع پاسخ فرکانسی سازه، المان‌های معیوب مشخص می‌شود و در مرحله دوم شدت خرابی در این اعضا با استفاده از مدل جایگزین مبتنی بر ماشین یادگیری حداکثر تعیین می‌شود. برای مقایسه کارایی سیستم مبتنی بر ماشین یادگیری حداکثر، نتایج حاصل از کرنل پیشنهادی با سایر کرنل‌های پیشنهاد شده برای ماشین یادگیری حداکثر و همچنین الگوریتم ماشین بردار پشتیبان حداقل مربعات، مقایسه شده است. مثال‌های عددی حل‌شده بیانگر افزایش قابل توجه دقت الگوریتم ماشین یادگیری حداکثر در فرایند عیب‌یابی سازه‌ها در صورت استفاده از کرنل موجکی لیتلود پالی است.
کلیدواژه‌ها
عنوان مقاله [English]
Structural Damage Detection using Frequency Response Function Index and Surrogate Model Based on Optimized Extreme Learning Machine Algorithm
نویسنده [English]
- R. Ghiasi
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.
کلیدواژه‌ها [English]
- Detecting structural damages
- Surrogate model
- Extreme Learning Machine (ELM) algorithm
- Littlewood-Paley Wavelet (LPW) kernel
1. Ghiasi, R., Torkzadeh, P., and Noori, M., “Structural Damage Detection using Artificial Neural Networks and Least Square Support Vector Machine with Particle Swarm Harmony Search Algorithm”, International Journal of Sustainable Materials and Structural Systems, Vol. 1, pp. 303-320, 2014.
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.
4. Sampaio, R. P. C., Maia, N. M. M., and Silva, J. M. M., “Damage Detection using the Frequency-Response-Function Curvature Method”, Journal of Sound and Vibration, Vol. 226, No. 5, pp. 1029-1042, 1999.
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.
8. Ghiasi, R., Ghasemi, M. R., and Noori, M., “Comparison of Seven Artificial Intelligence Methods for Damage Detection of Structures”, In: Kruis, J., Tsompanakis, Y., and Topping, B. H. V., (Eds.), Proceedings of the Fifteenth International Conference on Civil, Structural and Environmental Engineering Computing, CivilComp Press, Stirlingshire, UK, Paper 116, 2015.
9. Fathnejat, H., Torkzadeh, P., Salajegheh, E., and Ghiasi, R., “Structural Damage Detection by Model Updating Method Based on Cascade Feed-Forward Neural Network as an Efficient Approximation Mechanism”, International Journal of Optimization in Civil Engineering, Vol. 4, No. 4, pp. 451-472, 2014.
10. Khatibinia, M., Salajegheh, E., Salajegheh, J., and Fadaee, M. J., “Reliability-Based Design Optimization of Reinforced Concrete Structures Including Soil-Structure Interaction using a Discrete Gravitational Search Algorithm and a Proposed Metamodel”, Engineering Optimization, Vol. 45, No. 10, pp. 1147-1165, 2013.
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.
12. Ghiasi, R., Torkzadeh, P., and Noori, M., “A Machine-Learning Approach for Structural Damage Detection using Least Square Support Vector Machine Based on a New Combinational Kernel Function”, Structural Health Monitoring , Vol. 15 , No. 3 , pp. 302-316, 2016.
13. Naeim, F., “Dynamics of Structures-Theory and Applications to Earthquake Engineering”, Earthquake Spectra, Vol. 23, No. 2, pp. 491-492, 2007.
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
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.
4. Sampaio, R. P. C., Maia, N. M. M., and Silva, J. M. M., “Damage Detection using the Frequency-Response-Function Curvature Method”, Journal of Sound and Vibration, Vol. 226, No. 5, pp. 1029-1042, 1999.
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.
8. Ghiasi, R., Ghasemi, M. R., and Noori, M., “Comparison of Seven Artificial Intelligence Methods for Damage Detection of Structures”, In: Kruis, J., Tsompanakis, Y., and Topping, B. H. V., (Eds.), Proceedings of the Fifteenth International Conference on Civil, Structural and Environmental Engineering Computing, CivilComp Press, Stirlingshire, UK, Paper 116, 2015.
9. Fathnejat, H., Torkzadeh, P., Salajegheh, E., and Ghiasi, R., “Structural Damage Detection by Model Updating Method Based on Cascade Feed-Forward Neural Network as an Efficient Approximation Mechanism”, International Journal of Optimization in Civil Engineering, Vol. 4, No. 4, pp. 451-472, 2014.
10. Khatibinia, M., Salajegheh, E., Salajegheh, J., and Fadaee, M. J., “Reliability-Based Design Optimization of Reinforced Concrete Structures Including Soil-Structure Interaction using a Discrete Gravitational Search Algorithm and a Proposed Metamodel”, Engineering Optimization, Vol. 45, No. 10, pp. 1147-1165, 2013.
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.
12. Ghiasi, R., Torkzadeh, P., and Noori, M., “A Machine-Learning Approach for Structural Damage Detection using Least Square Support Vector Machine Based on a New Combinational Kernel Function”, Structural Health Monitoring , Vol. 15 , No. 3 , pp. 302-316, 2016.
13. Naeim, F., “Dynamics of Structures-Theory and Applications to Earthquake Engineering”, Earthquake Spectra, Vol. 23, No. 2, pp. 491-492, 2007.
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
)