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Keywords: damage detection, artificial intelligence, neural networks, support vector machine, Gaussian process, extreme learning machine.

Summary

Over the past two decades, significant research has been carried out in the area of damage detection of structural systems and the field of structural health monitoring (SHM) has become a major research field. The research in SHM has been mainly in two distinct areas: a) development of sensing technologies and hardware for identifying the location and the severity of damage, and b) development of diagnostics and computational tools for the analysis and interpretation of the structural response data in order to identify the location and the time of occurrence of the damage. Despite extensive work carried out in the area of diagnostics, a comprehensive and accurate methodology that can be used in conjunction with the hardware and to identify the time, location and the extent of the damage, with a high degree of reliability and especially taking into account the inherent uncertainties is still lacking. In this paper, in order to address some of the current shortcomings in this area, structural damage detection is performed incorporating several methods including artificial intelligence (AI) including back-propagation neural networks, least squares support vector machines (LS-SVMs), adaptive neural-fuzzy inference system, radial basis function neural network, large margin nearest neighbour, extreme learning machine (ELM), and Gaussian process. The comparative results are presented. By considering the dynamic behaviour of a structure as input variables, seven AI methods are constructed, trained and tested to detect the location and severity of damage in civil structures. The variation of running time, mean square error, number of training and testing data, and other indices for measuring the accuracy in the prediction are defined and calculated in order to inspect advantages as well as the shortcomings of each algorithm. The results indicate that the ELM and LS-SVM methods demonstrate better performance in predicting the location and severity of damage than other methods.

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Front Page Browse CCP CSETS CTR IJRT Other Authors Search Purchase Guide FAQ Contact us	Civil-Comp Proceedings ISSN 1759-3433 CCP: 108 PROCEEDINGS OF THE FIFTEENTH INTERNATIONAL CONFERENCE ON CIVIL, STRUCTURAL AND ENVIRONMENTAL ENGINEERING COMPUTING Edited by: J. Kruis, Y. Tsompanakis and B.H.V. Topping Paper 116 Comparison of Seven Artificial Intelligence Methods for Damage Detection of Structures R. Ghiasi¹, M.R. Ghasemi¹ and M. Noori^2,3 ¹Department of Civil Engineering, Faculty of Engineering, University of Sistan and Baluchestan, Zahedan, Iran ²Mechanical Engineering Department, California Polytechnic State University, San Luis Obispo, United States of America ³International Institute for Urban Systems Engineering, Southeast University, Nanjing, China doi:10.4203/ccp.108.116 purchase the full-text of this paper Full Bibliographic Reference for this paper R. Ghiasi, M.R. Ghasemi, M. Noori, "Comparison of Seven Artificial Intelligence Methods for Damage Detection of Structures", in J. Kruis, Y. Tsompanakis, B.H.V. Topping, (Editors), "Proceedings of the Fifteenth International Conference on Civil, Structural and Environmental Engineering Computing", Civil-Comp Press, Stirlingshire, UK, Paper 116, 2015. doi:10.4203/ccp.108.116 Keywords: damage detection, artificial intelligence, neural networks, support vector machine, Gaussian process, extreme learning machine. Summary Over the past two decades, significant research has been carried out in the area of damage detection of structural systems and the field of structural health monitoring (SHM) has become a major research field. The research in SHM has been mainly in two distinct areas: a) development of sensing technologies and hardware for identifying the location and the severity of damage, and b) development of diagnostics and computational tools for the analysis and interpretation of the structural response data in order to identify the location and the time of occurrence of the damage. Despite extensive work carried out in the area of diagnostics, a comprehensive and accurate methodology that can be used in conjunction with the hardware and to identify the time, location and the extent of the damage, with a high degree of reliability and especially taking into account the inherent uncertainties is still lacking. In this paper, in order to address some of the current shortcomings in this area, structural damage detection is performed incorporating several methods including artificial intelligence (AI) including back-propagation neural networks, least squares support vector machines (LS-SVMs), adaptive neural-fuzzy inference system, radial basis function neural network, large margin nearest neighbour, extreme learning machine (ELM), and Gaussian process. The comparative results are presented. By considering the dynamic behaviour of a structure as input variables, seven AI methods are constructed, trained and tested to detect the location and severity of damage in civil structures. The variation of running time, mean square error, number of training and testing data, and other indices for measuring the accuracy in the prediction are defined and calculated in order to inspect advantages as well as the shortcomings of each algorithm. The results indicate that the ELM and LS-SVM methods demonstrate better performance in predicting the location and severity of damage than other methods. purchase the full-text of this paper (price £20) go to the previous paper go to the next paper return to the table of contents return to the book description purchase this book (price £75 +P&P)
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