Structural damage diagnosis using incomplete static responses and LS-SVM.
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| Title: | Structural damage diagnosis using incomplete static responses and LS-SVM. |
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| Authors: | Kourehli, S. S.1 (AUTHOR) s-kourehli@iau-ahar.ac.ir |
| Source: | Inverse Problems in Science & Engineering. Mar2017, Vol. 25 Issue 3, p418-433. 16p. |
| Subjects: | Continuous beams (Structural engineering), Least squares, Estimation theory, Support vector machines, Mechanical engineering, Mathematical models |
| Abstract: | This paper presents novel approach to structural damage detection and estimation using incomplete static responses of a damaged structure and least squares support vector machine (LS-SVM). The presented method is based on the reduced stiffness matrix to formulate incomplete static responses as input parameters to the LS-SVM. The presented method is applied to a plane steel bridge, a four-span continuous beam and four-storey plane frame containing several damages. Also, the effect of the discrepancy in stiffness between the finite element model and the actual tested system has been investigated. The results show that the presented method is sensitive to the location and severity of the structural damage in spite of the incomplete noisy data and modelling errors. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | This paper presents novel approach to structural damage detection and estimation using incomplete static responses of a damaged structure and least squares support vector machine (LS-SVM). The presented method is based on the reduced stiffness matrix to formulate incomplete static responses as input parameters to the LS-SVM. The presented method is applied to a plane steel bridge, a four-span continuous beam and four-storey plane frame containing several damages. Also, the effect of the discrepancy in stiffness between the finite element model and the actual tested system has been investigated. The results show that the presented method is sensitive to the location and severity of the structural damage in spite of the incomplete noisy data and modelling errors. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 17415977 |
| DOI: | 10.1080/17415977.2016.1169277 |
