• Structural Monitoring and Maintenance
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• v.5 no.3
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• pp.379-390
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• 2018

In this paper, a novel and effective damage diagnosis algorithm is proposed to detect and estimate damage using two stages least squares support vector machine (LS-SVM) and limited number of attached sensors on structures. In the first stage, LS-SVM1 is used to predict the unmeasured mode shapes data based on limited measured modal data and in the second stage, LS-SVM2 is used to predicting the damage location and severity using the complete modal data from the first-stage LS-SVM1. The presented methods are applied to a three story irregular frame and cantilever plate. To investigate the noise effects and modeling errors, two uncertainty levels have been considered. Moreover, the performance of the proposed methods has been verified through using experimental modal data of a mass-stiffness system. The obtained damage identification results show the suitable performance of the proposed damage identification method for structures in spite of different uncertainty levels.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2006.04a
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• pp.778-785
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• 2006

In this paper, two possible ways for mode shape expansion are proposed and opened for discussion for future use. The first method minimizes the modal flexibility error between the experimental and analytical mode shapes corresponding to the measured DOFs to find the multiplication matrix which can be treated as the least-squares minimization problem. In the second method, Normalized Modal Difference (NMD) is used to calculate multiplication matrix using the analytical DOFs corresponding to measured DOfs. This matrix is then used to expand the measured mode shape to unmeasured DOFs. A simulated simply supported beam is used to demonstrate the performance of the methods. These methods are then compared with two most promising existing methods namely Kidder dynamic expansion and Modal expansion methods. It is observed that the performance of the modal flexibility method is comparable with existing methods. NMD also have the potential to expand the mode shapes though it is seen more sensitive to the distribution of error between FEM and actual test data.