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Smart Structures and Systems Volume 27, Number 4, April 2021 , pages 623-640 DOI: https://doi.org/10.12989/sss.2021.27.4.623 |
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Optimum feature selection for SHM of benchmark structures using efficient AI mechanism |
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Ramin Ghiasi, Mohammad Reza Ghasemi and Tommy H.T. Chan
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| Abstract | ||
| Structural Health Monitoring (SHM) is rapidly developing as a multi-disciplinary technology solution for condition assessment and performance evaluation of civil infrastructures. It consists of three parts: data collection, data processing (feature extraction/selection), and decision-making (feature classification). In this research, for effectively reducing a dimension of SHM data, various methods are proposed such as advanced feature extraction, feature subset selection using optimization algorithm, and effective surrogate model based on artificial intelligence methods. These frameworks enhance the capability of the SHM process to tackle with uncertainties and big data problem. To reach such goals, a framework based on three main blocks are proposed here: feature extraction block using wavelet pocket relative energy (WPRE), feature selection block using improved version of binary harmony search algorithm and finally feature classification block using wavelet weighted least square support vector machine (WWLS-SVM). The capability of the proposed framework is compared with various well known methods for each block. Results will be presented using metrics of precision, recall, accuracy and feature-reduction. Furthermore, to show the robustness of the proposed methods, six well-known benchmark datasets of SHM domain are studied. The results validate the suitability of the proposed methods in providing data reduction and accelerating damage detection process. | ||
| Key Words | ||
| structural health monitoring; feature extraction; feature selection; surrogate model; SHM benchmarks; data reduction | ||
| Address | ||
| (1) Ramin Ghiasi, Mohammad Reza Ghasemi: Department of Civil Engineering, Faculty of Engineering, University of Sistan and Baluchestan, Zahedan, Iran; (2) Tommy H.T. Chan: Civil Engineering and Built Environment School, Queensland University of Technology (QUT), Brisbane, QLD, Australia. | ||