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Seismic fragility analysis of high concrete faced rockfill dams based on plastic failure with support vector machine
Abstract With the rapid growth of economic, a growing number of earth and rockfill dams, especially high concrete faced rockfill dams (CFRDs), are being constructed in the intensity earthquake region in southwest of China. Their proximity to earthquake prone areas sets an imperative to evaluate the seismic performance of these dams. It can partially be evaluated based on fragility analysis and then fragility curves of the dams will be obtained. However, the establishment of fragility curves requires numerous numerical simulations with the finite element method (FEM). In this study, a support vector machine (SVM) is introduced to improve the computational efficiency of estimating structural response. Firstly, one hundred ground motion records are synthesized based on information given in the design scenario, and the corresponding intensity measures (IMs) can be obtained from each record. Secondly, numerical simulations are conducted by FEM and the corresponding damage measures (DMs) can be obtained. Three DMs are considered in this study, including vertical displacement, plastic shear strain, as well as the damage index (DI) of the face slab. Then, SVM models corresponding to the three DMs are trained according to the IMs and DMs, respectively. Finally, the fragility curves of different limit states based on the three DMs are calculated. This method of combining FEM and SVM is used for analyzing the seismic fragility of a 200 m CFRD. The results indicate that this method is effective for fragility analysis of CFRDs.
Highlights Fragility analysis of high CFRDs is investigated combining FEM with SVM. Three DMs which can reflect the response of dam are selected. Fragility curves under different limit states are calculated based on PGA. All materials of the CFRD model are simulated by elastoplastic models.
Seismic fragility analysis of high concrete faced rockfill dams based on plastic failure with support vector machine
Abstract With the rapid growth of economic, a growing number of earth and rockfill dams, especially high concrete faced rockfill dams (CFRDs), are being constructed in the intensity earthquake region in southwest of China. Their proximity to earthquake prone areas sets an imperative to evaluate the seismic performance of these dams. It can partially be evaluated based on fragility analysis and then fragility curves of the dams will be obtained. However, the establishment of fragility curves requires numerous numerical simulations with the finite element method (FEM). In this study, a support vector machine (SVM) is introduced to improve the computational efficiency of estimating structural response. Firstly, one hundred ground motion records are synthesized based on information given in the design scenario, and the corresponding intensity measures (IMs) can be obtained from each record. Secondly, numerical simulations are conducted by FEM and the corresponding damage measures (DMs) can be obtained. Three DMs are considered in this study, including vertical displacement, plastic shear strain, as well as the damage index (DI) of the face slab. Then, SVM models corresponding to the three DMs are trained according to the IMs and DMs, respectively. Finally, the fragility curves of different limit states based on the three DMs are calculated. This method of combining FEM and SVM is used for analyzing the seismic fragility of a 200 m CFRD. The results indicate that this method is effective for fragility analysis of CFRDs.
Highlights Fragility analysis of high CFRDs is investigated combining FEM with SVM. Three DMs which can reflect the response of dam are selected. Fragility curves under different limit states are calculated based on PGA. All materials of the CFRD model are simulated by elastoplastic models.
Seismic fragility analysis of high concrete faced rockfill dams based on plastic failure with support vector machine
Zhou, Yang (author) / Zhang, Yi (author) / Pang, Rui (author) / Xu, Bin (author)
2021-01-04
Article (Journal)
Electronic Resource
English
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