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Modeling and prediction of embankment dam displacement under earthquake loading using artificial neural networks and swarm optimization algorithm
Abstract Evaluation of embankment dam displacement (D) under earthquake loading can contribute to the safe design of the dam. Due to the complexities of modeling this problem, soft computing is an appropriate solution for predicting the embankment dam displacement under earthquake loading. In this research, Artificial Neural Networks (ANN) and Swarm Optimization Algorithm (PSOA) were integrated in an attempt to present a relationship for predicting the displacement of embankment dam (D). For this purpose, data from 102 real cases was utilized. Input parameters included the height (H) and natural period of the dam (Td), minimum required yield acceleration to slide a block of the dam body (ay), magnitude (Mw), dominant frequency (Tp), and peak acceleration (amax) of the earthquake. It was figured out that PSOA-ANN outperforms PSOA in estimating earthquake-induced dam displacement. Compared to other soft-computing methods for predicting embankment dam displacement under earthquake loading, the hybrid PSOA-ANN is more powerful and suitable.
Modeling and prediction of embankment dam displacement under earthquake loading using artificial neural networks and swarm optimization algorithm
Abstract Evaluation of embankment dam displacement (D) under earthquake loading can contribute to the safe design of the dam. Due to the complexities of modeling this problem, soft computing is an appropriate solution for predicting the embankment dam displacement under earthquake loading. In this research, Artificial Neural Networks (ANN) and Swarm Optimization Algorithm (PSOA) were integrated in an attempt to present a relationship for predicting the displacement of embankment dam (D). For this purpose, data from 102 real cases was utilized. Input parameters included the height (H) and natural period of the dam (Td), minimum required yield acceleration to slide a block of the dam body (ay), magnitude (Mw), dominant frequency (Tp), and peak acceleration (amax) of the earthquake. It was figured out that PSOA-ANN outperforms PSOA in estimating earthquake-induced dam displacement. Compared to other soft-computing methods for predicting embankment dam displacement under earthquake loading, the hybrid PSOA-ANN is more powerful and suitable.
Modeling and prediction of embankment dam displacement under earthquake loading using artificial neural networks and swarm optimization algorithm
Geo-Engineering
Mostafaei, Yashar (author) / Soleimani Kutanaei, Saman (author)
2025-02-22
Article (Journal)
Electronic Resource
English
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