Work

Modeling and prediction of embankment dam displacement under earthquake loading using artificial neural networks and swarm optimization algorithm

Year: 2025

Type: article

Abstract: Evaluation of embankment dam displacement (D) under earthquake loading can contribute to safe design of the dam. Due to the complexities encountered when modeling this problem, soft computing can be seen as an appropriate solution for predicting the embarkment dam displacement under earthquake loading. In this research, ANN and PSO 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), and magnitude (Mw), dominant frequency (Tp), and peak acceleration (amax) of the earthquake. It was figured out that PSO-ANN outperforms PSO in estimating earthquake-induced dam displacement. Compared to other soft-computing methods for predicting embankment dam displacement under earthquake loading, the hybrid PSO-ANN rendered more powerful and suitable.

Source: International Journal of Geo-Engineering

DOI: 10.1186/s40703-025-00235-7

Authors: Yashar Mostafaei, Saman Soleimani Kutanaei

Institutions: Islamic Azad University, Tehran, Islamic Azad University Roudehen Branch, Islamic Azad University, Science and Research Branch, Islamic Azad University of Ayatollah Amoli

Language: English

Topic: Dam Engineering and Safety

Subfield: Civil and Structural Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $2,290