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Assessing single and hybrid AI approaches in conjunction with FEM to enhance seepage prediction in earth dams by Leyla Harbi; Nadia Smail; Bouchrit Rouissat; Hicham Charrak is a Environmental Science article available to read on EtoBox.

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Dam monitoring is essential to early detect discontinuities, significant changes, and irreversible signs of distress. Key indicators include rising piezometric levels, pore pressures, leakage rates, and internal erosion. Real-time monitoring data is collected through sensors, contributing to the prediction of dam behavior, a critical aspect of ensuring long-term dam safety. Traditionally, statistical and finite element models have been employed to study and forecast dam behavior and safety. However, finite element models are limited in their ability to handle nonlinearity and are only applicable under near-ideal conditions during the dam's lifespan. Recently, machine learning techniques, such as artificial intelligence (AI), have introduced innovative approaches to geotechnical problem-solving. Hybrid models, which integrate traditional methods with machine learning, have proven effective in enhancing accuracy and predictive capabilities. In the context of earth dams, piezometric levels serve as crucial indicators of behavior in response to infiltration. This study explores the application of the Back Propagation Neural Network (BPNN) both independently and in combination with the

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Author

Leyla Harbi; Nadia Smail; Bouchrit Rouissat; Hicham Charrak

Publisher

Springer Science and Business Media LLC

Published

2023

Language

EN

Field

Environmental Science (Physical Sciences)