Integration of FEM-ANN Methods for Predicting the Horizontal Displacement of Barrette Walls

Authors

  • Truong Xuan Dang Ho Chi Minh University of Natural Resources and Environment, Vietnam
  • Luan Nhat Vo Faculty of Engineering and Technology, Van Hien University, Vietnam
  • Phuong Tuan Nguyen Mien Tay Construction University, Vinh Long Province, Vietnam
  • Hoa Van Vu Tran The SDCT Research Group, University of Transport Ho Chi Minh City, Ho Chi Minh City, Vietnam
  • Tuan Anh Nguyen University of Transport, Ho Chi Minh City, Vietnam
Volume: 15 | Issue: 3 | Pages: 22246-22251 | June 2025 | https://doi.org/10.48084/etasr.10026

Received: 26 December 2024 | Revised: 3 February 2025 | Accepted: 14 February 2025 | Online: 22 March 2025


Corresponding author: Tuan Anh Nguyen

Abstract

This study aims to evaluate and predict horizontal displacement (UX, UY) and force (Force) of barrette walls during excavation through the integration of Finite Element Method (FEM) and Artificial Neural Networks (ANN). This approach is expected to optimize prediction accuracy, particularly under complex geological conditions. Initially, the FEM model was used to analyze the displacement and forces on the barrette walls in each excavation stage. The analysis results provided detailed data on displacement and forces, including position (X, Y, Z), axial forces (N1, N2), shear forces (Q12, Q23, Q13), moments (M11, M22, M12), and excavation depth (Depth). These data were then used as input to train the ANN model. With a structure comprising two hidden layers (64 neurons each) and one output layer (2 neurons), the ANN was evaluated using metrics such as MSE, RMSE, and R². The results showed that the ANN model achieved high prediction performance with an R² value of 0.9999, demonstrating its ability to accurately predict horizontal displacements. The importance analysis of the input features revealed that Y, Depth, and X were the most influential factors in the prediction results. The error between the predicted and actual values was minimal, highlighting the model's efficiency and reliability. Integration of FEM and ANN has proven to be an effective solution for analyzing and predicting the mechanical behavior of barrette walls. This method has great potential for broad application in the design and construction of geotechnical projects, especially in complex scenarios that require high accuracy.

Keywords:

ANN, barrette wall displacement prediction, key feature evaluation, geotechnical modeling

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How to Cite

[1]
Dang, T.X., Vo, L.N., Nguyen, P.T., Tran, H.V.V. and Nguyen, T.A. 2025. Integration of FEM-ANN Methods for Predicting the Horizontal Displacement of Barrette Walls. Engineering, Technology & Applied Science Research. 15, 3 (Jun. 2025), 22246–22251. DOI:https://doi.org/10.48084/etasr.10026.

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Copyright (c) 2025 Truong Xuan Dang, Luan Nhat Vo, Phuong Tuan Nguyen, Hoa Van Vu Tran, Tuan Anh Nguyen

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