Integration of FEM-ANN Methods for Predicting the Horizontal Displacement of Barrette Walls - Dang, Truong Xuan; Vo, Luan Nhat; Nguyen, Phuong Tuan; Tran, Hoa Van Vu; Nguyen, Tuan Anh (Engineering, Technology & Applied Science Research, 2025)
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.