Optimization and Simulation of Fluid Reservoirs under Seismic Loads Using Finite Element Analysis and Artificial Neural Networks

This study investigates the optimization, design, and simulation of fluid storage tanks under seismic loads using Finite Element Analysis (FEA) and Artificial Neural Networks (ANN). Fluid tanks are critical infrastructure components, requiring accurate dynamic analysis to ensure safety during earthquakes. The research integrates machine learning, specifically ANN, to predict key structural parameters such as principal stresses, von Mises stress, displacement, and shear force based on tank geometry, material properties, and loading conditions. The ANN model, featuring one hidden layer with Re LU activation, demonstrated excellent learning and generalization, with Training and Validation Loss converging effectively. Simulation results from Abaqus revealed critical moments during seismic loading, highlighting nonlinear behaviors and fluid-structure interactions. The ANN predicted the von Mises stress with a mean error of ۳.۸%, displacement with ۲.۵% error, and shear force with ۴.۱% error compared with Abaqus simulations. Computational time was reduced by ۸۵% compared to full FEA runs.”

کلیدواژه ها:

Artificial Neural Network ، finite element analysis ، Seismic Design of Fluid Tanks