Development of a Novel Model for Soil-Structure Interaction Analysis in Seismic Zones Using Artificial Intelligence Methods

Abstract

In this study, a novel model for soil-structure interaction analysis in seismic zones is developed using artificial intelligence methods. Soil-structure interaction (SSI) is a critical factor influencing the seismic response of structures, yet it remains challenging due to the complexities associated with soil parameters and structural behavior under seismic loading. Traditional SSI analysis methods typically require high accuracy and involve complex, time-consuming computations. However, recent advancements in artificial intelligence and machine learning offer the potential to enhance the accuracy and efficiency of such analyses.

This research presents a hybrid model based on artificial neural networks and optimization algorithms to simulate the behavior of soil and structures under seismic effects. The model has been optimized to minimize prediction errors and increase analysis speed, providing a more precise simulation of structural behavior. Laboratory and field data related to soil and structural conditions under various scenarios were used to train and validate the model. The results indicate that the proposed model can serve as an effective tool for structural design and stability assessment against seismic forces. In addition to improving accuracy, this model has the potential to reduce both the costs and time associated with seismic analysis.