State-Of-The-Art Review of Artificial Neural Network Techniques in Building Frame Soil-Structure Interaction Analysis
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Abstract
Soil-structure interaction (SSI) pertains to the dynamic interplay between a building and the underlying soil, where the characteristics of both the structure and the soil influence the stress distribution and movement of both components. This interaction is particularly crucial in seismic regions, where the behavior of soil-structure systems can significantly affect structural stability and damage. Structures founded on deformable soils are prone to increased static settlement and reduced seismic resilience compared to those on stiffer soils. Despite the importance of SSI, especially concerning soil liquefaction in seismic areas, the dynamic response of reinforced concrete wall-frame dual systems to SSI remains inadequately explored and often overlooked in engineering practice. This review paper delves into the state-of-the-art artificial neural network (ANN) techniques applied to building frame soil-structure interaction analysis. By examining recent advancements in ANN methods, this study aims to address gaps in understanding SSI's impact on structural performance and seismic behavior. The review highlights how simulation studies of soil beneath foundations affect the frequency response and dynamic properties of structures, emphasizing the need for a comprehensive approach to integrate SSI considerations into structural design and sustainability practices.