SEISMIC PERFORMANCES OF STRUCTURE USING ARTIFICIAL NEURAL NETWORKS – A REVIEW

Abstract

Earthquakes are among the most catastrophic natural disasters, resulting in considerable structure destruction, fatalities, and extensive impact on socioeconomic. The design of earthquake-resistant structures has emerged as a crucial priority for addressing the risk associated with seismic events. Recent advancements in artificial intelligent, particularly neural networks (ANNs), have highlighted the capability of artificial intelligence, in improving seismic performance evaluation of structures. This paper reviews current research on the application of ANNs in predicting the seismic performance of structures. It examines how ANNs are utilized to develop probabilistic seismic demand models (PSDM) for aboveground and underground structures, highlighting their advantages over traditional methods in terms of accuracy and efficiency. The review demonstrates the capacity of artificial neural networks to simulate intricate seismic responses, facilitating novel and dependable strategies for reducing earthquake hazards and enhancing structure resilience. This study seeks to elucidate the present status of ANN applications and their capacity to enhance seismic performance assessments.