Reinforcement Learning for Enhancing Earthquake Resistance in Concrete Structures Using Artificial Intelligence

Abstract

In civil engineering, the seismic durability of building materials is still a major concern, particularly in areas where earthquakes are common. To improve the seismic resistance of concrete structures, this article investigates the use of artificial intelligence (AI) approaches, particularly reinforcement learning (RL). By using structural responses to seismic stresses as a source of information, reinforcement learning presents a viable method for optimizing the parameters of design and reinforcement tactics. The study simulates and assesses the behavior of building materials subjected to stresses caused by earthquakes by integrating finite element analysis with reinforcement learning methods. In comparison to conventional design methodologies, a case study is provided to show how well the suggested RL-based methodology improves seismic performance indicators including displacement, speed, and integrity of the structure. The results demonstrate how AI-driven reinforcement learning approaches can progress the field of resistance to earthquake design and help create concrete structures that are more durable and safe.