AI-Driven Predictive Models for Infrastructure Health Monitoring and Failure Prediction

Abstract

I would like to start my paper with a focus on a rising concern. The concern is related to the infrastructure industry, which faces challenges due to aging infrastructure, urbanization, and environmental pressures. While traditional maintenance methods rely on occasional maintenance checks, AI-based predictive models utilize machine learning and structural health monitoring (SHM) to provide a comprehensive solution, detecting errors and predicting failures. The failure to detect the errors is clearly seen in the I-35W bridge collapse of 2007. Our article will dive deep to know the potential of AI in improving the infrastructure reliability in bridges, dams, and buildings in the United States, India, and Canada. We will use six case studies, like as the Golden Gate Bridge, Tehri Dam, and Confederation
Bridge, to study the results of monitoring time, maintenance costs, and safety incidents. We will study the data integration, environmental variability, and regulatory hurdles as key challenges. Along with this, technical advancements such as deep learning and digital twins are also studied, based on their scalability and adaptability. Our study also covers the global application of AI technologies in civil engineering. This will help us to know about the future developments of generative AI and the integration with Internet of Things (IoT) technology. These topics play a major role in sustainable and great infrastructure.