Comparative Study of Machine Learning Algorithms in Predicting Load-Induced Bridge Failures

Bridges are critical components of transportation infrastructure, and their failure can lead to severe economic losses and safety risks. Traditional methods of monitoring and predicting structural failures often rely on manual inspections and periodic maintenance, which may miss early warning signs of degradation. This research explores the application of Artificial Intelligence (AI) techniques, including machine learning (ML) and deep learning (DL), in predicting structural failures of bridges. By analyzing data from sensors embedded in bridge structures, such as strain gauges, accelerometers, and displacement transducers, AI algorithms can detect patterns indicative of early damage, such as fatigue, corrosion, and structural weaknesses. The study focuses on developing predictive models using historical data on bridge failures, structural health monitoring (SHM) systems, and real-time data from Internet of Things (IoT) devices. The results demonstrate that AI-based predictive maintenance can significantly enhance the accuracy of failure prediction, reduce inspection costs, and improve bridge safety. This research highlights the potential of AI to transform bridge monitoring systems, making them smarter, more proactive, and capable of addressing the challenges of aging infrastructure.