Application of Machine Learning in Predicting the Structural Integrity of Bridges

Abstract

The structural integrity of bridges is paramount to ensuring public safety and the seamless functioning of transportation networks. Traditional methods of bridge inspection and maintenance, while effective, are often time-consuming, labor-intensive, and susceptible to human error. The advent of machine learning (ML) offers a transformative approach to enhancing the accuracy and efficiency of structural health monitoring (SHM) systems. This paper explores the application of various machine learning algorithms in predicting the structural integrity of bridges. By leveraging data from sensors, historical inspections, and environmental conditions, ML models can identify patterns and anomalies indicative of potential structural issues. The study reviews existing literature, outlines a comprehensive methodology for data collection and model training, and presents results demonstrating the efficacy of ML in bridge integrity prediction. The findings suggest that machine learning not only augments traditional SHM practices but also paves the way for proactive maintenance strategies, ultimately contributing to safer and more reliable bridge infrastructures.