Machine Learning Driven Structural Health Monitoring Techniques for Early Damage Detection in Critical Civil Infrastructure

Abstract

Critical civil infrastructure such as bridges, dams, tunnels, and high-rise buildings plays a vital role in economic development and public safety. Aging infrastructure, increasing service loads, and environmental degradation have significantly increased the risk of structural failures worldwide. Conventional structural health monitoring methods are largely dependent on periodic inspections and physics-based models, which are often time-consuming, subjective, and limited in their ability to detect early-stage damage. Recent advancements in machine learning offer powerful tools for automated, data- driven structural health monitoring capable of real-time damage detection and assessment. This paper investigates machine learning–driven structural health monitoring techniques for early damage detection in critical civil infrastructure. A comprehensive framework is presented that integrates sensor-based data acquisition, feature extraction, machine learning algorithms, and decision-making systems. Supervised, unsupervised, and deep learning approaches are analyzed in the context of vibration-based and response- based monitoring. The study evaluates performance in terms of detection accuracy, robustness to noise, and scalability for large infrastructure systems. Challenges related to data scarcity, model generalization, and interpretability are discussed. The findings demonstrate that machine learning significantly enhances early damage detection capability and supports proactive maintenance strategies, contributing to improved infrastructure resilience and safety.