Machine Learning Methods for Structural Health Monitoring and Predictive Maintenance in Civil Infrastructure

Abstract

Structural health monitoring and predictive maintenance have become essential components of modern civil infrastructure management due to the increasing age, complexity, and usage demands of built assets. Conventional inspection and maintenance approaches are often reactive, labor-intensive, and limited in their ability to detect early-stage damage. Recent advances in machine learning have enabled data-driven methodologies capable of analyzing large volumes of sensor and inspection data to identify damage patterns, assess structural condition, and predict future performance degradation. This review paper provides a comprehensive examination of machine learning techniques applied to structural health monitoring and predictive maintenance of civil infrastructure. Supervised, unsupervised, and deep learning models are critically analyzed with respect to damage detection, localization, severity assessment, and remaining useful life prediction. The integration of sensor networks, data preprocessing strategies, and feature extraction methods is discussed in detail. Challenges related to data scarcity, model generalization, interpretability, and real-world deployment are highlighted. Finally, emerging trends and future research directions are outlined, emphasizing hybrid physics-informed learning frameworks and digital twin integration for resilient and sustainable infrastructure systems.