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

Abstract

Structural Health Monitoring (SHM) and Predictive Maintenance (PdM) are critical for ensuring the safety, reliability, and longevity of civil infrastructure such as bridges, buildings, and dams. Traditional inspection methods are often manual, time-consuming, and prone to human error. Recent advancements in Machine Learning (ML) have revolutionized SHM and PdM by enabling data-driven, automated, and real- time monitoring systems. This review explores the integration of ML techniques into civil infrastructure maintenance, focusing on supervised, unsupervised, and deep learning approaches. Key applications include damage detection, anomaly recognition, life-cycle prediction, and system degradation modeling. The paper discusses various data sources such as sensor networks, vibration data, strain measurements, and visual imagery, and how ML algorithms process this information to identify patterns indicative of structural faults. Challenges such as data quality, model generalization, and interpretability are also examined. Furthermore, the review highlights emerging trends including the use of digital twins, transfer learning, and edge computing in SHM and PdM systems. By summarizing recent developments and outlining future research directions, this paper aims to provide a comprehensive understanding of how ML methods are transforming the monitoring and maintenance of civil infrastructure toward safer, smarter, and more sustainable practices.