Structural Health Monitoring (SHM) and Predictive Maintenance (PdM) are integral to ensuring the safety, longevity, and performance of modern infrastructure systems. The integration of machine learning (ML) into SHM and PdM frameworks has revolutionized the way infrastructure assets are monitored and maintained. By leveraging real-time sensor data and advanced analytics, ML-driven SHM systems enable early detection of structural issues, enhancing predictive capabilities and optimizing maintenance interventions. This chapter provides an in-depth exploration of the current trends and future directions in ML-based SHM, highlighting key advancements in sensor technologies, data acquisition, and hybrid machine learning models. Emphasis is placed on the challenges associated with small datasets, the role of data augmentation, and the growing integration of IoT for continuous, remote monitoring. Case studies from bridges, buildings, and transportation networks demonstrate the practical applications of predictive maintenance models, illustrating the tangible benefits of proactive infrastructure management. As smart cities and digital twins gain traction, the potential for ML-driven SHM and PdM to transform infrastructure maintenance strategies becomes increasingly significant, offering scalable solutions for the management of aging and complex systems. This chapter contributes to the understanding of how ML, IoT, and advanced predictive analytics can reshape infrastructure management practices in the digital age.
Structural health monitoring (SHM) and predictive maintenance (PdM) are pivotal to ensuring the sustainability and safety of infrastructure systems in the modern world [1]. As cities expand and infrastructure ages, traditional methods of inspection and maintenance often fall short in addressing the dynamic and complex needs of critical infrastructure [2]. In this context, SHM and PdM, empowered by advanced technologies such as machine learning (ML), provide innovative solutions to enhance the reliability and efficiency of infrastructure management [3]. These technologies enable the continuous monitoring of structural components, allowing for early detection of damage, real-time performance assessment, and the prediction of potential failures [4]. By transitioning from reactive to proactive maintenance strategies, the combination of SHM and PdM significantly reduces the risk of catastrophic infrastructure failures while extending the operational lifespan of assets [5].
Machine learning algorithms have become integral to SHM and PdM systems, revolutionizing the way infrastructure data is analyzed and interpreted [6]. Machine learning's ability to process vast amounts of sensor data and uncover complex patterns has proven invaluable in identifying structural anomalies and predicting maintenance needs [7]. These algorithms enable the analysis of real-time data collected from a variety of sensors embedded within infrastructure components, such as vibration, strain, temperature, and displacement sensors [8]. Through supervised and unsupervised learning, ML models can classify damage types, detect early signs of deterioration, and even forecast the remaining useful life of infrastructure assets. By enhancing predictive capabilities [9], ML-driven systems allow for more informed decision-making and more efficient resource allocation for maintenance activities [10].
The integration of Internet of Things (IoT) technologies with SHM and PdM systems has further expanded the potential of machine learning models [11]. IoT-enabled sensors provide continuous data streams, offering real-time insights into the condition of infrastructure components [12]. These sensors, often deployed in challenging and remote locations, gather diverse data types that are essential for comprehensive infrastructure monitoring [13]. Machine learning algorithms process this large volume of data, identifying trends and deviations that may indicate underlying issues. IoT integration facilitates the remote monitoring of infrastructure, allowing for more frequent assessments of conditions without the need for physical inspections [14]. This continuous flow of data helps maintain up-to-date information, enabling infrastructure managers to stay ahead of potential failures and respond to issues promptly [15].
