Machine Learning Applications in Structural Health Monitoring
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Abstract
Structural Health Monitoring (SHM) is critical for ensuring the safety, durability, and performance of civil infrastructures. Recently, Machine Learning (ML) techniques have revolutionized SHM by enabling intelligent damage detection, prediction, and decision-making processes. This article explores the applications of various ML algorithms in SHM, focusing on their methodologies, advantages, and challenges. Emphasizing case studies from Pakistan, the study highlights how ML improves data-driven diagnostics of structures such as bridges, buildings, and dams. Key challenges including sensor noise, data scarcity, and computational complexity are discussed. Furthermore, future directions suggest integration of deep learning and IoT technologies for real-time monitoring and predictive maintenance.
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