Structural Health Monitoring and Damage Detection through Machine Learning approaches

Abstract

Data-driven approaches are gaining popularity in structural health monitoring (SHM) due to recent technological advances in sensors, high-speed Internet and cloud computing. Since Machine learning (ML), particularly in SHM, was introduced in civil engineering, this modern and promising method has drawn significant research attention. SHM’s main goal is to develop different data processing methodologies and generate results related to the different levels of damage recognition process. SHM implements a technique for damage detection and classification, including data from a system collected under different structural states using a piezoelectric sensor network using guided waves, hierarchical non-linear primary component analysis and machine learning. The primary objective of this paper is to analyse the current SHM literature using evolving ML-based methods and to provide readers with an overview of various SHM applications. The technique and implementation of vibration-based, vision-based surveillance, along with some recent SHM developments are discussed.