Machine Learning Techniques for Structural Health Monitoring of Concrete Structures: A Systematic Review

Damage detection plays a major role in civil infrastructures. Construction structures like bridges dams are subject to wide spectrum of stress. Maintaining the structural health of such concrete structures is very crucial in order to avoid collapses. This paper has reviewed the studies that have employed damage identification and detection through machine learning. Using Machine learning (ML) techniques, it is easy to detect damage and rectify the damage in earlier stage. ML techniques studied in this research are SVM, decision tree, PCA, neural network-based approach and clustering-based approach. In this paper, the following techniques have been reviewed in supervised learning and they are: SVM, Neural networks, deep learning and decision trees. Clustering is the unsupervised learning technique involved in this paper. It is found that results obtained in the studies using SVM were more accurate with 98% and 100% when compared to the damage detection monitored using other machine learning techniques.