State-of-the-art review on advancements of data mining in structural health monitoring

•A wide range of researches are collected to demonstrate the recent advances in SHM.•Background, models, techniques, and functions of data mining are presented.•The latest data mining applications in SHM are reviewed and classified.•The abilities and drawbacks of applicable data mining techniques in SHM are described.•Future trends of data mining applications in SHM are addressed. To date, data mining (DM) techniques, i.e. artificial intelligence, machine learning, and statistical methods have been utilized in a remarkable number of structural health monitoring (SHM) applications. Nevertheless, there is no classification of these approaches to know the most used techniques in SHM. For this purpose, an intensive review is carried out to classify the aforementioned techniques. In doing so, a brief background, models, functions, and classification of DM techniques are presented. To this end, wide range of researches are collected in order to demonstrate the development of DM techniques, detect the most popular DM techniques, and compare the applicability of existing DM techniques in SHM. Eventually, it is concluded that the application of artificial intelligence has the highest demand rate in SHM while the most popular algorithms including artificial neural network, genetic algorithm, fuzzy logic, and principal component analysis are utilized for damage detection of civil structures.