2015 Khanzaei P, Abdulrazeg AA, Samali B, Ghaedi K (2015) Thermal and Structural Response of RCC Dams During Their Service Life. Smart Struct Syst 14:159-189.a Khanzaei et al. Hakim and Razak 2014 Hakim SJS, Razak HA (2014b) Modal parameters based structural damage detection using artificial neural networks - a review. Structural systems in civil engineering such as tall buildings, long hydraulic structures, and long span bridges are damage-prone under different loadings such as fatigue, aging, overloading, earthquakes and other natural disasters during their service life ( Duan and Zhang 2006 Duan Z, Zhang K (2006) Data Mining Technology for Structural Health Monitoring. Structural damage detection data mining technique artificial neural network genetic algorithm principal component analysis According to this categorization, applications of DMTs with respect to SHM research area are classified and it is concluded that, applications of DMTs in the SHM domain have increasingly been implemented, in the last decade and the most popular techniques in the area were artificial neural network (ANN), principal component analysis (PCA) and genetic algorithm (GA), respectively.

This paper presents the first attempt to illustrate the data mining techniques (DMTs) applications in SHM through an intensive review of those articles dealing with the use of DMTs aimed for classification-, prediction- and optimization-based data mining methods. In the last decades, DM has provided numerous solutions to structural health monitoring (SHM) problems as an all-inclusive technique due to its powerful computational ability. As a result, a structural damage detection approach including two main components, a set of accelerometers to record the response data and a data mining (DM) procedure, is widely used to extract the information on the structural health condition. Thus, damage assessment can guarantee the integrity of structures. Civil structures are usually prone to damage during their service life and it leads them to loss their serviceability and safety.

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