期刊
MEASUREMENT
卷 193, 期 -, 页码 -出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.measurement.2022.110939
关键词
Structural health monitoring; Data mining; Artificial intelligence; Machine learning; Deep learning; Industry 4; 0
资金
- University of Malaya
- K.N. TOOSI University of Technology
- Structural Health Monitoring Research Group (StrucHMRSGroup) [IIRG007A-2019]
This paper classifies the applications of data mining techniques in structural health monitoring (SHM) and presents the development of artificial intelligence, machine learning, and statistical methods. It compares the most commonly used techniques and algorithms in SHM.
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.
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