Gordan, Meisam and Sabbagh-Yazdi, Saeed-Reza and Ismail, Zubaidah and Ghaedi, Khaled and Carroll, Paraic and McCrum, Daniel and Samali, Bijan (2022) State-of-the-art review on advancements of data mining in structural health monitoring. Measurement, 193. ISSN 0263-2241, DOI https://doi.org/10.1016/j.measurement.2022.110939.
Full text not available from this repository.Abstract
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.
| Item Type: | Article |
|---|---|
| Funders: | Structural Health Monitoring Research Group (StrucHMRSGroup) [Grant No: IIRG007A-2019] |
| Uncontrolled Keywords: | Structural health monitoring; Data mining; Artificial intelligence; Machine learning; Deep learning; Industry 4; 0 |
| Subjects: | T Technology > TA Engineering (General). Civil engineering (General) |
| Divisions: | Faculty of Engineering > Department of Civil Engineering |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 05 Oct 2023 03:22 |
| Last Modified: | 05 Oct 2023 03:22 |
| URI: | http://eprints.um.edu.my/id/eprint/43000 |
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