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

被引:0
|
作者
Gordan, Meisam [1 ,2 ]
Sabbagh-Yazdi, Saeed-Reza [2 ]
Ismail, Zubaidah [1 ]
Ghaedi, Khaled [1 ]
Carroll, Paraic [3 ]
McCrum, Daniel [3 ]
Samali, Bijan [4 ]
机构
[1] Univ Malaya, Dept Civil Engn, Kuala Lumpur 50603, Malaysia
[2] KN TOOSI Univ Technol, Dept Civil Engn, Tehran, Iran
[3] Univ Coll Dublin, Sch Civil Engn, Dublin, Ireland
[4] Western Sydney Univ, Ctr Infrastruct Engn, Sydney, NSW, Australia
关键词
Structural health monitoring; Data mining; Artificial intelligence; Machine learning; Deep learning; Industry; 4; 0; ARTIFICIAL NEURAL-NETWORK; PRINCIPAL COMPONENT ANALYSIS; IMPERIALIST COMPETITIVE ALGORITHM; EMPIRICAL WAVELET TRANSFORM; BEAM-LIKE STRUCTURES; DAMAGE DETECTION; FAULT-DIAGNOSIS; KNOWLEDGE DISCOVERY; GENETIC ALGORITHM; ANOMALY DETECTION;
D O I
10.1016/j.measurement.2022.110939
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
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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页数:38
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