首页|State-of-the-art review on advancements of data mining in structural health monitoring
State-of-the-art review on advancements of data mining in structural health monitoring
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NSTL
Elsevier
? 2022 Elsevier LtdTo 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.
Artificial intelligenceData miningDeep learningIndustry 4.0Machine learningStructural health monitoring
Gordan M.、Ismail Z.、Ghaedi K.、Sabbagh-Yazdi S.-R.、Carroll P.、McCrum D.、Samali B.
展开 >
Department of Civil Engineering University of Malaya
Department of Civil Engineering K.N.TOOSI University of Technology
School of Civil Engineering University College Dublin
Centre of Infrastructure Engineering Western Sydney University
NSTL
Elsevier
