State-of-the-art review on advancements of data mining in structural health monitoring
Document Type
Article
Publication Date
4-1-2022
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.
Keywords
Structural health monitoring, Data mining, Artificial intelligence, Machine learning, Deep learning, Industry 4
Divisions
sch_civ
Funders
Structural Health Monitoring Research Group (StrucHMRSGroup) [Grant No: IIRG007A-2019]
Publication Title
Measurement
Volume
193
Publisher
Elsevier
Publisher Location
THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND