期刊
MEASUREMENT
卷 151, 期 -, 页码 -出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.measurement.2019.107175
关键词
Data mining-based damage identification; Experimental modal analysis; Cross Industry Standard Process for Data; Mining; Imperial competitive algorithm; Hybrid algorithm
资金
- University of Malaya (UM)
- Ministry of Higher Education (MOHE), Malaysia [IIRG007A, PG144-2016A]
Classical damage detection methods such as visual inspections have many limitations, i.e. time consuming procedure, costly process and ineffective for large and complex structural systems. To overcome these difficulties, a data mining-based damage identification approach is developed in this study. First four natural frequencies which obtained from the experimental modal analysis of a slab-on-girder bridge structure are used as an input database. The laboratory work is carried out through single-type and multiple-type damage scenarios. The applicability of machine learning, artificial intelligence and statistical data mining techniques are here examined using Support Vector Machine (SVM), Artificial Neural Network (ANN) and Classification and Regression Tree (CART) to predict the model behavior and damage severity. Then, a hybrid algorithm is proposed in the deployment step of Cross Industry Standard Process for Data Mining (CRISP-DM) model. According to the obtained results, the hybrid algorithm performs a better accuracy in compare to ANN technique itself. (C) 2019 Elsevier Ltd. All rights reserved.
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