Early damage detection under massive data via innovative hybrid methods: application to a large-scale cable-stayed bridge

被引:29
作者
Daneshvar, Mohammad Hassan [1 ]
Gharighoran, Alireza [2 ]
Zareei, Seyed Alireza [1 ]
Karamodin, Abbas [3 ]
机构
[1] Islamic Azad Univ, Isfahan Khorasgan Branch, Dept Civil Engn, Esfahan, Iran
[2] Univ Isfahan, Fac Engn & Transportat, Dept Civil Engn, Esfahan, Iran
[3] Ferdowsi Univ Mashhad, Dept Civil Engn, Fac Engn, Mashhad, Razavi Khorasan, Iran
关键词
Cable-stayed bridge; Gaussian mixture model; massive data; outlier analysis; structural health monitoring; time series analysis; HEALTH MONITORING-SYSTEM; VIBRATION; ALGORITHMS;
D O I
10.1080/15732479.2020.1777572
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
Application of massive data to structural health monitoring (SHM) may lead to serious problems such as difficulty, computational inefficiency, and low damage detectability. This paper proposes innovative hybrid methods in order to detect damage under massive data, ambient vibration, and environmental and/or operational variability. Each of the proposed methods consists of a three-stage algorithm including response modelling via a time series representation, Gaussian mixture model (GMM) for dimensionality reduction, and an outlier detector. Due to the importance of response modelling under ambient vibration, this article employs the combination of AR with ARX called the ARARX model. In the second stage, a GMM is individually fitted to residual samples extracted from ARARX regarding the undamaged and damaged conditions to obtain low-dimensional features from massive data. Eventually, outlier analyses are carried out by the Mahalanobis distance and an auto-associative neural network to make a decision about the occurrence of damage. Two different kinds of threshold limits are also considered to examine the performances of the proposed methods. A large-scale cable-stayed bridge is applied to validate the reliability and effectiveness of these methods with several comparative studies. Results demonstrate that both proposed methods are effective tools for damage detection.
引用
收藏
页码:902 / 920
页数:19
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