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Ensemble learning-based structural health monitoring by Mahalanobis distance metrics
Hassan Sarmadi1,2; Alireza Entezami1; Behzad Saeedi Razavi3; Ka-Veng Yuen4
Source PublicationStructural Control and Health Monitoring

Environmental variability is still a major challenge in structural health monitoring. Due to the similarity of changes caused by environmental variations to damage, false positive and false negative errors are prevalent in detecting damage that cause serious economic and safety issues. To address this challenge and its disadvantages, this article proposes a novel ensemble learning-based method in a nongenerative sequential algorithm for structural health monitoring under varying environmental conditions by three kinds of Mahalanobis distance metrics in three main levels. At each level, one attempts to find a few and adequate nearest neighbors of each feature to remove environmental variability via an innovative approach. The major contribution of this article is to develop a novel data-based method by the concepts of ensemble learning and unsupervised learning. The great advantages of the proposed method include developing a nonparametric data-based framework without estimating any unknown parameter, dealing with the negative effects of environmental variability, improving the performance of Mahalanobis distance, and increasing damage detectability. The performance and effectiveness of this method are validated by modal features of two real bridge structures along with several comparisons. Results demonstrate that the proposed ensemble learning-based method highly succeeds in detecting damage under environmental variability, and it is superior to some state-of-the-art techniques.

KeywordDamage Detection Ensemble Learning Environmental Variability Mahalanobis Distance Structural Health Monitoring Unsupervised Learning
URLView the original
Indexed BySCIE
WOS Research AreaConstruction & Building Technology ; Engineering ; Instruments & Instrumentation
WOS SubjectConstruction & Building Technology ; Engineering, Civil ; Instruments & Instrumentation
WOS IDWOS:000590822400001
Scopus ID2-s2.0-85094138464
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Cited Times [WOS]:44   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionFaculty of Science and Technology
Corresponding AuthorHassan Sarmadi
Affiliation1.Department of Civil Engineering,Faculty of Engineering,Ferdowsi University of Mashhad,Mashhad,Iran
2.Head of Research and Development,IPESFP Company,Mashhad,Iran
3.Department of Construction and Mineral Engineering,Faulty of Technical and Engineering,Standard Research Institute,Tehran,Iran
4.State Key Laboratory of Internet of Things for Smart City and Department of Civil and Environmental Engineering,University of Macau,Macao
Recommended Citation
GB/T 7714
Hassan Sarmadi,Alireza Entezami,Behzad Saeedi Razavi,et al. Ensemble learning-based structural health monitoring by Mahalanobis distance metrics[J]. Structural Control and Health Monitoring,2020,28(2).
APA Hassan Sarmadi,Alireza Entezami,Behzad Saeedi Razavi,&Ka-Veng Yuen.(2020).Ensemble learning-based structural health monitoring by Mahalanobis distance metrics.Structural Control and Health Monitoring,28(2).
MLA Hassan Sarmadi,et al."Ensemble learning-based structural health monitoring by Mahalanobis distance metrics".Structural Control and Health Monitoring 28.2(2020).
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