Supervised damage and deterioration detection in building structures using an enhanced autoregressive time-series approach

Article


Gharehbaghi, Vahid Reza, Nguyen, Andy, Farsangi, Ehsan Noroozinejad and Yang, T. Y.. 2020. "Supervised damage and deterioration detection in building structures using an enhanced autoregressive time-series approach." Journal of Building Engineering. 30, pp. 1-16. https://doi.org/10.1016/j.jobe.2020.101292
Article Title

Supervised damage and deterioration detection in building structures using an enhanced autoregressive time-series approach

ERA Journal ID210809
Article CategoryArticle
AuthorsGharehbaghi, Vahid Reza (Author), Nguyen, Andy (Author), Farsangi, Ehsan Noroozinejad (Author) and Yang, T. Y. (Author)
Journal TitleJournal of Building Engineering
Journal Citation30, pp. 1-16
Article Number101292
Number of Pages16
Year2020
PublisherElsevier
Place of PublicationUnited Kingdom
ISSN2352-7102
Digital Object Identifier (DOI)https://doi.org/10.1016/j.jobe.2020.101292
Web Address (URL)https://www.sciencedirect.com/science/article/pii/S2352710219328876
Abstract

In this paper, a supervised learning approach is introduced for detecting both damage and deterioration in two building models under ambient and forced vibrations. The coefficients and residuals of autoregressive (AR) time-series models are utilized for extracting features through some statistical indices. Moreover, a novel algorithm called best-uncorrelated features selection (BUFS) is proposed and utilized in order to select the most sensitive and uncorrelated features, which are used as predictors. Accordingly, a common set of predictors capable of detecting both damage and deterioration is established and used in order to form a general pattern of the structural condition. Besides, the BUFS algorithm can also be utilized with other features as well as different types of structures and depicts the most sensitive predictors. The results indicate that the proposed method is capable of detecting damage and deterioration in both models precisely, even in a noisy environment, and the appropriate features are introduced.

KeywordsAutoregressive (AR) time-series; Damage, and deterioration detection; BUFS algorithm; Supervised learning; Ambient vibration; Forced vibration
ANZSRC Field of Research 2020400510. Structural engineering
Byline AffiliationsKharazmi University, Iran
University of Southern Queensland
Graduate University of Advanced Technology, Iran
University of British Columbia, Canada
Institution of OriginUniversity of Southern Queensland
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Xiang, Ziru, Chan, Tommy H. T., Thambiratnam, David P. and Nguyen, Andy. 2017. "Prestress and excitation force identification in a prestressed concrete box–girder bridge." Computers and Concrete. 20 (5), pp. 617-625. https://doi.org/10.12989/cac.2017.20.5.617
Toward effective structural identification of medium-rise building structures
Nguyen, A., Kodikara, K. A. T. L., Chan, T. H. T. and Thambiratnam, D. P.. 2018. "Toward effective structural identification of medium-rise building structures." Journal of Civil Structural Health Monitoring. 8 (1), pp. 63-75. https://doi.org/10.1007/s13349-017-0259-y
Modelling techniques for structural evaluation for bridge assessment
Jamali, Shojaeddin, Chan, Tommy H. T., Nguyen, Andy and Thambiratnam, David P.. 2018. "Modelling techniques for structural evaluation for bridge assessment." Journal of Civil Structural Health Monitoring. 8 (2), pp. 271-283. https://doi.org/10.1007/s13349-018-0269-4
Damage detection in asymmetric buildings using vibration-based techniques
Wang, Y., Thambiratnam, D. P., Chan, T. H. T. and Nguyen, A.. 2018. "Damage detection in asymmetric buildings using vibration-based techniques." Structural Control and Health Monitoring. 25 (5), pp. 1-19. https://doi.org/10.1002/stc.2148