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 ID | 210809 |
| Article Category | Article |
| Authors | Gharehbaghi, Vahid Reza (Author), Nguyen, Andy (Author), Farsangi, Ehsan Noroozinejad (Author) and Yang, T. Y. (Author) |
| Journal Title | Journal of Building Engineering |
| Journal Citation | 30, pp. 1-16 |
| Article Number | 101292 |
| Number of Pages | 16 |
| Year | 2020 |
| Publisher | Elsevier |
| Place of Publication | United Kingdom |
| ISSN | 2352-7102 |
| 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. |
| Keywords | Autoregressive (AR) time-series; Damage, and deterioration detection; BUFS algorithm; Supervised learning; Ambient vibration; Forced vibration |
| ANZSRC Field of Research 2020 | 400510. Structural engineering |
| Byline Affiliations | Kharazmi University, Iran |
| University of Southern Queensland | |
| Graduate University of Advanced Technology, Iran | |
| University of British Columbia, Canada | |
| Institution of Origin | University of Southern Queensland |
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