A Nonparametric Method for Identifying Structural Damage in Bridges Based on the Best-Fit Auto-Regressive Models
Article
Khuc, Tung, Nguyen, Phat Tien, Nguyen, Andy and Catbas, F. Necati. 2020. "A Nonparametric Method for Identifying Structural Damage in Bridges Based on the Best-Fit Auto-Regressive Models." International Journal of Structural Stability and Dynamics. 20 (10), pp. 1-17. https://doi.org/10.1142/S0219455420420122
| Article Title | A Nonparametric Method for Identifying Structural Damage in Bridges Based on the Best-Fit Auto-Regressive Models |
|---|---|
| ERA Journal ID | 3766 |
| Article Category | Article |
| Authors | Khuc, Tung (Author), Nguyen, Phat Tien (Author), Nguyen, Andy (Author) and Catbas, F. Necati (Author) |
| Journal Title | International Journal of Structural Stability and Dynamics |
| Journal Citation | 20 (10), pp. 1-17 |
| Article Number | 2042012 |
| Number of Pages | 17 |
| Year | 2020 |
| Publisher | World Scientific Publishing |
| Place of Publication | Singapore |
| ISSN | 0219-4554 |
| 1793-6764 | |
| Digital Object Identifier (DOI) | https://doi.org/10.1142/S0219455420420122 |
| Web Address (URL) | https://www.worldscientific.com/doi/abs/10.1142/S0219455420420122 |
| Abstract | An enhanced method to determine the best-fit Auto-Regressive model (AR model) for structural damage identification is proposed in this paper. Whereby, two parameters of the model, including the number of model order and the window size of data, are analyzed simultaneously in order to accomplish the optimized values by means of Akaike’s Information Criterion (AIC) algorithm. The damage condition of structures can be detected by defined damage indicators obtained from the first three AR coefficients of the best-fit AR models. The ability of the proposed damage identification method is compared with the process that only utilizes conventional AR models without concern of parameter selection. The proposed method is verified using experimental data previously collected from a large-size bridge structure in the Structural Laboratory at the University of Central Florida. The results indicate that this method can detect and locate damage more effectively. |
| Keywords | AR model; AIC; damage identification; bridge health monitoring |
| ANZSRC Field of Research 2020 | 400510. Structural engineering |
| Byline Affiliations | Hanoi University of Civil Engineering, Vietnam |
| School of Civil Engineering and Surveying | |
| University of Central Florida, United States | |
| Institution of Origin | University of Southern Queensland |
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