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Smart Structures and Systems Volume 17, Number 6, June 2016 , pages 1017-1030 DOI: https://doi.org/10.12989/sss.2016.17.6.1017 |
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Operational modal analysis of reinforced concrete bridges using autoregressive model |
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Kyeongtaek Park, Sehwan Kim and Marco Torbol
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| Abstract | ||
| This study focuses on the system identification of reinforced concrete bridges using vector autoregressive model (VAR). First, the time series output response from a bridge establishes the autoregressive (AR) models. AR models are one of the most accurate methods for stationary time series. Burg\'s algorithm estimates the autoregressive coefficients (ARCs) at p-lag by reducing the sum of the forward and the backward errors. The computed ARCs are assembled in the state system matrix and the eigen-system realization algorithm (ERA) computes: the eigenvector matrix that contains the vectors of the mode shapes, and the eigenvalue matrix that contains the associated natural frequencies. By taking advantage of the characteristic of the AR model with ERA (ARMERA), civil engineering can address problems related to damage detection. Operational modal analysis using ARMERA is applied to three experiments. One experiment is coupled with an artificial neural network algorithm and it can detect damage locations and extension. The neural network uses a specific number of ARCs as input and multiple submatrix scaling factors of the structural stiffness matrix as output to represent the damage. | ||
| Key Words | ||
| system identification; autoregressive model; Burg | ||
| Address | ||
| Kyeongtaek Park and Marco Torbol: School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology, 50 UNIST-gil, Eonyang-eup, Ulju-gun, Ulsan 44919, Republic of Korea Sehwan Kim: Department of Biomedical Engineering, College of Medicine, Dankook University, 119, Dandae-ro, Dongnam-gu, Cheonan-si, Chungnam 31116, Republic of Korea | ||