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Smart Structures and Systems Volume 32, Number 1, July 2023 , pages 61-81 DOI: https://doi.org/10.12989/sss.2023.32.1.061 |
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An ensemble learning based Bayesian model updating approach for structural damage identification |
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Guangwei Lin, Yi Zhang, Enjian Cai, Taisen Zhao and Zhaoyan Li
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| Abstract | ||
| This study presents an ensemble learning based Bayesian model updating approach for structural damage diagnosis. In the developed framework, the structure is initially decomposed into a set of substructures. The autoregressive moving average (ARMAX) model is established first for structural damage localization based structural motion equation. The wavelet packet decomposition is utilized to extract the damage-sensitive node energy in different frequency bands for constructing structural surrogate models. Four methods, including Kriging predictor (KRG), radial basis function neural network (RBFNN), support vector regression (SVR), and multivariate adaptive regression splines (MARS), are selected as candidate structural surrogate models. These models are then resampled by bootstrapping and combined to obtain an ensemble model by probabilistic ensemble. Meanwhile, the maximum entropy principal is adopted to search for new design points for sample space updating, yielding a more robust ensemble model. Through the iterations, a framework of surrogate ensemble learning based model updating with high model construction efficiency and accuracy is proposed. The specificities of the method are discussed and investigated in a case study. | ||
| Key Words | ||
| active learning; ensemble of surrogate; model updating; probabilistic ensemble; TMCMC | ||
| Address | ||
| "(1) Guangwei Lin, Yi Zhang, Enjian Cai, Taisen Zhao, Zhaoyan Li: Key Laboratory of Earthquake Engineering and Engineering Vibration, Institute of Engineering Mechanics, China Earthquake Administration, China; (2) Guangwei Lin, Yi Zhang, Enjian Cai, Taisen Zhao, Zhaoyan Li: Key Laboratory of Earthquake Disaster Mitigation, Ministry of Emergency Management, China; (3) Guangwei Lin, Yi Zhang, Enjian Cai, Taisen Zhao: Department of Civil Engineering, Tsinghua University, Beijing, China. | ||