Smart Structures and Systems

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An ensemble learning based Bayesian model updating approach for structural damage identification

  • Guangwei Lin (Key Laboratory of Earthquake Engineering and Engineering Vibration, Institute of Engineering Mechanics, China Earthquake Administration) ;
  • Yi Zhang (Key Laboratory of Earthquake Engineering and Engineering Vibration, Institute of Engineering Mechanics, China Earthquake Administration) ;
  • Enjian Cai (Key Laboratory of Earthquake Engineering and Engineering Vibration, Institute of Engineering Mechanics, China Earthquake Administration) ;
  • Taisen Zhao (Key Laboratory of Earthquake Engineering and Engineering Vibration, Institute of Engineering Mechanics, China Earthquake Administration) ;
  • Zhaoyan Li (Key Laboratory of Earthquake Engineering and Engineering Vibration, Institute of Engineering Mechanics, China Earthquake Administration)
  • Received : 2022.12.24
  • Accepted : 2023.07.24
  • Published : 2023.07.25
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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.

Keywords

Acknowledgement

The authors gratefully acknowledge the financial support from the Scientific Research Fund of the Institute of Engineering Mechanics, China Earthquake Administration (Grant No. 2021D18), Visiting Researcher Fund Program of State Key Laboratory of Water Resources and Hydropower Engineering Science (2021SGG01), and Scientific Research Fund of Multi-Functional Shaking Tables Laboratory of Beijing University of Civil Engineering and Architecture.

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