Smart Structures and Systems

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Developing efficient model updating approaches for different structural complexity - an ensemble learning and uncertainty quantifications

  • Lin, Guangwei (Key Laboratory of Earthquake Engineering and Engineering Vibration, Institute of Engineering Mechanics, China Earthquake Administration) ;
  • Zhang, Yi (Key Laboratory of Earthquake Engineering and Engineering Vibration, Institute of Engineering Mechanics, China Earthquake Administration) ;
  • Liao, Qinzhuo (Department of Petroleum Engineering, CPG, King Fahd University of Petroleum & Minerals)
  • Received : 2021.03.31
  • Accepted : 2021.10.20
  • Published : 2022.02.25
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Abstract

Model uncertainty is a key factor that could influence the accuracy and reliability of numerical model-based analysis. It is necessary to acquire an appropriate updating approach which could search and determine the realistic model parameter values from measurements. In this paper, the Bayesian model updating theory combined with the transitional Markov chain Monte Carlo (TMCMC) method and K-means cluster analysis is utilized in the updating of the structural model parameters. Kriging and polynomial chaos expansion (PCE) are employed to generate surrogate models to reduce the computational burden in TMCMC. The selected updating approaches are applied to three structural examples with different complexity, including a two-storey frame, a ten-storey frame, and the national stadium model. These models stand for the low-dimensional linear model, the high-dimensional linear model, and the nonlinear model, respectively. The performances of updating in these three models are assessed in terms of the prediction uncertainty, numerical efforts, and prior information. This study also investigates the updating scenarios using the analytical approach and surrogate models. The uncertainty quantification in the Bayesian approach is further discussed to verify the validity and accuracy of the surrogate models. Finally, the advantages and limitations of the surrogate model-based updating approaches are discussed for different structural complexity. The possibility of utilizing the boosting algorithm as an ensemble learning method for improving the surrogate models is also presented.

Keywords

Acknowledgement

The authors gratefully acknowledge the financial support from Scientific Research Fund of Institute of Engineering Mechanics, China Earthquake Administration (Grant No. 2021D18), National Natural Science Foundation of China (Grand No. 51908324 & 52111540161). The support from Tsinghua University Initiative Scientific Research Program (20213080003) is also greatly appreciated.

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