Buy article PDF
The purchased file will be sent to you
via email after the payment is completed.
US$ 35
|
Smart Structures and Systems Volume 29, Number 2, February 2022 , pages 321-336 DOI: https://doi.org/10.12989/sss.2022.29.2.321 |
|
|
|
Developing efficient model updating approaches for different structural complexity - an ensemble learning and uncertainty quantifications |
||
Guangwei Lin, Yi Zhang and Qinzhuo Liao
|
||
| 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 lowdimensional 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. | ||
| Key Words | ||
| kriging; Markov chain Monte Carlo; model updating; polynomial chaos expansion; structural complexity | ||
| Address | ||
| (1) Guangwei Lin, Yi Zhang: Key Laboratory of Earthquake Engineering and Engineering Vibration, Institute of Engineering Mechanics, China Earthquake Administration, No. 63, Fuxing Road, Haidian District, Beijing, China; (2) Guangwei Lin, Yi Zhang: Department of Civil Engineering, Tsinghua University, Beijing, China; (3) Qinzhuo Liao: Department of Petroleum Engineering, CPG, King Fahd University of Petroleum & Minerals, Dhahran, Saudi Arabia. | ||