Bayesian Model Updating for Structural Dynamic Applications Combing Differential Evolution Adaptive Metropolis and Kriging Model
Publication: Journal of Structural Engineering
Volume 149, Issue 6
Abstract
The Bayesian model updating approach has attracted much attention by providing the most probable values (MPVs) of physical parameters and their uncertainties. However, the Bayesian approach has challenges in high-dimensional problems and requires high computational costs in large-scale engineering structures dealing with structural dynamics. This study proposes a new Bayesian updating framework using the Differential Evolution Adaptive Metropolis (DREAM) algorithm to enhance the Bayesian approach’s performance and computational efficiency. In addition, two time-saving strategies are proposed. Firstly, variance-based global sensitivity analysis is used to eliminate insignificant parameters to model responses and reduce model dimensionality. Secondly, a fast-running kriging model is employed as a surrogate of the time-consuming finite-element (FE) model to alleviate the computational burden. DREAM essentially is a multichain sampling method that runs different paths to seek all possible solutions and accurately approximate the posterior probability distribution function in the Bayesian approach. The proposed updating framework was demonstrated using one numerical example and a real-world cable-stayed pedestrian bridge. The results showed that the proposed method enables rationally identifying structural parameters and recovering dynamic responses. Compared with the traditional Bayesian approach without a surrogate model, the computational cost is orders of magnitude lower.
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Data Availability Statement
Data, models, and codes generated and used during the study are available from the corresponding author upon reasonable request. A MATLAB program of DREAM related to model updating for a simple structure in SHM is provided freely ion GitHub at https://github.com/Jice1991/DREAM-for-model-updating-for-SHM.
Acknowledgments
The authors express their gratitude and sincere appreciation for the partial financial support by the University of Louisville. The authors also thank the anonymous reviewers whose comments helped improve the quality of this article.
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Information & Authors
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Published In
Journal of Structural Engineering
Volume 149 • Issue 6 • June 2023
Copyright
© 2023 American Society of Civil Engineers.
History
Received: Aug 25, 2021
Accepted: Jan 6, 2023
Published online: Apr 13, 2023
Published in print: Jun 1, 2023
Discussion open until: Sep 13, 2023
ASCE Technical Topics:
- Adaptive systems
- Analysis (by type)
- Bayesian analysis
- Continuum mechanics
- Dynamic models
- Dynamics (solid mechanics)
- Engineering fundamentals
- Engineering mechanics
- Kriging
- Mathematics
- Models (by type)
- Parameters (statistics)
- Physical models
- Sensitivity analysis
- Solid mechanics
- Statistical analysis (by type)
- Statistics
- Structural dynamics
- Structural models
- Systems engineering
- Systems management
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