An Efficient Bayesian Updating Method and Its Application in the Structural Analysis of Underground Tunnels
Publication: Journal of Engineering Mechanics
Volume 150, Issue 10
Abstract
Model updating (i.e., inverse problems) involving high-dimension and nonlinearity plays an increasingly important role in various research fields, e.g., digital twin model updating. Bayesian updating has become a robust and rigorous probabilistic means for parameter uncertainty quantification. However, it remains a challenging task to simultaneously ensure theoretically rigorous and computationally efficient solving of inverse problems involving high-dimensionality and nonlinearity. To address the issue, by applying the basic idea of subset simulation (SuS) to the ensemble Kalman filter (EnKF) that is computationally efficient and applicable to high-dimensional inverse problems, we designed an efficient Monte Carlo method, termed ensemble Kalman filter with subset simulation (EnKF-SuS). In EnKF-SuS, SuS provides a rigorous theoretical basis for guiding EnKF to adaptively search and explore the true target space(s), while avoiding the disadvantages of Markov chain Monte Carlo (MCMC)-based sampling methods by utilizing EnKF to generate updated ensemble samples. The performance of EnKF-SuS was validated and analyzed through three case studies involving multimodal posterior, strongly nonlinear, or high-dimensional problems. Moreover, based on a simplified model for longitudinal response analysis of shield tunnels, we present the application of EnKF-SuS in model updating using centrifuge data and field data, respectively. Results indicate that EnKF-SuS can accurately and efficiently sample from general target posterior distributions, especially in the tested strongly nonlinear or high-dimensional problems, and it is computationally one to two orders of magnitude faster than tested with MCMC-sampling methods.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
Financial support from the Fundamental Research Funds for the Central Universities (Grant No. 226-2022-00196) and the National Natural Science Foundation of China (Grant Nos. 52125803 and 51988101) is gratefully acknowledged. We would like to thank reviewers for taking the time and effort to review the manuscript. We sincerely appreciate all valuable comments and suggestions, which helped us to improve the quality of the paper.
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Information & Authors
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Published In
Journal of Engineering Mechanics
Volume 150 • Issue 10 • October 2024
Copyright
© 2024 American Society of Civil Engineers.
History
Received: Jan 3, 2024
Accepted: May 31, 2024
Published online: Aug 2, 2024
Published in print: Oct 1, 2024
Discussion open until: Jan 2, 2025
ASCE Technical Topics:
- Algorithms
- Analysis (by type)
- Bayesian analysis
- Centrifuge models
- Data analysis
- Engineering fundamentals
- Environmental engineering
- Filters
- Filtration
- Geotechnical engineering
- Kalman filters
- Mathematics
- Methodology (by type)
- Models (by type)
- Nonlinear analysis
- Research methods (by type)
- Statistical analysis (by type)
- Structural analysis
- Structural engineering
- Tunnels
- Water treatment
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