Adaptive Sampling-Based Bayesian Model Updating for Bridges Considering Substructure Approach
Publication: ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
Volume 9, Issue 3
Abstract
During long-term bridge monitoring, model updating is necessary because it provides the basis for accurate condition assessment and damage detection. In this study, an adaptive sampling-based Bayesian model updating method for bridges is developed considering a substructure approach. First, the substructuring method is considered to solve eigenvalue problems. By reducing the size of the characteristic equations, the substructure approach overcomes poor algorithm performance, nonconvergence of results, and inefficient model updating caused by the large number of updated parameters when updating a large-scale system. Then Bayesian model updating is applied to quantify the uncertainty existing in bridge model updating and to obtain the posterior probability density function (PDF) of updating parameters that can be further used in different fields of engineering. By introducing the affine-invariant ensemble sampler (AIES) to replace the traditional Metropolis-Hastings (MH) sampler, an adaptive transitional Markov chain Monte Carlo algorithm is proposed to obtain the posterior probability of parameters with high efficiency. Application to a bridge structure demonstrates that the proposed method is efficient and useful in engineering problems.
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Data Availability Statement
All data, models, or code generated or used during the study are available from the corresponding author by request.
Acknowledgments
This research was jointly supported by the National Natural Science Foundation of China (Grant Nos. 52250011, 52222807, and 12002224) and Fundamental Research Funds for the Central Universities (Grant Nos. DUT22ZD213 and DUT22QN235).
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Information & Authors
Information
Published In
ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
Volume 9 • Issue 3 • September 2023
Copyright
© 2023 American Society of Civil Engineers.
History
Received: Feb 23, 2023
Accepted: May 12, 2023
Published online: Jun 29, 2023
Published in print: Sep 1, 2023
Discussion open until: Nov 29, 2023
ASCE Technical Topics:
- Adaptive systems
- Algorithms
- Analysis (by type)
- Bayesian analysis
- Bridge engineering
- Bridge tests
- Engineering fundamentals
- Field tests
- Mathematics
- Model accuracy
- Models (by type)
- Parameters (statistics)
- Statistical analysis (by type)
- Statistics
- Structural engineering
- Structures (by type)
- Substructures
- Systems engineering
- Systems management
- Tests (by type)
Authors
Metrics & Citations
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