Bayesian back analysis considering constraints

TAO Yuan-qin; SUN Hong-lei; CAI Yuan-qiang

doi:10.11779/CJGE202110014

Volume 43 Issue 10

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Chinese Journal of Geotechnical Engineering > 2021 > 43(10): 1878-1886. > DOI: 10.11779/CJGE202110014

TAO Yuan-qin, SUN Hong-lei, CAI Yuan-qiang. Bayesian back analysis considering constraints[J]. Chinese Journal of Geotechnical Engineering, 2021, 43(10): 1878-1886. DOI: 10.11779/CJGE202110014

Citation:

TAO Yuan-qin, SUN Hong-lei, CAI Yuan-qiang. Bayesian back analysis considering constraints[J]. Chinese Journal of Geotechnical Engineering, 2021, 43(10): 1878-1886. DOI: 10.11779/CJGE202110014

Citation:

TAO Yuan-qin, SUN Hong-lei, CAI Yuan-qiang. Bayesian back analysis considering constraints[J]. Chinese Journal of Geotechnical Engineering, 2021, 43(10): 1878-1886. DOI: 10.11779/CJGE202110014

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Bayesian back analysis considering constraints

1.
College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China
2.
College of Civil Engineering, Zhejiang University of Technology, Hangzhou 310014, China

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Received Date: January 13, 2021
Available Online: December 02, 2022

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Abstract

Abstract

Soil parameters significantly affect the prediction performance of geotechnical models. In the field of parameter identification, the MCMC-based Bayesian method is an effective way to infer the probability distribution of soil parameters. However, this method only considers the prior distribution of soil parameters and the difference between predictions and observations, without considering other additional information such as empirical correlations between soil parameters. In addition, the MCMC method leads to high computational cost if the numerical methods are used as the model, which limits its application. In this study, a new approximate Bayesian method considering the additional constraints is proposed, named REnKF-MDA. The proposed method is compared with the MCMC-based Bayesian method, MCMC-based Bayesian method with constraints, and REnKF. The effectiveness of the proposed REnKF-MDA method is illustrated by a simple polynomial case and a foundation settlement project. The results indicate that assimilating the additional constraint informations is helpful to improve the rationality and confidence of the inferred parameters. The confidence of the constraint is determined by the covariance of the constraint information. Taking the MCMC-based Bayesian method with constraints as a reference, the REnKF provides an accurate evaluation of the mean value, but significantly underestimates the uncertainty of the posterior distributions. In contrast, the REnKF-MDA estimates both the mean and uncertainty well.
- Bayesian method,
- parameter identification,
- ensemble Kalman filter,
- constraint information,
- data assimilation

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References (30)

References

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