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Problem of Uncertainties in Geotechnical Engineering Read chapter Read first chapter

2021 | OriginalPaper | Chapter

7. Parametric Identification of Advanced Soil Models for Sand

Authors : Wan-Huan Zhou, Zhen-Yu Yin, Ka-Veng Yuen

Published in: Practice of Bayesian Probability Theory in Geotechnical Engineering

Publisher: Springer Singapore

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Abstract

Parametric identification using Bayesian approach is appealing in geotechnical engineering compared to that using deterministic method, because the soil uncertainty can be taken into account. To date, this approach has been verified only for certain conventional simple constitutive models. This chapter presents an enhanced version of the differential evolution transitional Markov chain Monte Carlo (DE-TMCMC) method and a competitive Bayesian parameter identification approach for use in advanced soil models. To realize the intended computational savings, a parallel computing implementation of DE-TMCMC is achieved using the single program/multiple data (SPMD) technique in MATLAB, with comparison of the proposed DE-TMCMC and the original TMCMC for identification of parameters of a critical-state-based sand model from laboratory tests in terms of robustness and effectiveness based on multiple independent calculations. Results indicate that DE-TMCMC is highly robust and efficient in identifying the parameters of advanced soil models. Finally, Bayesian parameter identification is applied in conjunction with DE-TMCMC to identify parameters of an elasto-viscoplastic model from two in situ pressuremeter tests. All results demonstrate the excellent ability of the enhanced Bayesian parameter identification approach to identify soil model parameters from both laboratory and in situ tests.

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previous chapter Model Class Selection for Sand with Generalization Ability Evaluation
next chapter Estimation of Pullout Shear Strength of Grouted Soil Nails
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Metadata
Title
Parametric Identification of Advanced Soil Models for Sand
Authors
Wan-Huan Zhou
Zhen-Yu Yin
Ka-Veng Yuen
Copyright Year
2021
Publisher
Springer Singapore
Book
Practice of Bayesian Probability Theory in Geotechnical Engineering

Print ISBN: 978-981-15-9104-4

Electronic ISBN: 978-981-15-9105-1

Copyright Year: 2021

DOI
https://doi.org/10.1007/978-981-15-9105-1_7