Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Model Updating and Uncertainty Analysis for Creep of Clay
Many researchers have proposed various constitutive models for the purpose of capturing the complex physical mechanisms governing the creep behavior of soft soils. However, the more complex the model, the larger the number of associated uncertain parameters it has, and the less robust it is against modeling/measurement error. In this chapter, the Bayesian model class selection approach is applied to select the most plausible/suitable model describing the creep behavior of soft soil using laboratory measurements. In total, nine 1-D time-dependent constitutive models for the analysis of creep of clay are chosen for the assessment. Consolidated data from the intact samples of Vanttila clay and reconstituted samples of Hong Kong Marine Clay were adopted in the case study. All unknown model parameters are identified simultaneously by adopting the transitional Markov Chain Monte Carlo (TMCMC) method, and their uncertainty is quantified through the posterior probability density functions (PDFs). Engineers can adopt the present probabilistic method to determine the most suitable model and its associated model parameters for a given soft soil for the prediction of long-term creep behavior. The strategy also provides uncertainty evaluation of the model prediction based on the given data.
Model Updating and Uncertainty Analysis for Creep of Clay
Many researchers have proposed various constitutive models for the purpose of capturing the complex physical mechanisms governing the creep behavior of soft soils. However, the more complex the model, the larger the number of associated uncertain parameters it has, and the less robust it is against modeling/measurement error. In this chapter, the Bayesian model class selection approach is applied to select the most plausible/suitable model describing the creep behavior of soft soil using laboratory measurements. In total, nine 1-D time-dependent constitutive models for the analysis of creep of clay are chosen for the assessment. Consolidated data from the intact samples of Vanttila clay and reconstituted samples of Hong Kong Marine Clay were adopted in the case study. All unknown model parameters are identified simultaneously by adopting the transitional Markov Chain Monte Carlo (TMCMC) method, and their uncertainty is quantified through the posterior probability density functions (PDFs). Engineers can adopt the present probabilistic method to determine the most suitable model and its associated model parameters for a given soft soil for the prediction of long-term creep behavior. The strategy also provides uncertainty evaluation of the model prediction based on the given data.
Model Updating and Uncertainty Analysis for Creep of Clay
Zhou, Wan-Huan (Autor:in) / Yin, Zhen-Yu (Autor:in) / Yuen, Ka-Veng (Autor:in)
Practice of Bayesian Probability Theory in Geotechnical Engineering ; Kapitel: 4 ; 89-111
14.11.2020
23 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
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