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Of late, various constitutive models have been proposed in the literature for the purpose of capturing the various complex physical mechanisms governing the creep behavior of soft soil. However, the more complex the model, the greater the number of associated uncertain parameters it has, and the less robustness it is. In this study, 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, one elastic plastic (EP) model and eight elastic viscoplastic (EVP) models are investigated. To assess the performance of the different models in the prediction of creep behavior of soft soils, Bayesian model class selection is respectively performed using the oedometer test data from the intact samples of Vanttila clay and reconstituted samples of Hong Kong Marine Clay collected from the literature. All unknown model parameters are identified simultaneously by adopting the transitional Markov Chain Monte Carlo (TMCMC) method, and their uncertainty is quantified through the obtained posterior probability density functions (PDFs). The result shows that the proposed method is an excellent candidate for identifying the most plausible model and its associated parameters for different kinds of soft soils. The approach also provides uncertainty evaluation of the model prediction based on the given data. [ABSTRACT FROM AUTHOR]

Jet grouting column is widely used in ground improvements of hydraulic and geotechnical structures. Estimation of the diameter of the jet grouting column is indispensable to the design of those structures. Existing prediction models of the jet grouting column perform poorly when applied to the recently emerged double and triple-jet grouting systems and cannot quantify the prediction uncertainties. This study has compiled a global-wide jet grouting database and proposed an explicit model that is suitable for all three popular jet grouting systems (i.e., single, double, and triple). The Bayesian inference method was employed to estimate the regression parameters for the proposed model and quantify the prediction uncertainty. The proposed model is compared with a widely used empirical model through a cross-validation procedure. The comparisons show the proposed model is free of systematic errors and has much higher accuracy than the existing method, although the two have similar complexities in terms of the number of unknown regression parameters. The new model can reasonably quantify the prediction uncertainty in the sense that the 95% confidence interval of jet grouting column diameter covers the actual value with a probability of around 95%. To facilitate practice use, analytical formulas have been derived to calculate the statistics of jet grouting column diameter. This unique feature developed here to quantify the prediction uncertainty provides a potentially fundamentally new tool for engineers to estimate the jet grouting column diameter and assess the associated prediction errors. [ABSTRACT FROM AUTHOR]