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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]

The aim of this paper is to present a novel attempt for parametric estimation in the hydrostatic-season-time (HST) model. The empirical HST-model has been widely used for the analysis of different measurement data types on dams. The significance of individual parameters or their sub-groups for modelling the influence of the water level, air and water temperature, and irreversible deformations due to the ageing of the dam, depends on the structure itself. The process of finding an accurate HST-model for a given data set, which remains robust to outliers, cannot only be demanding but also time consuming. The Bayesian model class selection approach imposes a penalisation against overly complex model candidates and admits a selection of the most plausible HST-model according to the maximum value of model evidence provided by the data or relative plausibility within a set of model class candidates. The potential of Bayes interference and its efficiency in an HST-model are presented on geodetic time series as a result of a permanent monitoring system on a rock-fill embankment dam. The method offers high potential for engineers in the decision making process, whilst the HST-model can be promptly adapted to new information given by new measurements and can enhance the safety and reliability of dams. [ABSTRACT FROM AUTHOR]