Reliability prediction of the axial ultimate bearing capacity of piles: A hierarchical Bayesian method

The sea reclamation is one of the efficient ways to alleviate the shortage of land resources due to population growth, and the corresponding axial ultimate bearing capacity of piles has become one of the critical factors for evaluating the performance of the soil layer reclamation work. Many models are used to analyze the testing data. However, these models cannot describe the mean population bearing capacity and unit-to-unit variation simultaneously, and they cannot give the reliability of predicting the axial ultimate bearing capacity of piles. Thus, they are rarely used in practice. In this article, we propose a mixed-effects model, which could overcome the drawback of the models in the literature. A hierarchical Bayesian framework is developed to estimate the model parameters using Gibbs sampling. The proposed model is applied to a real pile dataset collected in silt-rock layer area, and we predict the mean axial bearing capacities under different reliability levels.