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

机译:桩的轴向极限承载力的可靠性预测:分层贝叶斯方法:

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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.
机译:填海是缓解人口增长导致土地资源短缺的有效途径之一,相应的桩轴向极限承载力已成为评价土层开垦工作绩效的关键因素之一。许多模型用于分析测试数据。但是,这些模型无法同时描述平均人口承载力和单位间变化,也无法提供预测桩的轴向极限承载力的可靠性。因此,它们很少在实践中使用。在本文中,我们提出了一种混合效应模型,可以克服文献中模型的缺点。开发了一个分级贝叶斯框架,以使用Gibbs采样估计模型参数。将该模型应用于粉砂岩层区域中的真实桩数据集,并预测了不同可靠性水平下的平均轴向承载力。

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