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Statistical Investigation of Bearing Capacity of Pile Foundation Based on Bayesian Reliability Theory

机译：基于贝叶斯可靠性理论的桩基承载力统计研究

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摘要

In order to improve the estimation accuracy of bearing capacity of pile foundation, a new forecast method of bearing capacity of pile foundation was proposed on Jeffrey's noninformative prior using the MCMC (Markov chain Monte Carlo) method of the Bayesian theory. The proposed approach was used to estimate the parameters of Normal distribution. Numerical simulation was used to produce pseudosamples. The parameter estimation of the maximum likelihood method and the Bayesian statistical theory was used to estimate the parameter estimation of the Normal distribution, which has been compared with the theoretical value of the pseudosample of Normal distribution. The result indicates that the forecast model of Normal distribution using the Bayesian method is better than that of the maximum likelihood method, and the performance of the proposed method was improved with increasing of pseudosample number. At last, the proposed method was applied to estimate the parameter of Normal bearing capacity distribution of pile foundation, which shows that the proposed method has a high precision and good applicability.
机译：为了提高桩基承载力估计准确性，在贝叶斯理论的MCMC（马尔可夫链蒙特卡罗）方法上提出了一种桩基桩基承载力预测方法。所提出的方法用于估计正态分布的参数。数值模拟用于产生伪素质。使用最大似然方法和贝叶斯统计理论的参数估计来估计正常分布的参数估计，该参数估计与正态分布的假伪的理论值进行了比较。结果表明，使用贝叶斯方法的正态分布预测模型优于最大似然方法的预测模型，并且随着伪数的增加，提高了所提出的方法的性能。最后，应用了所提出的方法来估计桩基的正常承载力分布参数，表明该方法具有高精度和良好的适用性。

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  • 来源
    《Advances in civil engineering》 |2019年第8期|9858617.1-9858617.7|共7页
  • 作者

    Luo Zuolong; Dong Fenghui;

  • 作者单位

    Shanxi Univ Dept Civil Engn Taiyuan 030000 Shanxi Peoples R China;

    Nanjing Forestry Univ Coll Civil Engn Nanjing 210037 Jiangsu Peoples R China;

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  • 正文语种 eng
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