针对基于Kriging模型的主动学习可靠性方法(active learning Kriging-Monte Carlo simulation,AK-MCS)中,Kriging预测方差低估导致评估过程存在选点和停止判断失误的问题,提出一种基于Bootstrap Kriging的主动学习可靠性方法(Bootstrapp...针对基于Kriging模型的主动学习可靠性方法(active learning Kriging-Monte Carlo simulation,AK-MCS)中,Kriging预测方差低估导致评估过程存在选点和停止判断失误的问题,提出一种基于Bootstrap Kriging的主动学习可靠性方法(Bootstrapped active learning Kriging-Monte Carlo simulation,BAK-MCS).首先拟合真实功能函数初始Kriging模型,然后使用Bootstrap Kriging方差构造BU学习函数,从而进行主动序贯采样更新Kriging模型,最后采用收敛Kriging模型结合蒙特卡罗模拟(Monte Carlo simulation,MCS)估计结构失效概率.数值算例结果表明,与MCS和AK-MCS方法相比,BAK-MCS在保持高预测精度的同时减少了真实功能函数的调用次数,提高了可靠性评估建模效率.展开更多
This paper is devoted to the probabilistic stability analysis of a tunnel face excavated in a two-layer soil. The interface of the soil layers is assumed to be positioned above the tunnel roof. In the framework of lim...This paper is devoted to the probabilistic stability analysis of a tunnel face excavated in a two-layer soil. The interface of the soil layers is assumed to be positioned above the tunnel roof. In the framework of limit analysis, a rotational failure mechanism is adopted to describe the face failure considering different shear strength parameters in the two layers. The surrogate Kriging model is introduced to replace the actual performance function to perform a Monte Carlo simulation. An active learning function is used to train the Kriging model which can ensure an efficient tunnel face failure probability prediction without loss of accuracy. The deterministic stability analysis is given to validate the proposed tunnel face failure model. Subsequently, the number of initial sampling points, the correlation coefficient, the distribution type and the coefficient of variability of random variables are discussed to show their influences on the failure probability. The proposed approach is an advisable alternative for the tunnel face stability assessment and can provide guidance for tunnel design.展开更多
文摘针对基于Kriging模型的主动学习可靠性方法(active learning Kriging-Monte Carlo simulation,AK-MCS)中,Kriging预测方差低估导致评估过程存在选点和停止判断失误的问题,提出一种基于Bootstrap Kriging的主动学习可靠性方法(Bootstrapped active learning Kriging-Monte Carlo simulation,BAK-MCS).首先拟合真实功能函数初始Kriging模型,然后使用Bootstrap Kriging方差构造BU学习函数,从而进行主动序贯采样更新Kriging模型,最后采用收敛Kriging模型结合蒙特卡罗模拟(Monte Carlo simulation,MCS)估计结构失效概率.数值算例结果表明,与MCS和AK-MCS方法相比,BAK-MCS在保持高预测精度的同时减少了真实功能函数的调用次数,提高了可靠性评估建模效率.
基金Projects supported by the China Scholarship Council
文摘This paper is devoted to the probabilistic stability analysis of a tunnel face excavated in a two-layer soil. The interface of the soil layers is assumed to be positioned above the tunnel roof. In the framework of limit analysis, a rotational failure mechanism is adopted to describe the face failure considering different shear strength parameters in the two layers. The surrogate Kriging model is introduced to replace the actual performance function to perform a Monte Carlo simulation. An active learning function is used to train the Kriging model which can ensure an efficient tunnel face failure probability prediction without loss of accuracy. The deterministic stability analysis is given to validate the proposed tunnel face failure model. Subsequently, the number of initial sampling points, the correlation coefficient, the distribution type and the coefficient of variability of random variables are discussed to show their influences on the failure probability. The proposed approach is an advisable alternative for the tunnel face stability assessment and can provide guidance for tunnel design.
