基于主动学习Kriging模型的地下管线抗震可靠度分析

Seismic reliability analysis of buried segmented pipelines based on active learning Kriging model

摘要:

为提升地下承插式接口铸铁管线抗震可靠度的计算效率,提出基于主动学习Kriging代理模型的Monte Carlo模拟方法（AK-MCS）进行地下管线抗震可靠度计算。采用梁单元模拟管线结构,均布弹簧反映管土相互作用,接口弹簧模拟邻接管道约束作用,建立了地下管线地震响应分析模型；考虑管线接口允许位移、管线埋深、土体容重和内摩擦角等模型参数随机性的影响,以管线接口位移量为安全准则,采用主动学习Kriging模型方法建立管线接口位移响应与随机变量参数关系的代理模型,从而获得管线接口安全状态。算例结果表明,AK-MCS法计算得到的管线失效概率与传统Monte Carlo模拟计算的结果相对误差在5%以内,且AK-MCS法计算时间约为传统Monte Carlo模拟计算时间的2%。因此在进行管线可靠度计算时,主动学习Kriging代理模型方法具有准确性与高效性。

Abstract:

In order to improve the calculation efficiency for seismic reliability of cast iron pipelines with bell-and-spigot joints, a Monte Carlo simulation method based on active learning Kriging model (AK-MCS) was proposed to calculate the seismic reliability of buried pipelines. A seismic response analysis model for buried pipeline system was established, in which the beam element was adopted to simulate the pipeline structure, and soil spring and joint spring were used to simulate the pipe-soil interaction and the constraint of adjacent pipe segments respectively. Considering the randomness of critical parameters such as the allowable displacement of pipeline joints, the depth of the pipeline, the unit weight, and the friction angle of the surrounding soil, based on the safety criteria of the allowable relative joint displacement, the active learning Kriging method was used to establish a surrogate model between the relative displacement of the pipeline joint and the random parameters. The safety margin of the pipeline joint was subsequently obtained through the validated surrogate model. Numerical results show that the relative difference of the pipeline failure probability between AK-MCS method and standard MCS method was less than 5%, and the computational time of the AK-MCS method was only about 2% of the standard MCS method. Therefore, the proposed AK-MCS method is an efficient alternative to evaluate the seismic reliability of buried pipelines.