Majority of pipeline infrastructure are old and susceptible to possible catastrophic failures due to fatigue. Timely maintenance is the key to keep pipeline in serviceable and safe condition. This paper proposed a Bayesian inference methodology based on the observed crack growth measurements and cycle data that predicts the probability density of failure after initially estimating the equivalent initial flaw size (EIFS). The model was first developed based on one-dimensional crack growth problem in plate with edge crack. Then the model was expanded to two-dimensional crack growth problem in pipe wall. Stress intensity factors (SIF) at the crack tip in pipe model were calculated using finite element (FE) analysis for different crack lengths and depths. Polynomial function and Gaussian process were used to develop surrogate models of SIF. The analysis demonstrated that the proposed Bayesian inference method with hyperparameters generated accurate inferred results for probability density function (PDF) of both EIFS and the number of cycles to failure.

中文翻译：

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贝叶斯推断方法在等效初始缺陷尺寸下的管道疲劳寿命预测

大部分管道基础设施陈旧，并且容易因疲劳而导致灾难性故障。及时维护是使管道保持可用和安全状态的关键。本文基于观察到的裂纹扩展测量结果和循环数据，提出了一种贝叶斯推理方法，该方法可在初步估算出等效的初始缺陷尺寸（EIFS）之后预测失效的概率密度。该模型首先基于边缘裂纹板的一维裂纹扩展问题而开发。然后将该模型扩展到管壁的二维裂纹扩展问题。对于不同的裂纹长度和深度，使用有限元（FE）分析来计算管道模型中裂纹尖端的应力强度因子（SIF）。使用多项式函数和高斯过程来开发SIF的替代模型。