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Consistent and coherent treatment of uncertainties and dependencies in fatigue crack growth calculations using multi-level Bayesian models

机译：使用多级贝叶斯模型对疲劳裂纹增长计算的不确定因素和依赖性的一致和相干性

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摘要

Engineers perform fatigue assessments to support structural integrity management. Given that the purpose of these calculations is linked to problems of decision making under various sources of uncertainty, probabilistic methods are often more useful than deterministic alternatives. Guidance on the direct probabilistic application of procedures in existing industrial standards is currently limited and dependencies between marginal probabilistic models are generally not considered, despite their potential significance being acknowledged. This paper proposes the use of Bayesian data analysis as a flexible and intuitive approach to coherently and consistently account for uncertainty and dependency in fatigue crack growth rate models. Various Bayesian models are established and presented, based on the same data as the existing models in BS 7910 (a widely used industrial standard). The models are compared in terms of their out of sample predictive accuracy, using methods with a basis in information theory and cross-validation. The Bayesian models exhibit an improved performance, with the most accurate predictions resulting from multi-level (hierarchical) models, which account for variation between constituent test datasets and partially pool information.
机译：工程师对支持结构完整性管理进行疲劳评估。鉴于这些计算的目的与各种不确定性来源下决策的问题相关联，概率方法通常比确定性替代品更有用。关于现有工业标准程序的直接概率应用的指导目前是有限的，并且通常不考虑边际概率模型之间的依赖性，尽管存在潜在的意义。本文提出使用贝叶斯数据分析作为灵活而直观的方法，即连贯，一贯地占疲劳裂纹增长模型的不确定性和依赖性。根据BS 7910中的现有模型（广泛使用的工业标准），基于与现有模型相同的数据建立和呈现各种贝叶斯模型。根据在信息理论和交叉验证的基础上使用方法，将模型与样品预测准确性进行比较。贝叶斯模型表现出改进的性能，具有由多级（分层）模型产生的最准确的预测，其中包括组成测试数据集和部分池信息之间的变化。

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