This paper proposes a probabilistic framework to calibrate burst strength models of intact and corroded pipelines based on the hierarchical Bayesian method. The approach uses burst test data of intact and corroded pipelines of different steel grades compiled from the literature and accounts for the variations among the data sources. First, the most appropriate burst strength models for corrosion-free and corroded pipelines are adopted. The burst pressure prediction models are categorised under low, medium and high-grade steel classes. Using the hierarchical Bayesian approach model uncertainty factors are derived to calibrate the burst strength models. The mean values and uncertainty of posterior probabilities of the model uncertainty factors are estimated for intact and corroded pipelines in three material categories. This study further investigates the uncertainty propagated by calibrated and non-calibrated models and draws important observations regarding the uncertainty associated with the calibration. The prediction uncertainties follow a non-linear increasing trend as corrosion defect increases. This study's importance is demonstrated with a case study that shows the differences in the uncertainty resulting from the use of the proposed approach compared to the conventional method. Additionally, for corroded pipes, model uncertainty factors are described as a function of defect depth with regression parameters estimated from hierarchical Bayesian-based regression analysis. Finally, a comparison between calibrated and non-calibrated models indicates that the calibrated models provide non-conservative predictions.

中文翻译：

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使用分层贝叶斯方法校准腐蚀管道的爆破强度模型

本文提出了一种基于分层贝叶斯方法来校准完整和腐蚀管道的爆破强度模型的概率框架。该方法使用从文献中汇编的不同钢种的完整和腐蚀管道的爆破测试数据，并考虑了数据源之间的差异。首先，采用最适合免腐蚀和腐蚀管道的爆破强度模型。爆破压力预测模型分为低、中、高档钢。使用分层贝叶斯方法导出模型不确定性因子来校准爆裂强度模型。针对三种材料类别的完整和腐蚀管道，估计模型不确定性因素的后验概率的平均值和不确定性。本研究进一步研究了校准和非校准模型传播的不确定性，并得出了与校准相关的不确定性的重要观察结果。随着腐蚀缺陷的增加，预测不确定性呈非线性增加趋势。这项研究的重要性通过一个案例研究得到了证明，该案例显示了与传统方法相比，使用所提出的方法所产生的不确定性的差异。此外，对于腐蚀的管道，模型不确定性因素被描述为缺陷深度的函数，并通过基于分层贝叶斯的回归分析估计回归参数。最后，校准模型和非校准模型之间的比较表明，校准模型提供了非保守的预测。