Abstract Serving as energy lifelines, pipelines remain one of the most efficient and economical ways to move natural resources. The mechanical properties of pipelines installed decades ago decline with time. To maintain the safety of vintage pipelines requires accurate estimation of the strength. This paper focus on the reliability-based strength prediction using nondestructive multimodality information by the method of Bayesian model averaging (BMA). A class of models are formed from all cases of linear combinations of the surface property measurements. The models are averaged based on the posterior model probabilities. Occam’s window is introduced to reduce the number of models under consideration while keeping the predictive accuracy. By not conditioning on any single model, BMA provide more reliable strength prediction by accounting for model uncertainties. In addition, the usefulness of the variables used to predict the strength are evaluated according to the frequency of appearance in the models with high posterior probabilities. The variables with paramount predictive importance can be selected by this way. Thus, BMA method shows advantages in both vintage pipe strength prediction and model selection.

中文翻译：

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使用多模态表面无损测量对老式管道进行概率体积特性估计

摘要 作为能源生命线，管道仍然是运输自然资源的最有效和最经济的方式之一。几十年前安装的管道的机械性能随着时间的推移而下降。为了保持老式管道的安全，需要准确估计强度。本文重点研究利用贝叶斯模型平均（BMA）方法利用无损多模态信息进行基于可靠性的强度预测。一类模型由表面特性测量的所有线性组合情况形成。基于后验模型概率对模型进行平均。引入奥卡姆窗口是为了减少考虑中的模型数量，同时保持预测的准确性。通过不以任何单一模型为条件，BMA 通过考虑模型的不确定性来提供更可靠的强度预测。此外，根据具有高后验概率的模型中出现的频率来评估用于预测强度的变量的有用性。可以通过这种方式选择具有最高预测重要性的变量。因此，BMA 方法在老式管道强度预测和模型选择方面均显示出优势。