In maintenance planning of rail track, it is imperative to assess the potential and frequency of rail defects. Although this problem has been mainly studied in the literature by either data‐driven or mechanic‐based models, in the present study a new method is proposed to account for the strengths of both approaches in a single model. The envisaged model incorporates fatigue crack growth model, through Finite Element Modeling (FEM), into Approximate Bayesian Computation (ABC) framework. The method is applied to the prediction of rail defect frequency for transverse defects obtained from a US Class I Railroad. The results of the proposed model show that inducing the mechanics of rail defects into a data‐driven model outperforms the traditional pure data‐driven models by over 20%. The outcome of this study, along with necessary future developments to broaden the scope of applicability of the method, will benefit railroad existing practice in capital and maintenance planning.

中文翻译：

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预测钢轨缺陷频率：使用疲劳建模和数据分析的集成方法

在铁路轨道的维修计划中，必须评估铁路缺陷的可能性和发生频率。尽管在文献中主要通过数据驱动或基于机械的模型研究了此问题，但在本研究中，提出了一种新方法来考虑单个模型中两种方法的优势。设想的模型通过有限元建模（FEM）将疲劳裂纹扩展模型合并到近似贝叶斯计算（ABC）框架中。该方法适用于从美国I级铁路获得的横向缺陷的轨道缺陷频率的预测。该模型的结果表明，将钢轨缺陷的机理引入数据驱动模型的性能要比传统的纯数据驱动模型高20％以上。这项研究的结果，