An accurate description of pipeline steel at low temperatures requires a comprehensive understanding of its ductile-brittle transition temperature (DBTT). In this work, we collect a data set of low-temperature toughness for pipeline steel and reduce the dimensionality of the data set using several feature screening approaches. Multiple machine learning models, validated via ten-fold cross-validation, are then employed to fit and predict the DBTT. Symbolic regression allows us to derive a relation for DBTT appropriate for pipeline steel. Such a formula is shown to provide a versatile model to estimate the DBTT of pipeline steel. It not only serves as a guide to predict the low-temperature properties of pipeline steel but also lays the groundwork for further research on other steel materials.

中文翻译：

---

管线钢韧脆转变温度的定量关系

准确描述低温下管线钢需要全面了解其韧脆转变温度（DBTT）。在这项工作中，我们收集了管线钢低温韧性的数据集，并使用多种特征筛选方法降低了数据集的维数。然后使用通过十倍交叉验证验证的多个机器学习模型来拟合和预测 DBTT。符号回归使我们能够推导出适合管线钢的 DBTT 关系。该公式为估算管线钢的 DBTT 提供了一个通用模型。它不仅可以为预测管线钢的低温性能提供指导，而且为其他钢材的进一步研究奠定了基础。