Concrete encased steel (CES) columns, also known as steel reinforced concrete (SRC) composite columns, exhibit superior fire resistance due to concrete acting as a protection layer for the embedded steel section. While modern design codes have provided design guides for the fire resistance of CES columns, they are only applicable to those made of normal strength concrete. For high strength CES columns, advanced analysis is needed to capture the brittleness of high strength concrete at elevated temperature. In this paper, two methods, namely the artificial neural network (ANN) and the analytical equations, are proposed to predict the fire resistance of axially-loaded CES columns made of high strength concrete. To train the ANN, a finite difference model is developed to compute the temperature field in CES columns and it is used to establish a database containing 15,200 specimens. The cross-sectional dimensions and materials grades of the specimens are carefully selected to cover a wide range of values including those commonly adopted in real-life applications. The inputs of the ANN are identified through an extensive parametric analysis. The selected ANN consists of 7 inputs, 3 outputs and 2 hidden layers and achieves a high determination coefficient R² value of 0.999. For practical implementation, analytical equations are also derived and achieve high R² values above 0.953. The predictive power of the ANN and the analytical equations are examined against the observations obtained from actual fire tests, showing reasonable accuracy of prediction. Both methods are simple, of high accuracy and have implicitly accounted for temperature-dependent material degradation, and hence do not require input of temperature-dependent material properties and advanced analysis software.

混凝土包裹钢 (CES) 柱，也称为钢筋混凝土 (SRC) 复合柱，由于混凝土充当嵌入式钢截面的保护层，因此具有优异的耐火性。虽然现代设计规范为 CES 柱的耐火性提供了设计指南，但它们仅适用于由普通强度混凝土制成的柱。对于高强度 CES 柱，需要高级分析来捕捉高强度混凝土在高温下的脆性。在本文中，提出了两种方法，即人工神经网络（ANN）和解析方程来预测由高强度混凝土制成的轴向承载 CES 柱的耐火性。为了训练 ANN，开发了有限差分模型来计算CES柱中的温度场，并用于建立包含15,200个样本的数据库。样品的横截面尺寸和材料等级经过精心挑选，以涵盖广泛的值，包括现实生活中常用的值。人工神经网络的输入是通过广泛的参数分析确定的。选定的人工神经网络由 7 个输入、3 个输出和 2 个隐藏层组成，并实现了高决定系数 R²值为 0.999。对于实际实施，还导出了解析方程并实现了高于 0.953 的高 R ²值。ANN 的预测能力和分析方程对照从实际火灾测试中获得的观察结果进行检查，显示出合理的预测准确性。这两种方法都很简单，精度高，并且隐含地考虑了与温度相关的材料降解，因此不需要输入与温度相关的材料属性和高级分析软件。