The interest in probabilistic methodologies to demonstrate structural fire safety has increased significantly in recent times. However, the evaluation of the structural behavior under fire loading is computationally expensive even for simple structural models. In this regard, machine learning-based surrogate modeling provides an appealing way forward. Surrogate models trained to simulate the behavior of structural fire engineering (SFE) models predict the response at negligible computational expense, thereby allowing for rapid probabilistic analyses and design iterations. Herein, a framework is proposed for the probabilistic analysis of fire exposed structures leveraging surrogate modeling. As a proof-of-concept a simple (analytical) non-linear model for the capacity of a concrete slab and an advanced (numerical) model for the capacity of a concrete column are considered. First, the procedure for training surrogate models is elaborated. Subsequently, the surrogate models are developed, followed by a probabilistic analysis to evaluate the probability density functions for the capacity. The results show that fragility curves developed based on the surrogate model agree with those obtained through direct sampling of the computationally expensive model, with the 10^–2 capacity quantile predicted with an error of less than 5%. Moreover, the computational cost for the probabilistic studies is significantly reduced by the adoption of surrogate models.

近年来，对证明结构防火安全性的概率方法的兴趣显着增加。但是，即使是简单的结构模型，在火荷载下对结构行为的评估在计算上也是昂贵的。在这方面，基于机器学习的代理建模提供了一种有吸引力的方法。经过培训的代理模型可以模拟结构消防工程（SFE）模型的行为，从而以可忽略的计算量预测响应，从而可以进行快速的概率分析和设计迭代。在此，提出了一种利用替代模型对火灾暴露结构进行概率分析的框架。作为概念验证，考虑了混凝土板承载力的简单（分析）非线性模型和混凝土柱承载力的高级（数字）模型。首先，阐述了训练替代模型的程序。随后，开发替代模型，然后进行概率分析以评估容量的概率密度函数。结果表明，基于替代模型开发的脆弱性曲线与通过直接采样计算昂贵的模型获得的脆弱性曲线相符，其中10^–2个容量分位数，预测的误差小于5％。此外，通过采用替代模型可以显着降低概率研究的计算成本。