建筑结构学报2026，Vol.47Issue(6)：125-136,12.DOI:10.14006/j.jzjgxb.2026.0026
基于参数化有限元与机器学习的钢筋混凝土柱耐火极限快速预测代理模型研究
Research on a rapid prediction surrogate model for fire resistance of reinforced concrete columns based on parametric finite element and machine learning
摘要
Abstract
The fire resistance limit of columns is a critical factor affecting the overall fire performance of structural systems.Traditional prediction methods generally rely on empirical formulas or finite element analysis(FEA),making it difficult to rapidly obtain relatively precise prediction results.To address these limitations,this study proposed a method for constructing a rapid prediction surrogate model for the fire resistance limit of reinforced concrete(RC)columns under fire,integrating parametric finite element modeling and machine learning.Through the secondary development of ABAQUS using Python,a parametric automatic modeling program and a feature point post-processing system were constructed,efficiently generating 3 600 thermo-mechanical coupled FEA models of RC columns.The reliability of the program and system was validated against existing experimental results.On this basis,taking machine learning as a surrogate for high-fidelity FEA,a rapid prediction surrogate model based on the gradient boosting decision tree(GBDT)core algorithm was established.Through hyperparameter optimization,the optimal parameter combination was identified,achieving rapid multi-objective evaluations for fire resistance time and failure displacement.The results demonstrate that the surrogate model achieves a high coefficient of determination of 0.93 on the test set,with a mean absolute error below 11 minutes,and a 96%classification accuracy for failure states.It significantly outperforms comparative models based on other algorithms while meeting engineering accuracy requirements,significantly improving the analysis efficiency.关键词
钢筋混凝土柱/耐火极限/机器学习/有限元分析/梯度提升树/预测能力Key words
reinforced concrete column/fire resistance limit/machine learning/finite element analysis/gradient boosting decision tree/predictive ability分类
建筑与水利引用本文复制引用
蔡新江,刘建,陈志杰,毛小勇,赵宝成..基于参数化有限元与机器学习的钢筋混凝土柱耐火极限快速预测代理模型研究[J].建筑结构学报,2026,47(6):125-136,12.基金项目
国家自然科学基金项目(52278514,51778395),江苏省高等学校基础科学(自然科学)研究重大项目(24KJA560003),苏州市建设系统科技项目. （52278514,51778395）
