Fire resistance time prediction and optimization of cold-formed steel walls based on machine learning

Abstract

Many full-scale experiments and numerical studies have been conducted to determine the fire performance of cold-formed steel (CFS) walls, but these studies are expensive and time consuming. This study proposes a machine learning (ML) based framework aiming at accurately predicting the fire resistance time (FRT) and optimizing the design of CFS walls under ISO 834 fire condition. To overcome the limitation of 32 experimental data points in the literature, a validated numerical method was used to generate 592 data points to expand the dataset of CFS walls, considering different wall configurations, various sheathing board types and thicknesses. The XGBoost (eXtreme Gradient Boosting) model was trained with numerical data and tested with experimental data. The hyperparameter tuning of the XGBoost model was implemented with Bayesian optimization, and it was found that the XGBoost model accurately predicted the FRT of CFS walls, with R2 and MAPE values being 0.933 and 8.46 %, respectively. The prediction process of the XGBoost model was interpreted by the SHAP (SHapley Additive exPlanations) method to determine the relative importance of input variables. The NSGA-II algorithm was adopted to optimize the FRT-cost dual-objective of CFS walls and the Pareto front including optimal solutions was emerging. The cost for each solution of Pareto front sets was lower than that in the real dataset at same FRT level. This phenomenon implied that the ML-based optimization framework successfully identified the cost-efficient design process. The proposed ML-based optimization framework offers a promising alternative for engineers to design CFS walls effectively with both mechanical and economic objectives.