An interpretable ensemble-learning-based open source model for evaluating the fire resistance of concrete-filled steel tubular columns. (1st November 2022) - Zhao, Xin-Yu; Chen, Jin-Xin; Wu, Bo
Highlights: An explainable ML model is proposed for predicting the fire resistance of concrete-filled steel tubular columns. Balancing composite motion optimization algorithm is used to figure out the configuration of the model. Model-agnostic approaches are applied to elucidate the underlying physical mechanisms of the model. Two explicit fire-resistance design equations are also presented. Abstract: A new simulation model rooted in explainable, pragmatic machine learning theories is proposed which simply and accurately predicts the fire resistance of concrete-filled steel tubular (CFST) columns. The XGBoost, an ensemble-learning capable method, was used to formulate the model and its key hyper-parameters were fine-tuned using the meta-heuristic balancing composite motion optimization (BCMO) algorithm. Model-agnostic approaches were applied to elucidate the underlying physical mechanisms of the developed model. Two explicit fire-resistance design equations derived from generalized linear model and genetic expression programming are also presented and their accuracy is shown to surpass that of conventional fire-resistance prediction methods. Finally, a preliminary reliability analysis based on the proposed model is conducted. A convenient graphic user interface together with all source code files are provided for practical and academic use.