An interpretable ensemble-learning-based open source model for evaluating the fire resistance of concrete-filled steel tubular columns

摘要

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 pro-gramming are also presented and their accuracy is shown to surpass that of conventional fire-resistance pre-diction 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.

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