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

•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.