An interpretable ensemble-learning-based open source model for evaluating the fire resistance of concrete-filled steel tubular columns. (1st November 2022)

• Record Type: Journal Article
• Authors: Zhao, Xin-Yu; Chen, Jin-Xin; Wu, Bo
• Is Part Of: Engineering structures. Volume 270(2022)
• Publication Date: 2022-11-01
• Subjects: Fire resistance -- Concrete-filled steel tubular columns -- Machine learning -- Model-agnostic approaches -- Interpretability; Structural engineering -- Periodicals; Structural analysis (Engineering) -- Periodicals; Construction, Technique de la -- Périodiques; Génie parasismique -- Périodiques; Pression du vent -- Périodiques; Earthquake engineering; Structural engineering; Wind-pressure; Periodicals; 624.105
• DOI: 10.1016/j.engstruct.2022.114886
• Languages: English
• ISSNs: 0141-0296

Abstract

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.