Genetic programming-based algorithms application in modeling the compressive strength of steel fiber-reinforced concrete exposed to elevated temperatures

•Development of machine learning models for CS of SFRC exposed to elevated temperature.•Empirical equation has been derived for CS of SFRC exposed to high temperature.•GEP model provided higher accuracy in predicting CS.•SHAP reveals temperature's pivotal role in CS. Steel-fiber-reinforced conc...

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Veröffentlicht in:	Composites. Part C, Open access Jg. 15; S. 100529
Hauptverfasser:	Ali, Mohsin, Chen, Li, Qureshi, Qadir Bux Alias Imran Latif, Alsekait, Deema Mohammed, Khan, Adil, Arif, Kiran, Luqman, Muhammad, Elminaam, Diaa Salama Abd, Hamza, Amir, Khan, Majid
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier B.V 01.10.2024 Elsevier
Schlagworte:	Compressive strength Elevated temperature Machine learning Predictive model SFRC ANN ρ SFRC Elevated temperature SHAP ETs Predictive model GP Compressive strength RMSE CS DT MAE MLR R GEP RF LIME RRMSE Machine learning OF MEP ML
ISSN:	2666-6820, 2666-6820
Online-Zugang:	Volltext
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