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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Bibliographic Details
Published in:	Composites. Part C, Open access Vol. 15; p. 100529
Main Authors:	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
Language:	English
Published:	Elsevier B.V 01.10.2024 Elsevier
Subjects:	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 Access:	Get full text
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