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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Vydáno v:Composites. Part C, Open access Ročník 15; s. 100529
Hlavní autoři: 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
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier B.V 01.10.2024
Elsevier
Témata:
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
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Shrnutí:•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 concrete (SFRC) has replaced traditional concrete in the construction sector, improving fracture resistance and post-cracking performance. However, extreme temperatures degrade concrete's material characteristics including stiffness and strength. The construction industry increasingly embraces machine learning (ML) to estimate concrete properties and optimize cost and time accurately. This study employs independent ML methods, gene expression programming (GEP), multi-expression programming (MEP), XGBoost, and Bayesian estimation model (BES) to predict SFRC compressive strength (CS) at high temperatures. 307 experimental data points from published studies were utilized to develop the models. The models were trained using 70 % of the dataset, with 15 % for validation and 15 % for testing. Iterative hyperparameter adjustment and trial-and-error refining achieved optimum predictions. All the models were evaluated using correlation (R) values for training, validation, and testing datasets. MEP showed slightly lower R-values of 0.923, 0.904, and 0.949 than GEP, which performed consistently with 0.963, 0.967, and 0.961. XGBoost had the greatest training R-value of 0.997 but dropped in validation (0.918) and testing (0.896). BES model exhibited commendable performance with scores of 0.986, 0.944, and 0.897. GEP and XGBoost exhibited great accuracy, with GEP sustaining constant accuracy across all datasets, highlighting its potency in predicting CS. Interpreting model predictions using SHapley Additive exPlanation (SHAP) highlighted temperature over heating rate. CS improved significantly as the steel fiber volume fraction (Vf) reached 1.5 %, plateauing thereafter. The proposed models are valid and accurate, providing designers and builders with a practical and adaptable method for estimating strength in SFRC structural applications, particularly under high-temperature conditions.
ISSN:2666-6820
2666-6820
DOI:10.1016/j.jcomc.2024.100529