Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)
Published: Feb. 10, 2025
The increasing demand for sustainable construction materials has led to the incorporation of Palm Oil Fuel Ash (POFA) into concrete reduce cement consumption and lower CO₂ emissions. However, predicting compressive strength (CS) POFA-based remains challenging due variability input factors. This study addresses this issue by applying advanced machine learning models forecast CS POFA-incorporated concrete. A dataset 407 samples was collected, including six parameters: content, POFA dosage, water-to-binder ratio, aggregate superplasticizer curing age. divided 70% training 30% testing. evaluated include Hybrid XGB-LGBM, ANN, Bagging, LSSVM, GEP, XGB LGBM. performance these assessed using key metrics, coefficient determination (R2), root mean square error (RMSE), normalized means (NRMSE), absolute (MAE) Willmott index (d). XGB-LGBM model achieved maximum R2 0.976 lowest RMSE, demonstrating superior accuracy, followed ANN with an 0.968. SHAP analysis further validated identifying most impactful factors, ratio emerging as influential. These predictive offer industry a reliable framework evaluating concrete, reducing need extensive experimental testing, promoting development more eco-friendly, cost-effective building materials.
Language: Английский
Citations
0A new financial risk prediction model based on deep learning and quasi-oppositional coot algorithm
Alexandria Engineering Journal, Journal Year: 2024, Volume and Issue: 108, P. 60 - 69
Published: July 23, 2024
Language: Английский
Citations
3Inversion of image-only intrinsic parameters for steel fibre concrete under combined rate-temperature conditions: An adaptively enhanced machine learning approach
Journal of Building Engineering, Journal Year: 2024, Volume and Issue: 94, P. 109836 - 109836
Published: June 13, 2024
Language: Английский
Citations
2An Improved Dung Beetle Optimization Algorithm
Published: April 12, 2024
Language: Английский
Citations
0