Fatemeh Mobasheri1, Masoud Hosseinpoor1,*, Ammar Yahia1,2, Farhad Pourkamali-Anaraki3
CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 3309-3347, 2025, DOI:10.32604/cmes.2025.072271
- 23 December 2025
Abstract Self-consolidating concrete (SCC) is an important innovation in concrete technology due to its superior properties. However, predicting its compressive strength remains challenging due to variability in its composition and uncertainties in prediction outcomes. This study combines machine learning (ML) models with conformal prediction (CP) to address these issues, offering prediction intervals that quantify uncertainty and reliability. A dataset of over 3000 samples with 17 input variables was used to train four ensemble methods, including Random Forest (RF), Gradient Boosting Regressor (GBR), Extreme gradient boosting (XGBoost), and light gradient boosting machine (LGBM), along with CP techniques, More >
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