Explainable Ensemble Learning Models for the Rheological Properties of Self-Compacting Concrete

نویسندگان

چکیده

Self-compacting concrete (SCC) has been developed as a type of capable filling narrow gaps in highly reinforced areas mold without internal or external vibration. Bleeding and segregation SCC can be prevented by the addition superplasticizers. Due to these favorable properties, adopted worldwide. The workability is closely related its yield stress plastic viscosity levels. Therefore, accurate prediction certain advantages. Predictions shear presented current study using four different ensemble machine learning techniques: Light Gradient Boosting Machine (LightGBM), Extreme (XGBoost), random forest, Categorical (CatBoost). A new database containing results slump flow, V-funnel, L-Box tests with corresponding values was curated from literature develop models. performances algorithms were compared state-of-the-art statistical measures accuracy. Afterward, output interpreted help SHapley Additive exPlanations (SHAP) analysis individual conditional expectation (ICE) plots. Each input variable’s effect on predictions model their interdependencies have illustrated. Highly could achieved coefficient determination greater than 0.96 for both viscosity.

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ژورنال

عنوان ژورنال: Sustainability

سال: 2022

ISSN: ['2071-1050']

DOI: https://doi.org/10.3390/su142114640