Explainable machine learning based prediction of 28-day compressive strength of recycled aggregate self-compacting concrete using particle packing indicators.
DOI:
https://doi.org/10.21041/ra.v16i3.1024Keywords:
recycled aggregate self-compacting concrete, machine learning, compressive strength prediction, particle packing indicators, explainable artificial intelligence, shap analysis, multicollinearity, sustainable concreteAbstract
This study aims to develop an explainable machine learning framework for predicting the 28-day compressive strength of recycled aggregate self-compacting concrete (RA-SCC) using particle packing indicators. A dataset comprising 365 mixtures from 102 studies was analyzed using six regression models, with SHapley Additive exPlanations (SHAP) applied for interpretability. The Extra Trees model achieved the highest accuracy (R2 = 0.9724, Root Mean Square Error (RMSE) = 1.19 MPa). The dataset is limited to 28-day strength and excludes admixture effects. The study introduces a combined approach integrating particle packing concepts, multicollinearity assessment, and explainable AI. The results demonstrate that machine learning can reliably predict strength while implicitly capturing packing-related mechanisms.
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