LGBM-based modeling scenarios to compressive strength of recycled aggregate concrete with SHAP analysis

Abstract

Concrete production contributes significantly to global greenhouse gas emissions, and its manufacture requires substantial natural resources. These concerns can be partly mitigated by recycling construction demolition waste as aggregates to produce Recycled Aggregate Concrete (RAC). RAC has gained momentum due to its lower environmental impact, production costs, and increased sustainability. The aim of this study was to advance the reasonable use of recycled aggregate in concrete and achieve optimal mixture ratio design. Four advanced machine learning algorithms, Support Vector Machine (SVR), Light Gradient Boosting Machine (LGBM), Random Forest (RF), and Multi-Layer Perceptron (MLP), were employed, and the novel optimization algorithms, biogeography-based optimization (BBO), Multi-Verse Optimizer (MVO) and Gravitational Search Algorithm (GSA), were integrated to to predict the compressive strength of RAC. Six potential influential factors for RAC strength were considered in the models. The study employed four evaluation metrics, Taylor diagrams and Regression Error Characteristic plots to compare model performance. The result shows LGBM-based hybrid model outperformed other methods, demonstrating high accuracy in predicting compressive strength. The Shapley Additive Explanation (SHAP) results emphasize the importance of understanding the interactions between the various factors and their effects on the mechanical properties of the RAC. The findings can inform the development of more sustainable and environmentally friendly building materials.