Explainable Metaheuristic-Optimized Ensemble Machine Learning for Compressive Strength Prediction and Sustainable Mix Design of Recycled Powder Mortar

Abstract

Recycled powder mortar (RPM) represents a sustainable cementitious material whose compressive strength (CS) is governed by complex nonlinear interactions among material and mixture parameters. This study proposes an explainable, metaheuristic-optimized ensemble machine learning (ML) framework for accurate prediction and interpretation of CS of RPM. A dataset comprising 204 observations was employed, with recycled powder type (kind), particle size, water/binder (W/B), and mass replacement ratio (MRR) considered as input variables. Extra Trees (ET), Gradient Boosting (GB), and Histogram-Based Gradient Boosting (HGB) were integrated within bagging, voting, and stacking architectures, while hyperparameters were optimized using genetic algorithm (GA), particle swarm optimization (PSO), differential evolution (DE), and grey wolf optimizer (GWO). The dataset was partitioned into 80% training and 20% testing subsets, with model robustness further evaluated through shuffled 10-fold cross-validation. Predictive performance was quantified using R², RMSE, MAE, and MAPE. The GA-optimized ET–GB–HGB stacking model demonstrated superior predictive performance, achieving R² values of 0.949, 0.966, and 0.947, with corresponding RMSE values of 2.149, 1.931, and 2.129 MPa for the training, testing, and 10-fold cross validation datasets, respectively. SHAP and permutation importance consistently established the hierarchy MRR > particle size > kind > W/B, with MRR accounting for more than half of global SHAP importance. Sobol sensitivity and SHAP interaction analyses further confirmed MRR as the dominant factor and particle size–MRR as the strongest coupled interaction. Response-surface analysis additionally elucidated their nonlinear effects on CS. Overall, the proposed framework establishes a robust, interpretable, and data-driven methodology for accurate CS prediction and performance-informed design of sustainable RPM mixtures.