Interpretable GA-PSO-optimised deep learning for multi-objective geopolymer concrete strength prediction

The study present an interpretable deep-learning framework, optimized using a hybrid Genetic Algorithm-Particle Swarm Optimization (GA-PSO), to predict and enhance the compressive strength of nano-modified geopolymer concrete (GPC). The framework integrates attention-augmented neural networks with SHAP-based explainability, Monte Carlo dropout uncertainty quantification, and surrogate-assisted multi-objective optimisation to simultaneously maximise strength while minimising cost and embodied CO2 emissions. A curated dataset comprising 234 experimental GPC mixes–incorporating variables such as precursor type, nano-silica dosage, activator content, and curing conditions—was subjected to advanced preprocessing and polynomial feature engineering. A Binary Grey Wolf Optimiser (BGWO) was used for feature selection. The proposed DeepGA-PSO model outperformed conventional regressors (e.g., SVR, Random Forest, XGBoost) with an $$R^2$$ of 0.994 and RMSE of 3.86 MPa. Explainability analyses identified curing regime, sodium hydroxide, and nano-silica content as key predictors. Optimisation via NSGA-II yielded Pareto-optimal mix designs suitable for cost-effective and low-carbon construction. A MATLAB-based GUI was developed to facilitate real-time mix design and prediction. This study offers a robust, scalable, and interpretable pipeline for data-driven GPC optimisation and provides a methodological foundation for intelligent infrastructure materials engineering.