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