AI-based meta model for predicting the performance of low-carbon concrete, considering the effects of multiple waste materials

Low-carbon concrete incorporating waste materials offers significant environmental benefits while maintaining structural performance. However, designing an optimal mix of these waste materials is challenging due to their potential impact on the concrete properties. To address this challenge, this paper presents a novel meta model that introduces a non-deterministic mix design framework and simultaneously optimizes four performance metrics: environmental (global warming potential), durability (rapid chloride permeability and bulk electrical resistivity), mechanical (compressive strength and splitting tensile strength), and workability (air content and slump). The model is trained using a hybrid dataset combining literature data with response surface methodology (RSM) generated samples. To this end, a Multilayer Perceptron (MLP) neural network is trained to capture the effects of waste materials, including shredded rubber (SR), glass powder (GP), and biomass fly ash (BFA), on concrete performance and is further combined with Monte Carlo simulation to identify optimal mix designs based on specific performance targets. The results demonstrate the AI model's accuracy in predicting concrete performance, as evidenced by statistical measures such as root mean square error (RMSE), mean absolute error (MAE), and the coefficient of determination (R²). This accuracy is further validated by comparing the AI predictions with laboratory concrete mix results. The results indicated that a 23.1% increase in compressive strength and an 83% decrease in chloride ion permeability were achieved by partially substituting 30% GP for cement. The incorporation of 15% BFA consistently reduced slump by 65% and increased air content by 49%. Moreover, the control mix had the highest GWP at 325 kg CO₂-eq/m³. Using 30% GP, 15% BFA, and 15% SR reduced it to 135 kg CO₂-eq/m³, a 41% decrease. Additionally, the back analysis provides optimized mix designs tailored to specific performance constraints. According to the specified target for designing low-carbon, chloride-resistant, and normal strength (45–55 MPa) concrete, a mixture of waste materials with SR = 3.2%, GP = 25.8%, and BFA = 7.4% is proposed by the developed meta model.