Optimization of cracking-resistant manufactured sand concrete using AI and least paste theory

Due to the shortage of natural sand, manufactured sand concrete (MSC) is widely used owing to its sustainability benefits, but optimizing its mix proportion design remains a challenge. In this paper, the least paste theory is adopted to optimize the cracking resistance of MSC. Artificial neural networks are employed to establish nonlinear relationships between the mix proportion parameters (namely nominal water-cement ratio, equivalent water-cement ratio, fly ash-binder ratio, slag-binder ratio, stone powder-binder ratio, and average paste thickness) and performance indicators (namely slump, 28 d compressive strength, and 28 d chloride ion diffusion coefficient). This study integrates artificial neural networks and the harmony search algorithm to optimize the mix proportion of manufactured sand concrete, enhancing cracking resistance while minimizing cost and carbon footprint. The findings contribute to the advancement of mix proportion design theory and promote the broader application of MSC in engineering projects.