AI-guided design of low-carbon high-packing-density self-compacting concrete

Self-compacting concrete (SCC) has gained substantial traction in modern engineering due to its exceptional fresh and hardened properties. However, traditional SCC design methods encounter significant challenges. Conventional experimental design approaches often necessitate a considerable number of trial mixes to fulfill diverse performance objectives, incurring escalated material, time, and labor costs. Additionally, conventional SCC designs tend to use high cementitious material content, leading to elevated carbon emissions and energy consumption compared to ordinary concrete. To address these issues, this study proposes a novel approach that combines the compressible packing model (CPM) with machine learning (ML) techniques. This approach innovatively utilizes particle packing theory to guide ML in optimizing SCC aggregate grading and mix proportions. By integrating physical principles into artificial intelligence, this approach facilitates the intelligent design of low-carbon, high-packing-density SCC. Compared to the conventional method, SCC designed using the innovative AI approach demonstrates a 57.2% reduction in embodied carbon emissions.

Automated summary

Self-compacting concrete (SCC) has gained popularity in modern engineering, but traditional design methods face challenges such as high material and labor costs, as well as elevated carbon emissions and energy consumption. To address these issues, this study proposes a novel approach that combines the compressible packing model with machine learning, enabling the intelligent design of low-carbon, high-packing-density SCC.