Chemistry-informed multi-objective mix design optimization of self-compacting concrete incorporating recycled aggregates

Abstract

One of the primary objectives of modern concrete advancements is to achieve a delicate equilibrium between improving mechanical performance, ensuring economic feasibility, and reducing carbon emissions. In this context, self-compacting concrete incorporating recycled aggregates (referred to herein as RASCC) has gained attention in the pursuit of sustainable construction materials. Nevertheless, concurrently attaining the above three objectives is not an easy feat for this particular type of concrete. In this study, a machine learning model based on the XGBoost algorithm was developed using 368 sample data points to predict the compressive strength of RASCC. To enhance the model’s accuracy, the chemical composition of RASCC’s binding materials, rather than the quantities of constituent materials, was chosen as part of input parameters, giving rise to the term “chemistry-informed” for this model. The model’s interpretability was comprehensively examined using the SHAP library. Then, in conjunction with the machine learning model, the NSGA-II algorithm was leveraged to establish a RASCC auxiliary design system, enabling triple-objective optimization (i.e., strength, cost, and carbon emissions). The findings indicated that the XGBoost-based model achieved superior accuracy in predicting RASCC’s strength compared to an existing neural network-based model. Additionally, using compound contents as inputs imbued the model with chemical significance, further enhancing its accuracy and interpretability. In conclusion, this study presents a plausible and beneficial tool for the efficient, cost-effective, and low-carbon design of RASCC. Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.

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