Data-driven analysis in 3D concrete printing: predicting and optimizing construction mixtures

Abstract

Accurately predicting 3D concrete printing (3DCP) properties through the utilization of machine learning holds promise for advancing cost-effective, eco-friendly construction practices that prioritize safety, reliability, and environmental sustainability. In this study, a comprehensive exploration of seven regression models was undertaken, complemented by the application of Bayesian optimization techniques to forecast critical metrics such as compressive strength, pump speed, and carbon footprint within the realm of 3DCP technology. Drawing upon a compilation of various 3DCP mixtures sourced from existing literature, an intricate carbon footprint calculation methodology was devised, resulting in the establishment of a bespoke database tailored to the study’s objectives. The performance evaluation of the developed models was conducted through the analysis of key statistical indicators, including R², RMSE, MAE, and Pearson correlation. To enhance the robustness and generalizability of the models, a rigorous tenfold cross-validation strategy coupled with a strategic introduction of noise was employed during the validation process. The incorporation of Shapley Additive Explanations (SHAP) analysis provided insightful interpretability into the predictive capabilities of the models, enabling a nuanced understanding of the underlying relationships between input variables and target outputs. Furthermore, the application of multi-objective optimization techniques facilitated judicious decision-making processes, enabling the identification of optimal 3DCP mixture compositions that concurrently enhance performance metrics, reduce operational costs, and mitigate CO₂ emissions.