Advancing Mix-Design Prediction in 3D Printed Concrete (2024-07)¶

Introducing 3D-concrete printing has started a revolution in the construction industry, presenting unique opportunities alongside undeniable challenges. Among these, the major challenge is the iterative process associated with mix design formulation, which results in significant material and time consumption. This research uses machine learning (ML) techniques such as Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), Decision Tree Regression (DTR), Gaussian Process Regression (GPR), and Artificial Neural Network (ANN) to overcome these challenges. A dataset containing 21 mix constituent features and 4 output properties (cast and printed compressive strength, and slump flow) was extracted from the literature to investigate the relationship between mix design and performance. The models were assessed using a range of evaluation metrics, including Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Mean Squared Error (MSE), and R-squared value. Gaussian Process Regression (GPR) yielded more favorable results. In the case of cast strength, GPR achieved an R2 value of 0.9069, along with RMSE, MSE, and MAE values of 13.04, 170.12, and 9.40, respectively. A similar trend was observed for printed strengths in directions 1, 2, and 3. GPR achieved R-squared values exceeding 0.91 for all directions, accompanied by significantly lower RMSE values (below 4.1). The machine learning models were also validated using four unique mix designs. These mixes were 3D printed and tested for compressive strength and slump flow. GPR's average error was 10.55%, while SVM achieved a slightly lower average error of 9.38%. Overall, this work presents a novel approach for optimizing 3D-printed concrete by enabling the prediction of slump flow and compressive strength directly from the mix design. This approach can facilitate the design and fabrication of 3D-printed concrete structures that fulfill the necessary strength and printability requirements.