Intelligent Prediction of Dynamic Yield Stress in 3D Printing Concrete Based on Machine Learning

Abstract

Applying 3D printing technology to the construction industry can bring many benefits. However, due to the specificity of 3D printing technology, its application in the construction industry has not yet been promoted. Machine learning (ML) techniques, which are popular at this stage, are expected to provide solutions to these problems. Rheological properties have been a key parameter for the quality of 3D printing concrete, and its accurate prediction can help to integrate 3D printing technology into the construction industry. In this study, a GA-RF model for predicting the dynamic yield stress (DYS) of 3D printing concrete is proposed for the first time, and the hyperparameters of the RF model are intelligently tuned during the training process. In addition, the importance analysis of the input parameters is performed. The results show that the developed prediction model has a high accuracy and the SF content has the most significant effect on DYS. The research results help to advance the construction industry to mass production of 3D printing concrete.