Framework for rapid characterization of fresh properties of cementitious materials using point cloud and machine learning

Abstract

This study presents a framework that utilizes point cloud analysis and machine learning to automate and accelerate the characterization of fresh properties of cementitious materials. The framework collects point cloud data using a depth camera and extracts diameter, height, and curvature information through post-processing techniques. Data augmentation technique is used to generate new data for ANN training based on nonlinear correlations between these parameters and experimentally determined fresh properties. The developed framework is validated through additional experimental results and shows high prediction accuracy, offering a rapid and effective approach for characterizing fresh properties of cementitious materials.