Evaluation of aggregate segregation in self-consolidating concrete using 3D point cloud analysis

Abstract

Self-consolidating concrete (SCC) is a widely used construction material known for its high workability. In general, the proportion of chemical admixture in SCC is higher than that of standard concrete, making it more susceptible to the segregation due to its low aggregate bearing capacity. To test the aggregate segregation, a variety of qualitative and quantitative methods have been proposed for either fresh or hardened SCC; however, the conventional methods are difficult to accurately investigate the segregation in advance of the construction. This study presents a novel method for evaluating the degree of segregation in fresh SCC at an early stage using point cloud data obtained from scanning the surface of SCC slump flow. The acquired point cloud provides three-dimensional (3D) spatial information, which can be used to calculate various parameters, including diameter, maximum height, and curvature. In particular, the volume of the segregation suspicious region (VSSR) is proposed to quantify the aggregate segregation of the fresh SCC. The reliability of the proposed method is evaluated by comparing the obtained spread diamters with those of the manual inspection, in which the average error is minimal (i.e., approximately 2.4%). Furthermore, the overall performance is compared with standardized qualitative tests and validated through digital image processing on hardened SCC samples, showing good agreement between the VSSR and the degree of segregation obtained from the image analysis. This method offers a comprehensive and efficient tool to assess segregation in the fresh SCC, contributing to improved quality control and optimization of SCC mix designs.