Abstract The quality of concrete mixing directly impacts its performance during later stages, yet effective methods for evaluating the mixing uniformity of freshly mixed concrete remain limited. This study proposes a novel method that combines machine vision and multiple fractal theory to assess mixing uniformity. First, a laser 3D scanning instrument is employed to capture the surface contour data of concrete at various mixing times. These contours are then transformed into two‐dimensional images with highly distributed probabilities, facilitating the quantitative analysis of multifractal analysis. The results demonstrate that the unevenness of the concrete surface exhibits multifractal behavior. Notably, there is a significant correlation between the spectral width ∆a as well as the generalized fractal dimensions ( D −15 – D 15 , D −15 – D 0 , and D 1 / D 0 ) and mixing time. Furthermore, the variations in the multifractal indices quantitatively capture changes in the homogeneity of the concrete. This research not only provides a new tool for concrete quality control but also promotes advancements in concrete technology and related fields. Compared to existing methods for hardened materials, this approach is innovative in its real‐time, non‐destructive assessment of mixing uniformity for fresh concrete.
Yang et al. (Thu,) studied this question.