Deep learning-based image analysis for confirming segregation in fresh self-consolidating concrete

Abstract Segregation in self-consolidating concrete (SCC) can significantly impact the quality and structural integrity of concrete applications. Traditional methods for assessing segregation, such as the visual stability index and column segregation tests, often involve manual intervention, introducing subjectivity and delaying the assessment process. This study proposes a novel image-based approach using deep learning, specifically the YOLOv8 segmentation model, to quantify and assess segregation in fresh SCC mixes. Utilizing high-resolution images from slump flow tests, the model identifies critical indicators of segregation, including the mortar halo and aggregate pile. These features are evaluated with two newly introduced quantitative metrics: the mortar halo index ( I mh ) and the aggregate pile index ( I ap ). Experimental validation demonstrates high model precision (96.4%) and recall (85.6%), establishing it as a robust tool for on-site quality control. Furthermore, the study examines the relationship between segregation levels and compressive strength, revealing a strong correlation between increased segregation and reduced strength. The proposed feedback-based optimization strategy for mix proportions enables real-time adjustments to mitigate segregation risks. This approach enhances the objectivity and efficiency of segregation assessments, facilitating improved mix design and overall concrete performance on construction sites.