Image-Processing Technology to Evaluate Static Segregation Resistance of Hardened Self-Consolidating Concrete

Abstract

Self-consolidating concrete (SCC) is a highly flowable, nonsegregating concrete that spreads into place, fills formwork, and encapsulates reinforcement without mechanical consolidation. SCC is increasingly being used worldwide because it has been found to offer a high-quality product with significant reductions in equipment use, construction time, labor, and construction noise. The stability, or static segregation resistance, of this new concrete type is typically assessed according to a hardened visual stability index (HVSI). Traditionally, HVSI assessment is based on visual judgment, and therefore its accuracy can be severely limited by human error, low efficiency, and work tedium. Thus, a study developed and implemented a methodology for automatically evaluating SCC stability. This was done in several steps: converting the image of a typical concrete sample (a cut surface with various shades of gray) into a binary image of light colors (aggregates) and dark colors (concrete cement), identifying aggregate sizes, detecting the mortar layer thickness, and using statistical analysis to derive the HVSI. Algorithms were developed for each phase and were implemented with standard coding languages. In effect, the methodology digitally processes a given concrete sample image and assesses its stability with respect to the HVSI. Accuracy of the new methodology was checked with control observations and was found to provide a reliable assessment of SCC stability in regard to static segregation resistance.