AI-powered real-time system for automated concrete slump prediction via video analysis

Concrete workability is essential to construction quality, and the slump test remains the most widely used on-site method for its assessment. However, traditional slump testing is manual, time-consuming, and highly operator-dependent, limiting its suitability for continuous or real-time monitoring during placement. SlumpGuard is an AI-powered vision system that analyzes the natural discharge flow from a mixer-truck chute using a single fixed camera. The system performs automatic chute detection, pouring-event identification, and video-based slump classification, enabling quality monitoring without sensors, hardware installation, or manual intervention. The system design is presented, along with a site-replicated dataset comprising over 6000 video clips, and extensive evaluations demonstrating reliable chute localization, accurate pouring detection, and robust slump prediction under diverse field conditions. An expert study further reveals substantial disagreement in human visual estimates, underscoring the need for automated assessment. Demonstration videos are available at this URL . • SlumpGuard, a camera-only AI system, enables real-time automated slump prediction. • A large-scale site-replicated dataset with over 6,000 video clips is constructed. • Expert studies reveal strong disagreement in human visual slump estimation. • The approach enables continuous, non-intrusive slump monitoring on real sites.