SlumpGuard: An AI-Powered Real-Time System for Automated Concrete Slump Prediction via Video Analysis
This addresses the need for automated quality assurance in construction, though it is an incremental application of existing AI methods to a specific domain.
The paper tackles the problem of manual and inconsistent concrete slump testing by proposing SlumpGuard, an AI-powered video analysis system that automates real-time workability assessment, improving accuracy and efficiency in quality control.
Concrete workability is essential for construction quality, with the slump test being the most common on-site method for its assessment. However, traditional slump testing is manual, time-consuming, and prone to inconsistency, limiting its applicability for real-time monitoring. To address these challenges, we propose SlumpGuard, an AI-powered, video-based system that automatically analyzes concrete flow from the truck chute to assess workability in real time. Our system enables full-batch inspection without manual intervention, improving both the accuracy and efficiency of quality control. We present the system design, the construction of a dedicated dataset, and empirical results from real-world deployment, demonstrating the effectiveness of SlumpGuard as a practical solution for modern concrete quality assurance.