Concrete workability is essential for construction quality, with the slump test being the most widely used on-site method for its assessment. However, traditional slump testing is manual, time-consuming, and highly operator-dependent, making it unsuitable for continuous or real-time monitoring during placement. To address these limitations, we present SlumpGuard, 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. We introduce the system design, construct a site-replicated dataset of over 6,000 video clips, and report extensive evaluations demonstrating reliable chute localization, accurate pouring detection, and robust slump prediction under diverse field conditions. An expert study further reveals significant disagreement in human visual estimates, highlighting the need for automated assessment.

翻译：混凝土工作性对施工质量至关重要，而坍落度试验是现场评估工作性最广泛使用的方法。然而，传统坍落度测试依赖人工操作、耗时且受操作者影响显著，难以在浇筑过程中进行连续或实时监测。为克服这些局限，本文提出SlumpGuard——一种基于人工智能的视觉系统，该系统通过单个固定摄像头分析搅拌车卸料槽的自然卸料流态。该系统执行自动卸料槽检测、浇筑事件识别与基于视频的坍落度分类，无需传感器、硬件安装或人工干预即可实现质量监控。我们介绍了系统设计，构建了包含6000余个视频片段的现场复现数据集，并通过大量评估证明了系统在不同现场条件下能实现可靠的卸料槽定位、精确的浇筑检测与稳健的坍落度预测。专家研究进一步揭示了人工视觉评估存在显著分歧，凸显了自动化评估的必要性。