Concrete workability measure is mostly determined based on subjective assessment of a certified assessor with visual inspections. The potential human error in measuring the workability and the resulting unnecessary adjustments for the workability is a major challenge faced by the construction industry, leading to significant costs, material waste and delay. In this paper, we try to apply computer vision techniques to observe the concrete mixing process and estimate the workability. Specifically, we collected the video data and then built three different deep neural networks for spatial-temporal regression. The pilot study demonstrates a practical application with computer vision techniques to estimate the concrete workability during the mixing process.

翻译：具体可行的措施主要是根据对经过目视检查的经认证的评估员的主观评估来确定的,在衡量可操作性以及由此对可操作性进行不必要调整方面可能发生的人为错误是建筑业面临的一项重大挑战,导致巨大的成本、物质浪费和延误。在本文件中,我们试图运用计算机视觉技术来观察混混混混混混混混混混混混混混混混混混混混混混混混混混混混混混混混混混混混混混混混混混混混混混。具体地说,我们收集了视频数据,然后为空间时回归建立了三个不同的深神经网络。试点研究展示了一种实际应用计算机视觉技术来估计混合过程的具体可行性。