  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.
