Spatial-temporal Analysis for Automated Concrete Workability Estimation
This addresses the challenge of human error and inefficiency in the construction industry, but it is incremental as it applies existing computer vision methods to a new domain.
The paper tackled the problem of subjective and error-prone manual concrete workability assessment by applying computer vision techniques to estimate workability from video data of the mixing process, demonstrating a practical application with deep neural networks for spatial-temporal regression.
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.