Abstract
Plastic viscosity is a key property of ultra-high-performance concrete (UHPC) and must be controlled during the mixing to achieve desired fresh and hardened properties. This paper presents a video recognition technology for real-time assessment of plastic viscosity using a video captured by a camera during the mixing of UHPC. A long-term recurrent convolutional network is proposed to extract spatial and temporal features of flowing UHPC from the video and correlate the features with plastic viscosity measured from a rheometer, thus enabling assessment of plastic viscosity using videos. This research also investigates the effects of plastic viscosity on the fiber dispersion and orientation, air content, and flexural properties of UHPC. The results show that the plastic viscosity significantly influences fiber distribution and air void content, thus affecting the flexural properties of UHPC. The presented method enables real-time assessment of plastic viscosity for control of flexural properties and air void content of UHPC. This study will greatly facilitate quality control for production of UHPC.
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