  Increasing the degree of digitisation and automation in the concrete
production process can play a crucial role in reducing the CO$_2$ emissions
that are associated with the production of concrete. In this paper, a method is
presented that makes it possible to predict the properties of fresh concrete
during the mixing process based on stereoscopic image sequences of the
concretes flow behaviour. A Convolutional Neural Network (CNN) is used for the
prediction, which receives the images supported by information on the mix
design as input. In addition, the network receives temporal information in the
form of the time difference between the time at which the images are taken and
the time at which the reference values of the concretes are carried out. With
this temporal information, the network implicitly learns the time-dependent
behaviour of the concretes properties. The network predicts the slump flow
diameter, the yield stress and the plastic viscosity. The time-dependent
prediction potentially opens up the pathway to determine the temporal
development of the fresh concrete properties already during mixing. This
provides a huge advantage for the concrete industry. As a result,
countermeasures can be taken in a timely manner. It is shown that an approach
based on depth and optical flow images, supported by information of the mix
design, achieves the best results.
