Abstract. Advancing the level of digitalization and automation in concrete manufacturing can substantially contribute to lowering CO2 emissions associated with the concrete production. This work introduces a new methodology for predicting the time-dependent properties of fresh concrete during mixing. For the prediction, a deep learning network is created which uses stereoscopic image sequences of the flowing material together with tabular data as input. Besides mix design parameters and process state data, like energy consumption, moisture and fresh concrete temperature, temporal information is included in the tabular data. The temporal information represents the time interval between image acquisition and the time for which the properties should be predicted. During training, this interval corresponds to the difference between the image acquisition and the time at which reference measurements are taken, allowing the network to implicitly learn the temporal evolution of the material properties, namely the slump flow diameter, yield stress, and plastic viscosity. Incorporating time-dependent prediction enables the forecasting of property changes throughout the mixing process, offering a valuable tool for real-time process control. This capability allows timely adjustments whenever deviations from the desired material behavior are detected. The experimental investigations presented in this paper demonstrate the feasibility of this method under realistic conditions.
Meyer et al. (Fri,) studied this question.