Publications / 2024 Proceedings of the 41st ISARC, Lille, France
The need to reduce CO2 emissions from concrete leads to increasingly complex mix designs involving e.g. CO2 reduced cements, recycled materials, and various chemical additives. This complexity results in a larger sensitivity of the concrete to unpredictable fluctuations in both, the base material properties and in boundary conditions such as temperature and humidity during the production process. Digital sensor systems and quality control schemes are considered as key to counteract this problem by enabling an automated production control.
As contribution towards this goal, this paper proposes Computer Vision based approaches for the predictive characterisation of raw materials (here: of concrete aggregates) and of the fresh concrete quality during the mixing process. In particular, we propose the usage of imaging sensors for the observation of both, aggregate material and the flow behaviour of fresh concrete during the mixing process, and present deep learning methods for the prediction of granulometric and rheological properties from the image observations, respectively. Incorporating such systems into the concrete production process enables the facilitation of a digital control loop for ready-mixed concrete production by allowing an in-line reaction to raw material fluctuations and to deviations of the concrete from the target properties.