ReCyCONtrol project consortium – Key technologies for the digital revolution in concrete construction

In Europe and Turkey, about 600,000 batches of ready-mixed concrete are produced every day. According to estimates, over 80% of these batches are remarkably similar in composition, which results in enormously high repetition rates. Although a major part of concrete production today is fully automated involving the processing of predetermined concrete mix designs, it is still impossible to adequately consider fluctuations in the concrete raw material characteristics. As a result, typical concrete mixes involve costly, environmentally disadvantageous safety margins, including increased cement contents, to make up for such fluctuations. Over the past three years, the ReCyCONtrol research consortium, which receives funds from the Federal Ministry of Education and Research, has developed a completely new approach to producing concrete in which sensors continuously monitor all steps of the production process while using artificial intelligence to adjust the concrete mix in response to fluctuations in its raw materials.

Research network of partners from industry and academia

The project consortium involves numerous partners from industry and academia. This research network includes companies HeidelbergMaterials, Master Builders Solutions Deutschland GmbH, Pemat GmbH, Bikotronic GmbH, Alcemy GmbH, and Moß GmbH, as well as the Federal Waterways Engineering and Research Institute (Bundesanstalt für Wasserbau; BAW). The Institute of Building Materials Science and the Institute of Photogrammetry and Geoinformation at Leibniz University Hanover coordinate the consortium (see Fig. 1).

Non-contact measurement systems and self-learning process control methods

The work of the ReCyCONtrol project consortium focused on developing non-contact measurement systems and self-learning process control methods for concrete production that make it possible to accurately capture adverse fluctuations in the characteristics of concrete raw materials, particularly recycled aggregates, in terms of their composition and granulometry, and to use this information to adjust the properties of the finished concrete by controlling them in such a way that the specified target characteristics of the concrete are achieved.

Method development

Developing an appropriate methodology initially required characterization of commercially available, sized recycled aggregates of different compositions (concrete/masonry rubble, natural mineral aggregates) and identification of the usual ranges of variation in their properties. Based on this work, the influence of fluctuations (particle size distribution, material composition) on the fresh concrete characteristics (consistency, rheological properties, etc.) was quantified. This step made it possible to determine permissible fluctuations in the concrete composition and characteristics from a real-life perspective, and to formulate engineering boundary conditions for capturing raw material and fresh concrete properties. So-called computer vision or AI-based methods were developed with a view to implementing real-time-enabled inline measurement systems for recording the properties of input materials and fresh concrete. To allow for the next step of transitioning from pure quality capture to quality control and adjustment, algorithms have been developed, and are still under development, that transfer the characteristic parameters of the raw materials and/or the fresh concrete determined using the above methods into the concrete technology context while providing recommendations for adjustment. If required, the algorithms developed for process control use this information to adjust the concrete mix design even before the actual mixing process begins or to adjust fresh concrete characteristics by dosing additives during the mixing process (see Fig. 2).

Targeted implementation

Thanks to the wide array of partners from industry and academia involved in the project consortium, as well as the client institution, it was possible to address and consider all challenges anticipated for the technology referred to above, which also allows for direct, targeted, and cost-efficient transfer of project outcomes into practice. Using automated, self-learning process control methods in concrete production is a ground-breaking innovation also from a scientific perspective.

Final colloquium

In October 2024, the final ReCyCONtrol colloquium took place at Leibniz University Hanover, involving about 90 participants from a wide range of fields in industry and academia. The final colloquium provided the framework for all project partners to present their outcomes. Besides exciting lectures, some of the methods were also demonstrated in a realistic, prototype-stage setting.