TY - GEN

T1 - Digitization of the concrete production chain using computer vision and artificial intelligence

AU - Haist, Michael

AU - Heipke, Christian

AU - Beyer, Dries

AU - Coenen, Max

AU - Schack, Tobias

AU - Vogel, Christian

AU - Ponick, Anne

AU - Langer, Amadeus

N1 - Funding Information: No. 033R260A, the funding of the project “Characterization of fresh concrete properties using optical measurement methods” provided by Deutscher Beton und Bautechnik Verein E.V. under the grant No. DBV321 and the funding of the project “Open Channel Flow” provided by the German Research Foundation under the grant No. 452024049. The authors would like to thank the following companies for their support in some aspects of the investigations: Heidelberger Beton GmbH, Master Builders Solutions Deutschland GmbH, Pemat Mischtechnik GmbH, Bikotronic GmbH, alcemy GmbH, Moß Abbruch-Erdbau-Recycling GmbH & Co. KG and the Bundesanstalt für Wasserbau (BAW) hannover.de/) provided by the German Federal Ministry of Education and Research under the grant

PY - 2022

Y1 - 2022

N2 - The production of concrete currently goes along with pronounced CO2-emissions and an enormous consumption of (mineral) resources. In response to sustainability requirements, concretes thus are increasingly produced using recipes containing six to ten different raw materials including recycled materials and industrial wastes. This increasing complexity results in an increased sensitivity to unpredictable fluctuations in material properties or boundary conditions during the production process. Digital sensor systems and quality control schemes are considered as key to solving this problem, however, digital technologies from other industries have not yet fully established themselves in concrete construction sector, especially in the quality control. Despite the fact that the concrete industry has extremely high repetition factors, big data based quality control is missing, as we currently lack both sensor systems providing data and concrete specific data treatment algorithms. This paper presents an overview on digital methods based on computer vision and artificial intelligence to quantify the properties of concrete raw materials and the fresh concrete along the entire process chain. The methods differentiate between systems that are incorporated into the production process, i.e. in the concrete plant, and systems that are applied after production, i.e. at the construction site. While the first kind enables an online reaction and control of the concrete properties in real-time, namely already during the batch-production, the latter approach allows an offline and, therefore, post-production quality control. All proposed methods eventually contribute to a facilitation of a digital control loop for ready-mixed concrete production. The developed techniques can be easily applied to pre-cast elements production or concrete products.

AB - The production of concrete currently goes along with pronounced CO2-emissions and an enormous consumption of (mineral) resources. In response to sustainability requirements, concretes thus are increasingly produced using recipes containing six to ten different raw materials including recycled materials and industrial wastes. This increasing complexity results in an increased sensitivity to unpredictable fluctuations in material properties or boundary conditions during the production process. Digital sensor systems and quality control schemes are considered as key to solving this problem, however, digital technologies from other industries have not yet fully established themselves in concrete construction sector, especially in the quality control. Despite the fact that the concrete industry has extremely high repetition factors, big data based quality control is missing, as we currently lack both sensor systems providing data and concrete specific data treatment algorithms. This paper presents an overview on digital methods based on computer vision and artificial intelligence to quantify the properties of concrete raw materials and the fresh concrete along the entire process chain. The methods differentiate between systems that are incorporated into the production process, i.e. in the concrete plant, and systems that are applied after production, i.e. at the construction site. While the first kind enables an online reaction and control of the concrete properties in real-time, namely already during the batch-production, the latter approach allows an offline and, therefore, post-production quality control. All proposed methods eventually contribute to a facilitation of a digital control loop for ready-mixed concrete production. The developed techniques can be easily applied to pre-cast elements production or concrete products.

KW - artificial intelligence

KW - automated process monitoring

KW - computer vision

KW - digital concrete loop

KW - digital concrete production

KW - digital quality control

UR - http://www.scopus.com/inward/record.url?scp=85143895782&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:85143895782

SN - 9782940643158

T3 - fib Symposium

SP - 434

EP - 443

BT - Proceedings for the 6th fib International Congress, 2022- Concrete Innovation for Sustainability

T2 - 6th fib International Congress on Concrete Innovation for Sustainability, 2022

Y2 - 12 June 2022 through 16 June 2022

ER -