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

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Original languageEnglish
Title of host publicationProceedings for the 6th fib International Congress, 2022- Concrete Innovation for Sustainability
Pages434-443
Number of pages10
Publication statusPublished - 2022
Event6th fib International Congress on Concrete Innovation for Sustainability, 2022 - Oslo, Norway
Duration: 12 Jun 202216 Jun 2022

Publication series

Namefib Symposium
ISSN (Print)2617-4820

Abstract

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.

Keywords

    artificial intelligence, automated process monitoring, computer vision, digital concrete loop, digital concrete production, digital quality control

ASJC Scopus subject areas

Cite this

Digitization of the concrete production chain using computer vision and artificial intelligence. / Haist, Michael; Heipke, Christian; Beyer, Dries et al.
Proceedings for the 6th fib International Congress, 2022- Concrete Innovation for Sustainability. 2022. p. 434-443 (fib Symposium).

Research output: Chapter in book/report/conference proceedingConference contributionResearchpeer review

Haist, M, Heipke, C, Beyer, D, Coenen, M, Schack, T, Vogel, C, Ponick, A & Langer, A 2022, Digitization of the concrete production chain using computer vision and artificial intelligence. in Proceedings for the 6th fib International Congress, 2022- Concrete Innovation for Sustainability. fib Symposium, pp. 434-443, 6th fib International Congress on Concrete Innovation for Sustainability, 2022, Oslo, Norway, 12 Jun 2022.
Haist, M., Heipke, C., Beyer, D., Coenen, M., Schack, T., Vogel, C., Ponick, A., & Langer, A. (2022). Digitization of the concrete production chain using computer vision and artificial intelligence. In Proceedings for the 6th fib International Congress, 2022- Concrete Innovation for Sustainability (pp. 434-443). (fib Symposium).
Haist M, Heipke C, Beyer D, Coenen M, Schack T, Vogel C et al. Digitization of the concrete production chain using computer vision and artificial intelligence. In Proceedings for the 6th fib International Congress, 2022- Concrete Innovation for Sustainability. 2022. p. 434-443. (fib Symposium).
Haist, Michael ; Heipke, Christian ; Beyer, Dries et al. / Digitization of the concrete production chain using computer vision and artificial intelligence. Proceedings for the 6th fib International Congress, 2022- Concrete Innovation for Sustainability. 2022. pp. 434-443 (fib Symposium).
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title = "Digitization of the concrete production chain using computer vision and artificial intelligence",
abstract = "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.",
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author = "Michael Haist and Christian Heipke and Dries Beyer and Max Coenen and Tobias Schack and Christian Vogel and Anne Ponick and Amadeus Langer",
note = "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, Mo{\ss} Abbruch-Erdbau-Recycling GmbH & Co. KG and the Bundesanstalt f{\"u}r Wasserbau (BAW) hannover.de/) provided by the German Federal Ministry of Education and Research under the grant; 6th fib International Congress on Concrete Innovation for Sustainability, 2022 ; Conference date: 12-06-2022 Through 16-06-2022",
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AU - Haist, Michael

AU - Heipke, Christian

AU - Beyer, Dries

AU - Coenen, Max

AU - Schack, Tobias

AU - Vogel, Christian

AU - Ponick, Anne

AU - Langer, Amadeus

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KW - automated process monitoring

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