Details
Original language | English |
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Title of host publication | Proceedings for the 6th fib International Congress, 2022- Concrete Innovation for Sustainability |
Pages | 434-443 |
Number of pages | 10 |
Publication status | Published - 2022 |
Event | 6th fib International Congress on Concrete Innovation for Sustainability, 2022 - Oslo, Norway Duration: 12 Jun 2022 → 16 Jun 2022 |
Publication series
Name | fib Symposium |
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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
- Engineering(all)
- Civil and Structural Engineering
- Engineering(all)
- Building and Construction
- Materials Science(all)
- Materials Science (miscellaneous)
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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 proceeding › Conference contribution › Research › peer review
}
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 -