Details
Originalsprache | Englisch |
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Titel des Sammelwerks | Proceedings of the 41st International Symposium on Automation and Robotics in Construction |
Herausgeber/-innen | Vicente Gonzalez-Moret, Jiansong Zhang, Borja García de Soto, Ioannis Brilakis |
Erscheinungsort | Lille, France |
Seiten | 26-33 |
Seitenumfang | 8 |
ISBN (elektronisch) | 9780645832211 |
Publikationsstatus | Veröffentlicht - 1 Juni 2024 |
Publikationsreihe
Name | Proceedings of the International Symposium on Automation and Robotics in Construction |
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ISSN (elektronisch) | 2413-5844 |
Abstract
The need to reduce CO 2 emissions from concrete leads to increasingly complex mix designs involving e.g. CO 2 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 investigates the research question whether Computer Vision can be used 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.
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Artificial intelligence
- Ingenieurwesen (insg.)
- Sicherheit, Risiko, Zuverlässigkeit und Qualität
- Ingenieurwesen (insg.)
- Steuerungs- und Systemtechnik
- Ingenieurwesen (insg.)
- Bauwesen
- Informatik (insg.)
- Angewandte Informatik
- Ingenieurwesen (insg.)
- Tief- und Ingenieurbau
Zitieren
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- BibTex
- RIS
Proceedings of the 41st International Symposium on Automation and Robotics in Construction. Hrsg. / Vicente Gonzalez-Moret; Jiansong Zhang; Borja García de Soto; Ioannis Brilakis. Lille, France, 2024. S. 26-33 (Proceedings of the International Symposium on Automation and Robotics in Construction).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Computer Vision as Key to an Automated Concrete Production Control
AU - Coenen, Max
AU - Meyer, Maximilian
AU - Beyer, Dries
AU - Heipke, Christian
AU - Haist, Michael
N1 - Publisher Copyright: © 2024 ISARC. All Rights Reserved.
PY - 2024/6/1
Y1 - 2024/6/1
N2 - The need to reduce CO 2 emissions from concrete leads to increasingly complex mix designs involving e.g. CO 2 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 investigates the research question whether Computer Vision can be used 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.
AB - The need to reduce CO 2 emissions from concrete leads to increasingly complex mix designs involving e.g. CO 2 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 investigates the research question whether Computer Vision can be used 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.
KW - CNN
KW - Computer Vision
KW - Concrete 4.0
KW - Deep Learning
KW - ViT
KW - image-based granulometry
KW - image-based rheology
UR - http://www.scopus.com/inward/record.url?scp=85199655931&partnerID=8YFLogxK
U2 - 10.22260/ISARC2024/0005
DO - 10.22260/ISARC2024/0005
M3 - Conference contribution
SN - 978-0-6458322-1-1
T3 - Proceedings of the International Symposium on Automation and Robotics in Construction
SP - 26
EP - 33
BT - Proceedings of the 41st International Symposium on Automation and Robotics in Construction
A2 - Gonzalez-Moret, Vicente
A2 - Zhang, Jiansong
A2 - García de Soto, Borja
A2 - Brilakis, Ioannis
CY - Lille, France
ER -