Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | Proceedings of the 2023 European Conference on Computing in Construction (EC3) |
| Seitenumfang | 8 |
| ISBN (elektronisch) | 978-0-701702-73-1 |
| Publikationsstatus | Veröffentlicht - 2023 |
Abstract
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Information systems
- Ingenieurwesen (insg.)
- Bauwesen
Zitieren
- Standard
- Harvard
- Apa
- Vancouver
- BibTex
- RIS
Proceedings of the 2023 European Conference on Computing in Construction (EC3). 2023.
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Granulometry transformer
T2 - image-based granulometry of concrete aggregate for an automated concrete production control
AU - Coenen, Max
AU - Beyer, Dries
AU - Haist, Michael
N1 - Funding information: The work is part of the project ReCyCONtrol funded by the German Federal Ministry of Education and Research (BMBF) under the grant No. 033R260A.
PY - 2023
Y1 - 2023
N2 - The size distribution of concrete aggregate significantly affects the quality characteristics of the final concrete. However, despite its large influence, only approximate knowledge about the size distribution is known during concrete mix design and production, leading to a diminished ability of controlling target concrete properties. To overcome this limitation and to allow a precise control of desired concrete properties, we present `Granulometry Transformer', a vision-based approach for an automated aggregate grading curve estimation from image data. Our approach demonstrates state-of-the-art results on two challenging public benchmark data sets of both, coarse and fine aggregate material.
AB - The size distribution of concrete aggregate significantly affects the quality characteristics of the final concrete. However, despite its large influence, only approximate knowledge about the size distribution is known during concrete mix design and production, leading to a diminished ability of controlling target concrete properties. To overcome this limitation and to allow a precise control of desired concrete properties, we present `Granulometry Transformer', a vision-based approach for an automated aggregate grading curve estimation from image data. Our approach demonstrates state-of-the-art results on two challenging public benchmark data sets of both, coarse and fine aggregate material.
UR - http://www.scopus.com/inward/record.url?scp=85177177347&partnerID=8YFLogxK
U2 - 10.35490/EC3.2023.223
DO - 10.35490/EC3.2023.223
M3 - Conference contribution
SN - 9780701702731
BT - Proceedings of the 2023 European Conference on Computing in Construction (EC3)
ER -