Details
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 2023 European Conference on Computing in Construction (EC3) |
| Publication status | Published - 2023 |
Abstract
To improve sustainability, concretes are increasingly produced using recipes containing up to a dozen different raw materials. The increasing complexity of the composition leads to an increased sensitivity and decreased robustness of the concrete, making a reliable quality control of the concrete highly important. Despite that, current quality control is mainly conducted based on analogous and empirical tests. This paper presents a novel approach for an automatic quality assessment of fresh concrete on the construction site. Based on a camera sensor setup, delivering image sequences showing the concrete flow during the discharge process of a mixing truck, we propose the Concrete Flow Transformer, a deep learning approach based on Vision Transformers, for the prediction of fresh concrete properties. The performance of the proposed approach is evaluated on a challenging real-world data set, demonstrating highly convincing results for the prediction of both, the consistency and rheological parameters of the fresh concrete.
ASJC Scopus subject areas
- Computer Science(all)
- Information Systems
- Engineering(all)
- Building and Construction
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Proceedings of the 2023 European Conference on Computing in Construction (EC3). 2023.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Concrete flow transformer
T2 - predicting fresh concrete properties from concrete flow using vision transformers
AU - Coenen, Max
AU - Vogel, Christian
AU - Schack, Tobias
AU - Haist, Michael
N1 - Funding Information: The authors acknowledge the funding of the project ReCyCONtrol (https://www.recycontrol. uni-hannover.de/en/) provided by the German Federal Ministry of Education and Research (BMBF) under the grant No. 033R260A and the funding of the project Open Channel Flow provided by the German Research Foundation (DFG) under the grant No. 452024049.
PY - 2023
Y1 - 2023
N2 - To improve sustainability, concretes are increasingly produced using recipes containing up to a dozen different raw materials. The increasing complexity of the composition leads to an increased sensitivity and decreased robustness of the concrete, making a reliable quality control of the concrete highly important. Despite that, current quality control is mainly conducted based on analogous and empirical tests. This paper presents a novel approach for an automatic quality assessment of fresh concrete on the construction site. Based on a camera sensor setup, delivering image sequences showing the concrete flow during the discharge process of a mixing truck, we propose the Concrete Flow Transformer, a deep learning approach based on Vision Transformers, for the prediction of fresh concrete properties. The performance of the proposed approach is evaluated on a challenging real-world data set, demonstrating highly convincing results for the prediction of both, the consistency and rheological parameters of the fresh concrete.
AB - To improve sustainability, concretes are increasingly produced using recipes containing up to a dozen different raw materials. The increasing complexity of the composition leads to an increased sensitivity and decreased robustness of the concrete, making a reliable quality control of the concrete highly important. Despite that, current quality control is mainly conducted based on analogous and empirical tests. This paper presents a novel approach for an automatic quality assessment of fresh concrete on the construction site. Based on a camera sensor setup, delivering image sequences showing the concrete flow during the discharge process of a mixing truck, we propose the Concrete Flow Transformer, a deep learning approach based on Vision Transformers, for the prediction of fresh concrete properties. The performance of the proposed approach is evaluated on a challenging real-world data set, demonstrating highly convincing results for the prediction of both, the consistency and rheological parameters of the fresh concrete.
UR - http://www.scopus.com/inward/record.url?scp=85177238086&partnerID=8YFLogxK
U2 - 10.35490/EC3.2023.222
DO - 10.35490/EC3.2023.222
M3 - Conference contribution
AN - SCOPUS:85177238086
SN - 9780701702731
BT - Proceedings of the 2023 European Conference on Computing in Construction (EC3)
ER -