Details
| Original language | English |
|---|---|
| Pages (from-to) | 227-235 |
| Number of pages | 9 |
| Journal | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Volume | 5 |
| Issue number | 2 |
| Publication status | Published - 17 May 2022 |
| Event | 2022 24th ISPRS Congress on Imaging Today, Foreseeing Tomorrow, Commission II - Nice, France Duration: 6 Jun 2022 → 11 Jun 2022 |
Abstract
A large component of the building material concrete consists of aggregate with varying particle sizes between 0.125 and 32 mm. Its actual size distribution significantly affects the quality characteristics of the final concrete in both, the fresh and hardened states. The usually unknown variations in the size distribution of the aggregate particles, which can be large especially when using recycled aggregate materials, are typically compensated by an increased usage of cement which, however, has severe negative impacts on economical and ecological aspects of the concrete production. In order to allow a precise control of the target properties of the concrete, unknown variations in the size distribution have to be quantified to enable a proper adaptation of the concrete's mixture design in real time. To this end, this paper proposes a deep learning based method for the determination of concrete aggregate grading curves. In this context, we propose a network architecture applying multi-scale feature extraction modules in order to handle the strongly diverse object sizes of the particles. Furthermore, we propose and publish a novel dataset of concrete aggregate used for the quantitative evaluation of our method.
Keywords
- Automation in construction, Concrete aggregate, Deep learning, Granulometry, Particle size distribution
ASJC Scopus subject areas
- Physics and Astronomy(all)
- Instrumentation
- Environmental Science(all)
- Environmental Science (miscellaneous)
- Earth and Planetary Sciences(all)
- Earth and Planetary Sciences (miscellaneous)
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In: ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. 5, No. 2, 17.05.2022, p. 227-235.
Research output: Contribution to journal › Conference article › Research › peer review
}
TY - JOUR
T1 - LEARNING TO SIEVE
T2 - 2022 24th ISPRS Congress on Imaging Today, Foreseeing Tomorrow, Commission II
AU - Coenen, Max
AU - Beyer, Dries
AU - Heipke, Christian
AU - Haist, Michael
N1 - Funding Information: This work was supported by the Federal Ministry of Education and Research of Germany (BMBF) as part of the research project ReCyCONtrol [Project number 033R260A]. (https://www.recycontrol.uni-hannover.de).
PY - 2022/5/17
Y1 - 2022/5/17
N2 - A large component of the building material concrete consists of aggregate with varying particle sizes between 0.125 and 32 mm. Its actual size distribution significantly affects the quality characteristics of the final concrete in both, the fresh and hardened states. The usually unknown variations in the size distribution of the aggregate particles, which can be large especially when using recycled aggregate materials, are typically compensated by an increased usage of cement which, however, has severe negative impacts on economical and ecological aspects of the concrete production. In order to allow a precise control of the target properties of the concrete, unknown variations in the size distribution have to be quantified to enable a proper adaptation of the concrete's mixture design in real time. To this end, this paper proposes a deep learning based method for the determination of concrete aggregate grading curves. In this context, we propose a network architecture applying multi-scale feature extraction modules in order to handle the strongly diverse object sizes of the particles. Furthermore, we propose and publish a novel dataset of concrete aggregate used for the quantitative evaluation of our method.
AB - A large component of the building material concrete consists of aggregate with varying particle sizes between 0.125 and 32 mm. Its actual size distribution significantly affects the quality characteristics of the final concrete in both, the fresh and hardened states. The usually unknown variations in the size distribution of the aggregate particles, which can be large especially when using recycled aggregate materials, are typically compensated by an increased usage of cement which, however, has severe negative impacts on economical and ecological aspects of the concrete production. In order to allow a precise control of the target properties of the concrete, unknown variations in the size distribution have to be quantified to enable a proper adaptation of the concrete's mixture design in real time. To this end, this paper proposes a deep learning based method for the determination of concrete aggregate grading curves. In this context, we propose a network architecture applying multi-scale feature extraction modules in order to handle the strongly diverse object sizes of the particles. Furthermore, we propose and publish a novel dataset of concrete aggregate used for the quantitative evaluation of our method.
KW - Automation in construction
KW - Concrete aggregate
KW - Deep learning
KW - Granulometry
KW - Particle size distribution
UR - http://www.scopus.com/inward/record.url?scp=85132313639&partnerID=8YFLogxK
U2 - 10.5194/isprs-annals-V-2-2022-227-2022
DO - 10.5194/isprs-annals-V-2-2022-227-2022
M3 - Conference article
AN - SCOPUS:85132313639
VL - 5
SP - 227
EP - 235
JO - ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
JF - ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
SN - 2194-9042
IS - 2
Y2 - 6 June 2022 through 11 June 2022
ER -