Details
| Original language | English |
|---|---|
| Pages (from-to) | 145-152 |
| Number of pages | 8 |
| Journal | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Issue number | X-2-2024 |
| Publication status | Published - 10 Jun 2024 |
| Event | 2024 ISPRS TC II Mid-term Symposium on The Role of Photogrammetry for a Sustainable World - Las Vegas, United States Duration: 11 Jun 2024 → 14 Jun 2024 |
Abstract
Increasing the degree of digitisation and automation in the concrete production process can play a crucial role in reducing the CO2 emissions that are associated with the production of concrete. In this paper, a method is presented that makes it possible to predict the properties of fresh concrete during the mixing process based on stereoscopic image sequences of the concretes flow behaviour. A Convolutional Neural Network (CNN) is used for the prediction, which receives the images supported by information on the mix design as input. In addition, the network receives temporal information in the form of the time difference between the time at which the images are taken and the time at which the reference values of the concretes are carried out. With this temporal information, the network implicitly learns the time-dependent behaviour of the concretes properties. The network predicts the slump flow diameter, the yield stress and the plastic viscosity. The time-dependent prediction potentially opens up the pathway to determine the temporal development of the fresh concrete properties already during mixing. This provides a huge advantage for the concrete industry. As a result, countermeasures can be taken in a timely manner. It is shown that an approach based on depth and optical flow images, supported by information of the mix design, achieves the best results.
Keywords
- Building materials, Deep learning, Fresh concrete properties, Stereoscopy, Time dependency
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, No. X-2-2024, 10.06.2024, p. 145-152.
Research output: Contribution to journal › Conference article › Research › peer review
}
TY - JOUR
T1 - Image-based Deep Learning for the time-dependent prediction of fresh concrete properties
AU - Meyer, Max
AU - Langer, Amadeus
AU - Mehltretter, Max
AU - Beyer, Dries
AU - Coenen, Max
AU - Schack, Tobias
AU - Haist, Michael
AU - Heipke, Christian
N1 - Publisher Copyright: © Author(s) 2024.
PY - 2024/6/10
Y1 - 2024/6/10
N2 - Increasing the degree of digitisation and automation in the concrete production process can play a crucial role in reducing the CO2 emissions that are associated with the production of concrete. In this paper, a method is presented that makes it possible to predict the properties of fresh concrete during the mixing process based on stereoscopic image sequences of the concretes flow behaviour. A Convolutional Neural Network (CNN) is used for the prediction, which receives the images supported by information on the mix design as input. In addition, the network receives temporal information in the form of the time difference between the time at which the images are taken and the time at which the reference values of the concretes are carried out. With this temporal information, the network implicitly learns the time-dependent behaviour of the concretes properties. The network predicts the slump flow diameter, the yield stress and the plastic viscosity. The time-dependent prediction potentially opens up the pathway to determine the temporal development of the fresh concrete properties already during mixing. This provides a huge advantage for the concrete industry. As a result, countermeasures can be taken in a timely manner. It is shown that an approach based on depth and optical flow images, supported by information of the mix design, achieves the best results.
AB - Increasing the degree of digitisation and automation in the concrete production process can play a crucial role in reducing the CO2 emissions that are associated with the production of concrete. In this paper, a method is presented that makes it possible to predict the properties of fresh concrete during the mixing process based on stereoscopic image sequences of the concretes flow behaviour. A Convolutional Neural Network (CNN) is used for the prediction, which receives the images supported by information on the mix design as input. In addition, the network receives temporal information in the form of the time difference between the time at which the images are taken and the time at which the reference values of the concretes are carried out. With this temporal information, the network implicitly learns the time-dependent behaviour of the concretes properties. The network predicts the slump flow diameter, the yield stress and the plastic viscosity. The time-dependent prediction potentially opens up the pathway to determine the temporal development of the fresh concrete properties already during mixing. This provides a huge advantage for the concrete industry. As a result, countermeasures can be taken in a timely manner. It is shown that an approach based on depth and optical flow images, supported by information of the mix design, achieves the best results.
KW - Building materials
KW - Deep learning
KW - Fresh concrete properties
KW - Stereoscopy
KW - Time dependency
UR - http://www.scopus.com/inward/record.url?scp=85199913004&partnerID=8YFLogxK
U2 - 10.48550/arXiv.2402.06611
DO - 10.48550/arXiv.2402.06611
M3 - Conference article
AN - SCOPUS:85199913004
SP - 145
EP - 152
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 - X-2-2024
T2 - 2024 ISPRS TC II Mid-term Symposium on The Role of Photogrammetry for a Sustainable World
Y2 - 11 June 2024 through 14 June 2024
ER -