Details
| Titel in Übersetzung | Machine Learning for the Numerical Homogenization of Concrete |
|---|---|
| Originalsprache | Deutsch |
| Seiten | 354-360 |
| Seitenumfang | 7 |
| Band | 98 |
| Ausgabenummer | 11 |
| Fachzeitschrift | Bauingenieur |
| Publikationsstatus | Veröffentlicht - 2023 |
Abstract
Material modeling of concrete using modern numerical methods significantly accelerates the design process of structures. However, for multiscale modeling of such a heterogeneous material, the established homogenization methods are still very computationally intensive, especially for high accuracy requirements. In this paper, we propose a machine learning approach that provides a computationally efficient solution method while delivering a high degree of accuracy. The dataset used for the training and testing process consists of artificial and real microstructural images (input), while the result data (output) are the homogenized stresses of a given representative volume element (RVE). The performance of the model is demonstrated by examples and compared with classical homogenization methods. The developed ML model achieves higher accuracy in determining the homogenized stresses and significantly reduces the computation time.
ASJC Scopus Sachgebiete
- Ingenieurwesen (insg.)
- Tief- und Ingenieurbau
- Ingenieurwesen (insg.)
- Bauwesen
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in: Bauingenieur, Jahrgang 98, Nr. 11, 2023, S. 354-360.
Publikation: Beitrag in nicht-wissenschaftlicher/populärwissenschaftlicher Zeitschrift/Zeitung › Beitrag in Publikumszeitung/-zeitschrift › Transfer
}
TY - GEN
T1 - Maschinelles Lernen für die numerische Homogenisierung von Beton
AU - Aldakheel, Fadi
AU - Haist, Michael
AU - Lohaus, Ludger
AU - Wriggers, Peter
PY - 2023
Y1 - 2023
N2 - Material modeling of concrete using modern numerical methods significantly accelerates the design process of structures. However, for multiscale modeling of such a heterogeneous material, the established homogenization methods are still very computationally intensive, especially for high accuracy requirements. In this paper, we propose a machine learning approach that provides a computationally efficient solution method while delivering a high degree of accuracy. The dataset used for the training and testing process consists of artificial and real microstructural images (input), while the result data (output) are the homogenized stresses of a given representative volume element (RVE). The performance of the model is demonstrated by examples and compared with classical homogenization methods. The developed ML model achieves higher accuracy in determining the homogenized stresses and significantly reduces the computation time.
AB - Material modeling of concrete using modern numerical methods significantly accelerates the design process of structures. However, for multiscale modeling of such a heterogeneous material, the established homogenization methods are still very computationally intensive, especially for high accuracy requirements. In this paper, we propose a machine learning approach that provides a computationally efficient solution method while delivering a high degree of accuracy. The dataset used for the training and testing process consists of artificial and real microstructural images (input), while the result data (output) are the homogenized stresses of a given representative volume element (RVE). The performance of the model is demonstrated by examples and compared with classical homogenization methods. The developed ML model achieves higher accuracy in determining the homogenized stresses and significantly reduces the computation time.
UR - http://www.scopus.com/inward/record.url?scp=85181461786&partnerID=8YFLogxK
U2 - 10.37544/0005-6650-2023-11-42
DO - 10.37544/0005-6650-2023-11-42
M3 - Beitrag in Publikumszeitung/-zeitschrift
AN - SCOPUS:85181461786
VL - 98
SP - 354
EP - 360
JO - Bauingenieur
JF - Bauingenieur
SN - 0005-6650
ER -