Maschinelles Lernen für die numerische Homogenisierung von Beton
- verfasst von
- Fadi Aldakheel, Michael Haist, Ludger Lohaus, Peter Wriggers
- 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.
- Organisationseinheit(en)
-
Institut für Baumechanik und Numerische Mechanik
Institut für Baustoffe
Institut für Kontinuumsmechanik
- Typ
- Beitrag in Publikumszeitung/-zeitschrift
- Journal
- Bauingenieur
- Band
- 98
- Seiten
- 354-360
- Anzahl der Seiten
- 7
- ISSN
- 0005-6650
- Publikationsdatum
- 2023
- Publikationsstatus
- Veröffentlicht
- ASJC Scopus Sachgebiete
- Tief- und Ingenieurbau, Bauwesen
- Elektronische Version(en)
-
https://doi.org/10.37544/0005-6650-2023-11-42 (Zugang:
Geschlossen)
