Using CNNs to optimize numerical simulations in geotechnical engineering

Wolf, Beat (School of Engineering and Architecture (HEIA-FR), HES-SO // University of Applied Sciences Western Switzerland) ; Donzallaz, Jonathan (School of Engineering and Architecture (HEIA-FR), HES-SO // University of Applied Sciences Western Switzerland) ; Jost, Colette (School of Engineering and Architecture (HEIA-FR), HES-SO // University of Applied Sciences Western Switzerland) ; Hayoz, Amanda (School of Engineering and Architecture (HEIA-FR), HES-SO // University of Applied Sciences Western Switzerland) ; Commend, Stéphane (School of Engineering and Architecture (HEIA-FR), HES-SO // University of Applied Sciences Western Switzerland) ; Hennebert, Jean (School of Engineering and Architecture (HEIA-FR), HES-SO // University of Applied Sciences Western Switzerland)

Deep excavations are today mainly designed by manually optimising the wall’s geometry, stiffness and strut or anchor layout. In order to better automate this process for sustained excavations, we are exploring the possibility of approximating key values using a machine learning (ML) model instead of calculating them with time-consuming numerical simulations. After demonstrating in our previous work that this approach works for simple use cases, we show in this paper that this method can be enhanced to adapt to complex real-world supported excavations. We have improved our ML model compared to our previous work by using a convolutional neural network (CNN) model, coding the excavation configuration as a set of layers of fixed height containing the soil parameters as well as the geometry of the walls and struts. The system is trained and evaluated on a set of synthetically generated situations using numerical simulation software. To validate this approach, we also compare our results to a set of 15 real-world situations in a t-SNE. Using our improved CNN model we could show that applying machine learning to predict the output of numerical simulation in the domain of geotechnical engineering not only works in simple cases but also in more complex, real-world situations.


Keywords:
Conference Type:
full paper
Faculty:
Ingénierie et Architecture
School:
HEIA-FR
Institute:
iCoSys - Institut des systèmes complexes
iTEC - Institut des technologies de l'environnement construit
Publisher:
Winterthur, Switzerland, 2-4 September 2020
Date:
2020-09
Winterthur, Switzerland
2-4 September 2020
Pagination:
pp. 247-256
Published in:
Lecture Notes in Computer Science ; Proceedings of IAPR Workshop on Artificial Neural Networks in Pattern Recognition, ANNPR 2020 : Artificial Neural Networks in Pattern Recognition, 2-4 September 2020, Winterthur, Switzerland
DOI:
ISSN:
0302-9743
ISBN:
978-3-030-58308-8
Appears in Collection:

Note: The status of this file is: restricted


 Record created 2020-10-13, last modified 2020-10-27

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