Using CNNs to Optimize Numerical Simulations in Geotechnical Engineering
| Autor: | Amanda Hayoz, Stéphane Commend, Colette Jost, Jean Hennebert, Beat Wolf, Pierre Kuonen, Jonathan Donzallaz |
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| Rok vydání: | 2020 |
| Předmět: |
030213 general clinical medicine
Computer simulation business.industry Computer science Stiffness 02 engineering and technology Convolutional neural network 03 medical and health sciences 020303 mechanical engineering & transports 0302 clinical medicine Software 0203 mechanical engineering medicine Geotechnical engineering Use case Soil parameters medicine.symptom business Coding (social sciences) |
| Zdroj: | Artificial Neural Networks in Pattern Recognition ISBN: 9783030583088 ANNPR |
| DOI: | 10.1007/978-3-030-58309-5_20 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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