Physics-Informed Neural Networks Applied to Catastrophic Creeping Landslides

This is a Preprint and has not been peer reviewed. The published version of this Preprint is available: https://doi.org/10.1007/s10346-023-02072-0. This is version 1 of this Preprint.

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Authors

Ahmad Moeineddin, Carolina Segui, Stephan Dueber, Raul Fuentes

Abstract

In this study, a new paradigm compared to traditional numerical approaches to solve the partial differential equation (PDE) that governs the thermo-poro-mechanical behavior of the shear band of deep-seated landslides, is presented. In particular, we show projections of the temperature inside the shear band as a proxy to estimate catastrophic failure of deep-seated landslides. A deep neural network is trained to find the temperature, by using a loss function defined by the underlying PDE and field data of three landslides. To validate the network, we have applied this network to the following cases: Vaiont, Shuping and Mud Creek landslides. The results show that, by creating and training the network with synthetic data, the behavior of the landslide can be reproduced and can forecast the basal temperature of the three case studies. Hence, providing a real-time estimation of the stability of the landslide, compared to other solutions whose stability study has to be calculated individually for each case scenario. Moreover, this study offers a novel procedure to design a neural network architecture, considering stability, accuracy, and over-fitting. This approach could be useful also to other applications beyond landslides.

DOI

https://doi.org/10.31223/X5J36P

Subjects

Computational Engineering, Geotechnical Engineering

Keywords

PINNs, Numerical modeling, Landslides, Shear band, temperature, numerical modeling, Landslides, Shear band, temperature

Dates

Published: 2023-02-12 20:27

License

CC BY Attribution 4.0 International