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2023 | OriginalPaper | Chapter

A WGAN Approach to Synthetic TBM Data Generation

Authors : Paul J. Unterlass, Georg H. Erharter, Alla Sapronova, Thomas Marcher

Published in: Trends on Construction in the Digital Era

Publisher: Springer International Publishing

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Abstract

The chapter delves into the application of a Wasserstein GAN (WGAN) approach to generate synthetic TBM operational data, addressing the limitations of real-world datasets in geotechnical engineering. By training a WGAN on real data from a major European tunnel construction site, the authors demonstrate the generation of synthetic data that mimics the patterns of original data while ensuring sufficient dissimilarity to avoid confidentiality issues. The study evaluates the generated data through visualization techniques and a GRU regressor model, showing that the synthetic data can be effectively used in machine learning tasks. This work underscores the potential of WGANs to overcome data scarcity and privacy constraints in geotechnical applications, paving the way for more extensive use of machine learning in the field.
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Metadata
Title
A WGAN Approach to Synthetic TBM Data Generation
Authors
Paul J. Unterlass
Georg H. Erharter
Alla Sapronova
Thomas Marcher
Copyright Year
2023
Book
Trends on Construction in the Digital Era

Print ISBN: 978-3-031-20240-7

Electronic ISBN: 978-3-031-20241-4

Copyright Year: 2023

DOI
https://doi.org/10.1007/978-3-031-20241-4_1