A WGAN Approach to Synthetic TBM Data Generation

Paul Johannes Unterlaß; Georg H. Erharter; Alla Sapronova; Thomas Marcher

doi:10.1007/978-3-031-20241-4_1

A WGAN Approach to Synthetic TBM Data Generation

Paul Johannes Unterlaß^*, Georg H. Erharter, Alla Sapronova, Thomas Marcher

^*Korrespondierende/r Autor/-in für diese Arbeit

Institut für Felsmechanik und Tunnelbau (2200)

Publikation: Beitrag in Buch/Bericht/Konferenzband › Beitrag in einem Konferenzband › Begutachtung

Abstract

In this work we propose a generative adversarial network (GAN) based approach of generating synthetic geotechnical data for further applications in research and education. Geotechnical data generated by GANs shows similar characteristics as the original data, but still presents unique samples with no connection to the technical content of the original data. The data can therefore be made available publicly without any legal issues.

A WGAN (Wasserstein GAN) algorithm is used to generate synthetic tunnel boring machine (TBM) operational data based on real data from a major European tunnel construction site. The demands on the synthetic TBM data are of a dualistic nature: on the one hand, the data has to be sufficiently dissimilar to the original data, so that it does not create confidentiality issues (demand for originality). On the other hand, it has to show the same patterns and follow the same rules as the original data, so that it can be used as if it were real TBM data (demand for conformity). The WGAN model describes how a synthetic dataset is generated, in terms of a probabilistic model based on real data. By sampling from this model, we are able to generate new, unique synthetic and realistic TBM data.

We show that the demands for originality and conformity of the newly generated data are fulfilled.

Originalsprache	englisch
Titel	Trends on Construction in the Digital Era - Proceedings of ISIC 2022
Redakteure/-innen	António Gomes Correia, Miguel Azenha, Paulo J.S. Cruz, Paulo Novais, Paulo Pereira
Herausgeber (Verlag)	Springer, Cham
Seiten	3-19
Seitenumfang	17
Band	306
ISBN (elektronisch)	978-3-031-20241-4
ISBN (Print)	978-3-031-20240-7
DOIs	https://doi.org/10.1007/978-3-031-20241-4_1
Publikationsstatus	Veröffentlicht - 2023
Veranstaltung	International Society for Intelligent Construction 2022 Conference: Trends on Construction n the Post-Digital Era: ISIC 2022 - Avenida D. Afonso Henriques, 701, Guimarães, Portugal Dauer: 6 Sept. 2022 → 9 Sept. 2022 Konferenznummer: 3 https://icisic2022.com/

Publikationsreihe

Name	Lecture Notes in Civil Engineering
Band	306 LNCE
ISSN (Print)	2366-2557
ISSN (elektronisch)	2366-2565

Konferenz

Konferenz	International Society for Intelligent Construction 2022 Conference: Trends on Construction n the Post-Digital Era
Kurztitel	ISIC 2022
Land/Gebiet	Portugal
Ort	Guimarães
Zeitraum	6/09/22 → 9/09/22
Internetadresse	https://icisic2022.com/

ASJC Scopus subject areas

Tief- und Ingenieurbau

Zugriff auf Dokument

10.1007/978-3-031-20241-4_1

Andere Dateien und Links

Verknüpfung zur Publikation in Scopus

1 Konferenz oder Fachtagung (Teilnahme an/Organisation von)
1 Vortrag bei Konferenz oder Fachtagung

A WGAN Approach to Synthetic TBM Data Generation
Paul Johannes Unterlaß (Redner/in)
8 Sept. 2022
Aktivität: Vortrag oder Präsentation › Vortrag bei Konferenz oder Fachtagung › Science to science
International Society for Intelligent Construction 2022 Conference: Trends on Construction n the Post-Digital Era
Paul Johannes Unterlaß (Teilnehmer/-in) & Alla Sapronova (Teilnehmer/-in)
6 Sept. 2022 → 9 Sept. 2022
Aktivität: Teilnahme an / Organisation von › Konferenz oder Fachtagung (Teilnahme an/Organisation von)

Dieses zitieren

Unterlaß, P. J., Erharter, G. H., Sapronova, A., & Marcher, T. (2023). A WGAN Approach to Synthetic TBM Data Generation. in A. Gomes Correia, M. Azenha, P. J. S. Cruz, P. Novais, & P. Pereira (Hrsg.), Trends on Construction in the Digital Era - Proceedings of ISIC 2022 (Band 306, S. 3-19). (Lecture Notes in Civil Engineering; Band 306 LNCE). Springer, Cham. https://doi.org/10.1007/978-3-031-20241-4_1

A WGAN Approach to Synthetic TBM Data Generation. / Unterlaß, Paul Johannes ; Erharter, Georg H.; Sapronova, Alla et al.
Trends on Construction in the Digital Era - Proceedings of ISIC 2022. Hrsg. / António Gomes Correia; Miguel Azenha; Paulo J.S. Cruz; Paulo Novais; Paulo Pereira. Band 306 Springer, Cham, 2023. S. 3-19 (Lecture Notes in Civil Engineering; Band 306 LNCE).

Publikation: Beitrag in Buch/Bericht/Konferenzband › Beitrag in einem Konferenzband › Begutachtung

Unterlaß, PJ , Erharter, GH , Sapronova, A & Marcher, T 2023, A WGAN Approach to Synthetic TBM Data Generation. in A Gomes Correia, M Azenha, PJS Cruz, P Novais & P Pereira (Hrsg.), Trends on Construction in the Digital Era - Proceedings of ISIC 2022. Bd. 306, Lecture Notes in Civil Engineering, Bd. 306 LNCE, Springer, Cham, S. 3-19, International Society for Intelligent Construction 2022 Conference: Trends on Construction n the Post-Digital Era, Guimarães, Portugal, 6/09/22. https://doi.org/10.1007/978-3-031-20241-4_1

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title = "A WGAN Approach to Synthetic TBM Data Generation",

abstract = "In this work we propose a generative adversarial network (GAN) based approach of generating synthetic geotechnical data for further applications in research and education. Geotechnical data generated by GANs shows similar characteristics as the original data, but still presents unique samples with no connection to the technical content of the original data. The data can therefore be made available publicly without any legal issues.A WGAN (Wasserstein GAN) algorithm is used to generate synthetic tunnel boring machine (TBM) operational data based on real data from a major European tunnel construction site. The demands on the synthetic TBM data are of a dualistic nature: on the one hand, the data has to be sufficiently dissimilar to the original data, so that it does not create confidentiality issues (demand for originality). On the other hand, it has to show the same patterns and follow the same rules as the original data, so that it can be used as if it were real TBM data (demand for conformity). The WGAN model describes how a synthetic dataset is generated, in terms of a probabilistic model based on real data. By sampling from this model, we are able to generate new, unique synthetic and realistic TBM data.We show that the demands for originality and conformity of the newly generated data are fulfilled.",

keywords = "Synthetic data generation, Generative adversarial networks, Machine learning, TBM operational data, Tunnelling, Tunneling",

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booktitle = "Trends on Construction in the Digital Era - Proceedings of ISIC 2022",

note = "International Society for Intelligent Construction 2022 Conference: Trends on Construction n the Post-Digital Era : ISIC 2022, ISIC 2022 ; Conference date: 06-09-2022 Through 09-09-2022",

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N2 - In this work we propose a generative adversarial network (GAN) based approach of generating synthetic geotechnical data for further applications in research and education. Geotechnical data generated by GANs shows similar characteristics as the original data, but still presents unique samples with no connection to the technical content of the original data. The data can therefore be made available publicly without any legal issues.A WGAN (Wasserstein GAN) algorithm is used to generate synthetic tunnel boring machine (TBM) operational data based on real data from a major European tunnel construction site. The demands on the synthetic TBM data are of a dualistic nature: on the one hand, the data has to be sufficiently dissimilar to the original data, so that it does not create confidentiality issues (demand for originality). On the other hand, it has to show the same patterns and follow the same rules as the original data, so that it can be used as if it were real TBM data (demand for conformity). The WGAN model describes how a synthetic dataset is generated, in terms of a probabilistic model based on real data. By sampling from this model, we are able to generate new, unique synthetic and realistic TBM data.We show that the demands for originality and conformity of the newly generated data are fulfilled.

AB - In this work we propose a generative adversarial network (GAN) based approach of generating synthetic geotechnical data for further applications in research and education. Geotechnical data generated by GANs shows similar characteristics as the original data, but still presents unique samples with no connection to the technical content of the original data. The data can therefore be made available publicly without any legal issues.A WGAN (Wasserstein GAN) algorithm is used to generate synthetic tunnel boring machine (TBM) operational data based on real data from a major European tunnel construction site. The demands on the synthetic TBM data are of a dualistic nature: on the one hand, the data has to be sufficiently dissimilar to the original data, so that it does not create confidentiality issues (demand for originality). On the other hand, it has to show the same patterns and follow the same rules as the original data, so that it can be used as if it were real TBM data (demand for conformity). The WGAN model describes how a synthetic dataset is generated, in terms of a probabilistic model based on real data. By sampling from this model, we are able to generate new, unique synthetic and realistic TBM data.We show that the demands for originality and conformity of the newly generated data are fulfilled.

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A WGAN Approach to Synthetic TBM Data Generation

Abstract

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Konferenz

ASJC Scopus subject areas

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A WGAN Approach to Synthetic TBM Data Generation

International Society for Intelligent Construction 2022 Conference: Trends on Construction n the Post-Digital Era

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