Quality monitoring in vibro ground improvement: A hybrid machine learning approach

Research output: Contribution to journal › Article › Research › peer-review

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Quality monitoring in vibro ground improvement: A hybrid machine learning approach. / Terbuch, Anika; Zöhrer, Alexander; Winter, Vincent et al.
In: Geomechanics and tunnelling, Vol. 15.2022, No. 5, 04.10.2022, p. 658-664.

Research output: Contribution to journal › Article › Research › peer-review

Harvard

Terbuch, A, Zöhrer, A, Winter, V, O'Leary, P, Khalilimotlaghkasmaei, N & Steiner, G 2022, 'Quality monitoring in vibro ground improvement: A hybrid machine learning approach', Geomechanics and tunnelling, vol. 15.2022, no. 5, pp. 658-664. https://doi.org/10.1002/geot.202200028, https://doi.org/10.1002/geot.202200028

APA

Terbuch, A., Zöhrer, A., Winter, V., O'Leary, P., Khalilimotlaghkasmaei, N., & Steiner, G. (2022). Quality monitoring in vibro ground improvement: A hybrid machine learning approach. Geomechanics and tunnelling, 15.2022(5), 658-664. https://doi.org/10.1002/geot.202200028, https://doi.org/10.1002/geot.202200028

Vancouver

Terbuch A, Zöhrer A, Winter V, O'Leary P, Khalilimotlaghkasmaei N, Steiner G. Quality monitoring in vibro ground improvement: A hybrid machine learning approach. Geomechanics and tunnelling. 2022 Oct 4;15.2022(5):658-664. doi: 10.1002/geot.202200028, 10.1002/geot.202200028

Author

Terbuch, Anika ; Zöhrer, Alexander ; Winter, Vincent et al. / Quality monitoring in vibro ground improvement : A hybrid machine learning approach. In: Geomechanics and tunnelling. 2022 ; Vol. 15.2022, No. 5. pp. 658-664.

Bibtex - Download

@article{6b2861f03ba34c9e8113c76e3fe3277e,
title = "Quality monitoring in vibro ground improvement: A hybrid machine learning approach",
keywords = "Artificial Ingelligence in Geotechnical Engineering, General, hybrid learning, KPI analysis, outlier detection, quality monitoring, time-series, vibro ground improvement",
author = "Anika Terbuch and Alexander Z{\"o}hrer and Vincent Winter and Paul O'Leary and Negin Khalilimotlaghkasmaei and Gernot Steiner",
note = "Publisher Copyright: {\textcopyright} 2022, Ernst und Sohn. All rights reserved.",
year = "2022",
month = oct,
day = "4",
doi = "10.1002/geot.202200028",
language = "English",
volume = "15.2022",
pages = "658--664",
journal = "Geomechanics and tunnelling",
issn = "1865-7362",
publisher = "Wiley-VCH ",
number = "5",

}

RIS (suitable for import to EndNote) - Download

TY - JOUR

T1 - Quality monitoring in vibro ground improvement

T2 - A hybrid machine learning approach

AU - Terbuch, Anika

AU - Zöhrer, Alexander

AU - Winter, Vincent

AU - O'Leary, Paul

AU - Khalilimotlaghkasmaei, Negin

AU - Steiner, Gernot

N1 - Publisher Copyright: © 2022, Ernst und Sohn. All rights reserved.

PY - 2022/10/4

Y1 - 2022/10/4

KW - Artificial Ingelligence in Geotechnical Engineering

KW - General

KW - hybrid learning

KW - KPI analysis

KW - outlier detection

KW - quality monitoring

KW - time-series

KW - vibro ground improvement

UR - http://www.scopus.com/inward/record.url?scp=85139267708&partnerID=8YFLogxK

U2 - 10.1002/geot.202200028

DO - 10.1002/geot.202200028

M3 - Article

AN - SCOPUS:85139267708

VL - 15.2022

SP - 658

EP - 664

JO - Geomechanics and tunnelling

JF - Geomechanics and tunnelling

SN - 1865-7362

IS - 5

ER -

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