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

Anika Terbuch; Alexander Zöhrer; Vincent Winter; Paul O'Leary; Negin Khalilimotlaghkasmaei; Gernot Steiner

doi:10.1002/geot.202200028

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

Anika Terbuch, Alexander Zöhrer, Vincent Winter, Paul O'Leary, Negin Khalilimotlaghkasmaei, Gernot Steiner

Chair of Automation and Measurement (530)

Keller Grundbau GmbH

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

1 Citation (Scopus)

Original language	English
Pages (from-to)	658-664
Number of pages	7
Journal	Geomechanics and tunnelling
Volume	15.2022
Issue number	5
DOIs	https://doi.org/10.1002/geot.202200028 https://doi.org/10.1002/geot.202200028
Publication status	Published - 4 Oct 2022

Bibliographical note

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

Keywords

Artificial Ingelligence in Geotechnical Engineering
General
hybrid learning
KPI analysis
outlier detection
quality monitoring
time-series
vibro ground improvement

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10.1002/geot.202200028Licence: Unspecified
10.1002/geot.202200028

Cite this

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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",

year = "2022",

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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

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

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U2 - 10.1002/geot.202200028

DO - 10.1002/geot.202200028

M3 - Article

AN - SCOPUS:85139267708

SN - 1865-7362

VL - 15.2022

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EP - 664

JO - Geomechanics and tunnelling

JF - Geomechanics and tunnelling

IS - 5

ER -

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

Bibliographical note

Keywords

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71. Geomechanik Kolloquium 2022

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

Bibliographical note

Keywords

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71. Geomechanik Kolloquium 2022

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