Quantification and validation of uncertainties in subsoil models

  • In infrastructure planning and construction, modeling the subsoil and its associated uncertainty is a fundamental task of geotechnical engineers. However, probabilistic methods and tools for quantifying and displaying the uncertainty of the subsoil models are rarely used in practice where deterministic interpolation dominates. In digital planning using Building Information Modeling (BIM), the probabilistic approach supports creating a discipline model in which the uncertainties of the spatial layer structure are statistically quantified to evaluate the georisks in the design and execution of civil constructions. This article presents a case study using a combination of Sequential Gaussian Simulation (SGSIM) and Sequential Indicator Simulation (SISIM) to account for uncertainties in soil layer geometry. In a case study at the Munich Town Hall, a geostatistical approach is applied and validated based on 70 bore logs, whereby the probabilities for the occurrence of a particular layer are spatially quantified. The case study illustratesIn infrastructure planning and construction, modeling the subsoil and its associated uncertainty is a fundamental task of geotechnical engineers. However, probabilistic methods and tools for quantifying and displaying the uncertainty of the subsoil models are rarely used in practice where deterministic interpolation dominates. In digital planning using Building Information Modeling (BIM), the probabilistic approach supports creating a discipline model in which the uncertainties of the spatial layer structure are statistically quantified to evaluate the georisks in the design and execution of civil constructions. This article presents a case study using a combination of Sequential Gaussian Simulation (SGSIM) and Sequential Indicator Simulation (SISIM) to account for uncertainties in soil layer geometry. In a case study at the Munich Town Hall, a geostatistical approach is applied and validated based on 70 bore logs, whereby the probabilities for the occurrence of a particular layer are spatially quantified. The case study illustrates the methodology‘s great potential and benefits compared to the conventional deterministic approach based on interpolation procedures.zeige mehrzeige weniger

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Metadaten
Verfasserangaben:Alexander Wiegel, Andrés Peña-OlarteORCiDGND, Roberto Cudmani
DOI:https://doi.org/10.1002/gete.202400011
ISSN:0172-6145
Titel des übergeordneten Werkes (Englisch):Geotechnik
Untertitel (Englisch):Case study at the New Town Hall, Munich
Verlag:Wiley-Blackwell
Verlagsort:Oxford
Dokumentart:Wissenschaftlicher Artikel
Sprache:Englisch
Jahr der Erstveröffentlichung:2024
Veröffentlichende Institution:Fachhochschule Potsdam
Datum der Freischaltung:25.11.2024
GND-Schlagwort:Building Information Modeling; Gauß-Zufallsfeld; Geostatistik
Jahrgang:47
Ausgabe / Heft:4
Erste Seite:269
Letzte Seite:281
Fachbereiche und Zentrale Einrichtungen:FB3 Bauingenieurwesen
DDC-Klassifikation:600 Technik, Medizin, angewandte Wissenschaften / 620 Ingenieurwissenschaften
Lizenz (Deutsch):Keine öffentliche Lizenz - es gilt das deutsche Urheberrecht
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