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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 illustrates the methodology‘s great potential and benefits compared to the conventional deterministic approach based on interpolation procedures.
Die Prognose der Unsicherheiten in 3D-Baugrundmodellen für BIM verbessert die Risikobewertung und Entscheidungsfindung und ermöglicht eine wirtschaftlichere und nachhaltigere Planung und Ausführung von Baumaßnahmen. Basierend auf Open-Source-Software wird ein Ansatz zur Implementierung probabilistischer Baugrundmodelle im Industry-Foundation-Classes- (IFC-) Datenschema vorgeschlagen. Die Grundlagen von IFC, die für die Erstellung von Fachobjekt-Geometrien sowie deren Georeferenzierung und Attribuierung erforderlich sind, werden erläutert. Zwei probabilistische 3D-Modelle, die mit Bohrprofil- bzw. Cone-Penetration-Test- (CPT-) Daten erstellt wurden, dienen als Anwendungsbeispiele; diese zeigen die prognostizierte Geometrie der Bodenschichten mit den zugehörigen Unsicherheiten basierend auf Volume Pixel (Voxel). Solange Voxel-Modelle nicht in IFC integriert sind, bieten Isoflächen eine praktikable Zwischenlösung zur Darstellung von Bodenschichten und Unsicherheiten. Eine konsistente Georeferenzierung gewährleistet eine korrekte Positionierung im Koordinationsmodell. Die Modellierung von Baugrund- und geotechnischen Daten wird für verschiedene IFC-Versionen gezeigt. Die aktuelle IFC-Version 4 verbessert die Oberflächenmodellierung und reduziert die Datenredundanz. Der vorgestellte Ansatz ermöglicht eine maßgeschneiderte Implementierung probabilistischer Baugrundmodelle in IFC und eine effizientere Zusammenarbeit der an der Erstellung des BIM-Modells beteiligten Experten.
In this study, we investigate with the Discrete Element Method (DEM) the mechanical behavior of a cohesionless granular material under undrained true triaxial conditions, considering both monotonic and cyclic loading. We link the microstructure evolution within the granular assembly to its macroscopic cyclic response. To capture the mechanical response of our reference material (Karlsruhe fine sand), a rolling resistance linear contact model along with spherical particles is calibrated through a trial-and-error process, adjusting the model parameters to capture the experimentally observed behavior as close as possible. A series of cyclic undrained triaxial tests were simulated to investigate the micromechanical processes underlying liquefaction of sand under cyclic shearing. We analyzed the evolution of various fabric indices, including the redundancy index, contact normal orientations, and fabric anisotropy in relation to the pre- and post-liquefaction responses. The results reveal that a redundancy index below unity provides a unified criterion for the loss of the isostatic condition within the granular assembly, which triggers the onset of liquefaction. Throughout the cyclic loading process, sliding-dominant contact-yielding mechanisms remain prevalent. Additionally, significant changes in contact normal orientation and increasing fabric anisotropy dependent on the induced axial strain occur as the sample undergoes post-liquefaction deformation.
In geotechnical engineering, the heterogeneity and variability of soil properties can pose a significant challenge for the design and construction of structures. This often-complex subsurface environment is generally represented by simplified homogeneous subsoil models derived from in-situ soundings and boreholes. However, these models do not consider the natural variability of the subsoil and the related uncertainties and, as such, disregard their influence on the design of geotechnical structures. Geostatistical models can be used to capture the inherent spatial subsoil variability by generating random fields (equiprobable realizations). The Random Finite Element Method (RFEM) is an approach that can be used to investigate the influence of soil variability (random fields) on the design of geotechnical structures. In this study, the technical implementation of the RFEM and its application for a twin tunnel case study in Munich (Germany) were examined. The influence of unexpected geological conditions was conducted within a probabilistic framework with the aim of comparing the results with the deterministic approach (idealized subsoil model). A conventional sensitivity analysis of soil variability was also performed. The results of this study demonstrate that deterministic approaches alone cannot adequately characterize tunneling-induced ground and foundation responses in heterogeneous subsoil conditions. Incorporating subsoil variability through RFEM provides a more realistic assessment, enabling engineers to better quantify risk, identify critical settlement zones, and ultimately improve the reliability of tunnel design.