TY - CONF A1 - Zinas, Orestis A1 - Wilhelm, Sigrid T1 - CPT-based probabilistic design of monopile foundations N2 - This work was presented in the two-day workshop ‘Reliability-based methods in geotechnics’ organized by BAM and DGGT AK 2.15 in cooperation with the DGGT Academy. The presentation provides an overview of the sources of uncertainty in geotechnical analyses. A probabilistic 3D ground model of the cone-tip resistance from CPTs is developed utilizing sparse CPT data from a real offshore wind farm site in the North Sea. The predictive cone tip resistance model is used to derive a design property, such as the friction angle, using geotechnical transformation models. The friction angle profiles are then used to show an example of monopile design under uncertainty. T2 - Workshop: Einführung in die zuverlässigkeitsbasierte Bemessung in der Geotechnik 2024 CY - Berlin, Germany DA - 19.11.2024 KW - Geotechnical site-characterization KW - CPT KW - Spatial variability KW - Gaussian process regression KW - Design of monopiles KW - Offshore wind farms PY - 2024 AN - OPUS4-61716 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Zinas, Orestis A1 - Papaioannou, Iason A1 - Schneider, Ronald A1 - Cuéllar, Pablo T1 - Spatial modeling of heterogeneous geotechnical site investigation data using multivariate Gaussian Process N2 - This presentation is part of the Engineering Risk Analysis group open seminars. It aims to showcase the results of an ongoing study centered on developing a novel probabilistic methodology for 3D geotechnical site characterization. This methodology integrates data from Cone Penetration Tests (CPTs) and categorical borehole data. The presentation covers the mathematical details of the proposed Multivariate Gaussian Process model and demonstrates its application to a real geotechnical site in New Zealand. T2 - ERA Seminars CY - Munich, Germany DA - 24.07.2024 KW - Geotechnical site-characterization KW - CPT KW - Boreholes KW - Gaussian Process PY - 2024 AN - OPUS4-60716 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Zinas, Orestis T1 - 3D Probabilistic Site Characterization N2 - The aim of the study is to infer the soil stratification from the provided CPT and borehole data. We infer the soil type at any location within the domain of interest from the SBT index Ic (Robertson, 2009). This index can be directly related to the CPT data through an empirical correlation model. In addition, the soil classes contained in the borehole logs can be expressed as bounds on Ic. A log-transformation was applied to Ic, Y = ln(Ic), and Y was modelled by a 3D Random Field, with a fully Bayesian hierarchical Gaussian Process model to explicitly capture uncertainties. T2 - 19th eawe PhD Seminar CY - Hannover, Germany DA - 06.09.2023 KW - Wind Energy KW - Site-characterization KW - Probabilistic PY - 2023 AN - OPUS4-58940 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Zinas, Orestis T1 - 3D spatial modelling of CPT data for probabilistic preliminary assessment of potential pile tip damage upon collision with boulders N2 - Tip damage of monopiles from boulder collisions during installation has emerged as a critical design issue, primarily due to the combination of the widespread use of large-diameter piles and the complexity of the ground conditions at the available sites. Recently, a framework relating potential pile tip damage to cone tip resistance from Cone Penetration Tests (CPTs) has been proposed in the literature. Gaussian processes are powerful stochastic models that enable probabilistic spatial interpolation of soil data at any location within a site. On this basis, this study utilizes sparse CPT data from a site in the North Sea, to first develop an efficient Gaussian process regression model, which is used to derive a three-dimensional (3D) probabilistic predictive map of the cone tip resistance. Assuming deterministic loading conditions and a factual collision with a boulder of pre-defined properties, the cone tip resistance predictive model is subsequently used for a probabilistic preliminary assessment of potential pile tip damage. Results of the analysis are realistic 3D probability maps of potential damage that aim to support engineering judgment and contribute towards cost-effective site investigation planning and offshore wind farm design. T2 - 5th International Symposium on Frontiers in Offshore Geotechnics (ISFOG) CY - Nantes, France DA - 09.06.2025 KW - CPT data KW - Gaussian process regression KW - Boulder impact KW - Preliminary pile tip damage assessment PY - 2025 AN - OPUS4-63444 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Zinas, Orestis T1 - Stochastic variational Gaussian process for 3D site characterization N2 - This work was presented at the the 2nd Workshop on Future of Machine Learning in Geotechnics (2FOMLIG) & the 5th Machine Learning in Geotechnics Dialogue (5MLIGD), in Chengdu, China. In this study, we propose an efficient multivariate Gaussian process regression model, utilizing the Linear Model of Coregionalization, stochastic variational inference and Dirichlet-based transformations, to jointly model continuous CPT and categorical (USCS) borehole variables. The predictive performance of the model is assessed using a real dataset from a site located in Christchurch, New Zealand. T2 - 2nd Workshop on Future of Machine Learning in Geotechnics (2FOMLIG) & the 5th Machine Learning in Geotechnics Dialogue (5MLIGD) CY - Chengdu, China DA - 11.10.2024 KW - Geotechnical site-characterization KW - Cone Penetration Tests KW - Multivariate Gaussian process regression KW - Categorical borehole data KW - Stratigraphy prediction KW - Linear Model of Coregionalization PY - 2024 AN - OPUS4-61775 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Zinas, Orestis T1 - CPT-based probabilistic analysis of monopile foundations considering spatial and transformation uncertainties N2 - This study utilizes cone penetration testing data data from a real offshore windfarm project in the North Sea and presents a method for incorporating both, statistical and spatial uncertainties, in a reliability-based assessment of monopile foundations. Initially, a Gaussian Process regression model is constructed to predict a 3D map of the cone tip resistance and the associated uncertainties in the predictions and the hyperparameters, leveraging Markov Chain Monte Carlo methods. The CPT-based prediction is combined with a correlation derived from data collected at a nearby site to predict the probability distribution of the friction angle at a test location, which is subsequently used to evaluate the probability of failure for a monopile foundation with a finite element model. T2 - 9th International Symposiumon Geotechnical Safety and Risk (ISGSR) CY - Oslo, Norway DA - 25.08.2025 KW - Monopile design KW - Cone Penetration Test KW - Gaussian process regression KW - Bayesian Inference PY - 2025 AN - OPUS4-64001 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Zinas, Orestis T1 - Potentials of probabilistic approaches in offshore foundation installation N2 - This presentation discusses the potentials of probabilistic methods in offshore foundation installation, from the perspective of probabilistic ground models and data-driven site characterization. We discuss about methodologies for utilizing site-specific geotechnical (CPT) and geological data, aiming to construct an integrated ground model that can predict stratigraphic profiles and useful for geotechnical design parameters at any location within a 3D domain. The predicted parameters and stratigraphy are then used to predict the probability of potential pile tip damage, upon collision with a boulder. T2 - Colloquium Buckling of Offshore Wind Energy Structures CY - Berlin, Germany DA - 14.02.2024 KW - Wind KW - Offshore KW - Buckling KW - Probabilistic KW - Ground PY - 2024 AN - OPUS4-59538 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Zinas, Orestis T1 - A Modular Gaussian Process Regression Toolbox for Uncertainty Aware Geotechnical Site Characterization N2 - A modular Gaussian Process Regression toolbox for efficient large-scale geotechnical site characterization from sparse 1D data was presented at the Third Future of Machine Learning in Geotechnics (3FOMLIG), Florence, Italy, October 16, 2025. The PyTorch/GPyTorch-based framework enables multivariate modeling of correlated soil properties and joint regression-classification of continuous CPT parameters with categorical soil units through Dirichlet transformations. Stochastic Variational Inference reduces computational complexity from O(N³) to O(M³), enabling GPU-accelerated processing of 100,000+ measurements. Validated on a 33 km² North Sea offshore wind farm site with 100+ sparse investigation points, the toolbox generates uncertainty-aware 3D predictions, supporting univariate, multivariate (LMC), and sequential multi-group modeling strategies. T2 - Third Future of Machine Learning in Geotechnics (3FOMLIG) CY - Florence, Italy DA - 15.10.2025 KW - Probabilistic site-characterization KW - Gaussian process regression KW - Bayesian inference KW - Offshore Wind Farms PY - 2025 AN - OPUS4-64423 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Wiehle, Philipp T1 - Feuchteverhalten von Lehmsteinmauerwerk unter natürlichen Klimabedingungen N2 - Druckfestigkeit und Elastizitätsmodul von Lehmsteinmauerwerk nehmen mit steigendem Feuchtegehalt ab, weswegen die Mauerwerksdruckfestigkeit bei der Bemessung gemäß DIN 18940 in Abhängigkeit der maximal zu erwartenden Ausgleichsfeuchte abgemindert wird. Da bisher allerdings keine Messdaten zum Feuchtegehalt von Lehmsteinmauerwerk unter natürlichen Klimabedingungen vorliegen, stellen die normativen Grenzwerte lediglich eine konservative Abschätzung dar. Um die Feuchtegehalte von Lehmsteinmauerwerk und somit die Tragfähigkeit realitätsnah einschätzen zu können, wurden im Rahmen der vorliegenden Arbeit sowohl experimentelle als auch numerische Untersuchungen zum feuchtetechnischen Verhalten durchgeführt. Dabei wurden erstmalig magnetresonanzspektroskopische Untersuchungen an tragenden Lehmsteinen unterschiedlicher Herstellungsmethoden angewandt Weiterhin wurden Luftfeuchtesensoren in Lehmsteinmauerwerk eingebettet, um hygrische Langzeitfeuchtemessungen unter natürlichen Klimabedingungen durchzuführen. Auf Basis dieser Erkenntnisse wurde schließlich ein numerisches Modell kalibriert und Parameterstudien durchgeführt, um den unter realen Klimabedingungen tatsächlich auftretenden Feuchtegehalt im Lehmmauerwerk sowie dessen Verteilung über den Querschnitt einschätzen zu können und die normativen Grenzwerte zu überprüfen. Gezeigt wurde, dass die realitätsnahe instationäre Berechnung des Feuchtetransports mit üblichen hygrothermischen Simulationsprogrammen auf Basis einfacher feuchtetechnischer Kennwerte auch bei Lehmbaustoffen möglich ist. Der Einfluss des Quellens und Schwindens der enthaltenen Tonminerale und organischen Bestandteile wurde durch eine Modifikation bei der Ermittlung des Wasseraufnahmekoeffizienten explizit berücksichtigt und ist somit auch in den daraus abgeleiteten Transportkoeffizienten enthalten. Darüber hinaus wurde die Feuchtespeicherfunktion im überhygroskopischen Bereich auf Grundlage der Porenvolumenverteilung abgeschätzt. Die Approximation der Transportkoeffizienten und der Feuchtespeicherfunktion stellt dabei eine wesentliche Vereinfachung für Lehmbaustoffe dar, da ihre experimentelle Bestimmung aufgrund des Kontaktes mit Flüssigwasser kaum möglich ist. In Bezug auf das Feuchteverhalten üblicher Außenwandaufbauten von Wohnräumen konnte festgestellt werden, dass die Ausgleichsfeuchte im Lehmsteinmauerwerk stets unterhalb des in Nutzungsklasse 1 gemäß DIN 18940 zulässigen Grenzwertes von 65 % liegt. Je nach Putzsystem und Dämmstoff reduziert sich die Ausgleichsfeuchte auf Werte unter 60 %, wobei sich eine annähernd gleichmäßige Verteilung der Feuchte über den Mauerwerksquerschnitt hinweg einstellt. Bei Außenwänden von unbeheizten Räumen ergab sich eine maximale Ausgleichsfeuchte, die über weite Teile des Querschnitts unterhalb von 80 % lag. Der Ansatz einer maximalen Ausgleichsfeuchte von 90 % in Nutzungsklasse 2 ist folglich zu hoch angesetzt. Die zulässige Ausgleichsfeuchte bzw. der Umgebungsfeuchtefaktor in Nutzungsklasse 2 sollte dementsprechend angepasst werden. T2 - LEHM 2024 - 9. Internationale Fachtagung für Lehmbau CY - Weimar, Germany DA - 27.09.2024 KW - Lehm KW - Mauerwerk KW - Feuchte KW - Tragverhalten KW - Bemessung PY - 2024 AN - OPUS4-61904 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Viefhues, Eva T1 - Fault detection for linear parameter varying systems under changes in the process noise covariance N2 - Detecting changes in the eigenstructure of linear systems is a comprehensively investigated subject. In particular, change detection methods based on hypothesis testing using Gaussian residuals have been developed previously. In such residuals, a reference model is confronted to data from the current system. In this paper, linear output-only Systems depending on a varying external physical parameter are considered. These systems are driven by process noise, whose covariance may also vary between measurements. To deal with the varying parameter, an interpolation approach is pursued, where a limited number of reference models { each estimated from data measured in a reference state { are interpolated to approximate an adequate reference model for the current parameter. The problem becomes more complex when the different points of interpolation correspond to different noise conditions. Then con icts May arise between the detection of changes in the eigenstructure due to a fault and the detection of changes due to different noise conditions. For this case, a new change detection approach is developed based on the interpolation of the eigenstructure at the reference points. The resulting approach is capable of change detection when both the external physical parameter and the process noise conditions are varying. This approach is validated on a numerical simulation of a mechanical system. T2 - 21st IFAC World Congress CY - Berlin, Germany DA - 13.07.2020 KW - Linear parameter varying systems KW - Fault detection KW - Changing process noise KW - Subspace-based residual KW - Model interpolation PY - 2020 AN - OPUS4-52366 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -