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 A1 - Baeßler, Matthias 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 2025) 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 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-634417 SN - 978-2-85782-758-0 DO - https://doi.org/10.53243/ISFOG2025-323 SP - 505 EP - 510 PB - International Society for Soil Mechanics and Geotechnical Engineering CY - London AN - OPUS4-63441 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Zinas, Orestis A1 - Papaioannou, Iason A1 - Schneider, Ronald A1 - Cuéllar, Pablo T1 - Multivariate Gaussian Process Regression for 3D site characterization from CPT and categorical borehole data N2 - Accurate prediction of subsurface stratigraphy and geotechnical properties, along with quantification of associated uncertainties, is essential for improving the design and assessment of geotechnical structures. Several studies have utilized indirect data from Cone Penetration Tests (CPTs) and employed statistical and Machine Learning methods to quantify the geological and geotechnical uncertainty. Incorporating direct borehole data can reduce uncertainties. This study proposes a computationally efficient multivariate Gaussian Process model that utilizes site-specific data and: (i) jointly models multiple categorical (USCS labels) and continuous CPT variables, (ii) learns a non-separable covariance structure leveraging the Linear Model of Coregionalization, and (iii) predicts a USCS based stratigraphy and CPT parameters at any location within the 3D domain. The results demonstrate that integrating geotechnical and geological data into a unified model yields more reliable predictions of subsurface stratification, enabling the parallel interpretation of both USCS classification and CPT profiles. Importantly, the model demonstrates its potential to integrate multiple variables from different sources and data types, contributing to the advancement of methodologies for the joint modeling of geotechnical, geological, and geophysical data. KW - Geotechnical site-characterization KW - Cone Penetration Tests KW - Stratigraphy prediction KW - Multivariate Gaussian process KW - Variational inference KW - Linear Model of Coregionalization PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-629456 DO - https://doi.org/10.1016/j.enggeo.2025.108052 SN - 1872-6917 VL - 352 SP - 1 EP - 19 PB - Elsevier B.V. AN - OPUS4-62945 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 - Multivariate Gaussian Process for 3D subsurface stratigraphy prediction from CPT and labelled borehole data N2 - Quantifying uncertainties in subsurface properties and stratigraphy can lead to better understanding of the ground conditions and enhance the design and assessment of geotechnical structures. Several studies have utilized Cone Penetration Test (CPT) data and employed Bayesian and Machine Learning methods to quantify the geological uncertainty, based on the Robertson’s soil classification charts and the Soil Behaviour Type Index (Ic). The incorporation of borehole data can reduce the stratigraphic uncertainty. Significant challenges can arise, however, mainly due to the intrinsic differences between field and laboratory-based soil classification systems, which can potentially lead to inconsistent soil classification. To this end, this study proposes a multivariate Gaussian Process model that utilizes site-specific data and: i) jointly models multiple categorical (USCS labels) and continuous (Ic) variables, ii) learns a (shared) spatial correlation structure and the betweenoutputs covariance, and iii) produces two types of dependent classification outputs. The results indicate that the integration of geotechnical and geological information into a unified model can provide more reliable predictions of the subsurface stratification, by allowing simultaneous interpretation of USCS and Ic profiles. Importantly, the model demonstrates the potential to integrate multiple variables of different types, aiming to contribute to the development of a methodology for joint modeling of geotechnical, geological and geophysical data. T2 - 7th International Conference on Geotechnical and Geophysical Site Characterization CY - Barcelona, Spain DA - 18.06.2024 KW - Geotechnical site-characterization KW - Probabilistic KW - Soil classification KW - Gaussian Process PY - 2024 SP - 1733 EP - 1740 AN - OPUS4-60712 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 -