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 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 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 - Schneider, Ronald T1 - Probabilistic approaches to the design and assessment of offshore foundations considering spatially varying site conditions N2 - In this presentation, we discuss the potential of probabilistic approaches to the design and assessment of offshore foundations. The potential is demonstrated in a numerical example considering a laterally loaded monopile. As an outlook, we present a concept for managing the risk associated with installing large monopiles. T2 - 3rd International Conference on Structural Integrity for Offshore Energy Industry (SI 2021) CY - Online meeting DA - 15.11.2021 KW - Offhore KW - Foundations KW - Probabilistic KW - Design KW - Assessment PY - 2021 AN - OPUS4-53748 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -