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 - 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 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 -