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Physics-Informed Surrogate Modeling for Offshore Helical Pile Design Optimization

CORDIS · observation · Publication date unknown

tions limits their broader application. This project aims to develop an open-source decision-making tool to optimize offshore HP design using data-driven techniques. By incorporating physics-informed machine learning algorithms (PIA), the tool will enable surrogate modeling to replace traditional numerical analyses, allowing accurate predictions of HP’s static and dynamic performance, including installation effects. This will improve design processes, reduce costs, and contribute to sustainable offshore engineering practices. The fellowship has three key Research and Innovation Objectives (R&IO): (1) to establish a validated numerical model that simulates HP load-bearing performance, including installation effects, which will form a database for (2) developing PIA-based data-driven models to assess HP capacity. These models will then be integrated into (3) an open-source decision-making tool using reliability-based foundation design approach (RBFDA) to optimize HP design under various conditions. The fellowship will be conducted at NGI, Norway, with a secondment at OsloMet, Norway. T

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recordType

award

status

SIGNED

region

EU

value

251578.56

unit

EUR