Multifidelity Data Fusion for the Estimation of Static Stiffness of Suction Caisson Foundations in Layered Soil
Publication: Journal of Geotechnical and Geoenvironmental Engineering
Volume 150, Issue 8
Abstract
The static stiffness of suction caisson foundations is an important engineering factor for offshore wind foundation design. However, existing simplified design models are mainly developed for nonlayered soil conditions, and their accuracy for layered soil conditions is uncertain. This creates a challenge for designing these foundations in offshore wind farm sites, where layered soil conditions are commonplace. To address this, this paper proposes a multifidelity data fusion approach that combines information from different physics-based models of varying accuracy, data sparsity, and computational costs in order to improve the accuracy of stiffness estimations for layered soil conditions. The results indicate that the proposed approach is more accurate than both the simplified design model and a single-fidelity machine learning model, even with limited training data. The proposed method offers a promising data-efficient solution for fast and robust stiffness estimations, which could lead to more cost-effective offshore foundation designs.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
The second author is funded by the Royal Academy of Engineering under the Research Fellowship scheme.
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Published In
Journal of Geotechnical and Geoenvironmental Engineering
Volume 150 • Issue 8 • August 2024
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© 2024 American Society of Civil Engineers.
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Received: Mar 31, 2023
Accepted: Mar 22, 2024
Published online: Jun 6, 2024
Published in print: Aug 1, 2024
Discussion open until: Nov 6, 2024
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