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This paper presents a semi-supervised learning workflow for estimating geotechnical soil properties at offshore wind farm sites using ultrahigh-resolution seismic surveys and cone-penetration testing data. By employing two convolutional neural networks, the workflow automates soil property estimation, significantly reducing turnaround time compared to traditional methods. The proposed approach has shown promising results in a case study at the Hollandse Kust Zuid wind farm, demonstrating improved efficiency

Author

k57f4mtkfb

Language

EN