1887

Abstract

Summary

Synthetic cone penetrometer tests (CPTs) enable profiles of geotechnical resistance to be derived from 2D or 3D geophysical seismic survey data at any location across a wind farm site, alleviating dependence on interpolation between geotechnical CPTs. However, the lower vertical resolution of synthetic CPT data compared with in situ geotechnical CPT data introduces uncertainty in design outcomes. To explore the effect of synthetic CPT resolution on design outcome, this study applies co-located in situ geotechnical and geophysical data from the TNW wind farm site investigation, to calculate the required minimum volume of monopiles to assure ultimate limit state under lateral loading. Comparison between the two datasets and design outcomes has demonstrated that with synthetic CPTs, it is possible to obtain minimum monopile dimensions within 3% of those calculated with the in situ geotechnical CPT, corresponding to within 7% of the required capacity. This study highlights the potential for the use of synthetic CPTs in pile design to facilitate the expansion of offshore wind by reducing the time required for geotechnical site investigations and uncertainty in design outcome from interpolating between in situ geotechnical CPTs.

Loading

Article metrics loading...

/content/papers/10.3997/2214-4609.202521144
2025-10-27
2026-01-14
Loading full text...

Full text loading...

References

  1. Anastassopoulos, C., Charles, J.A. and Gourvenec, S.2023. Effect of CPT profile resolution on minimum required size of monopile for ultimate limit state design. In: 9th International SUT Offshore Site Investigation Geotechnics Conference Proceedings. 393–400., https://doi.org/10.3723/IPLP6449.
    [Google Scholar]
  2. Bolève, A., Eddies, R., Staring, M., Benboudiaf, Y., Pournaki, H. and Nepveaux, M.2025. Innovative cone resistance and sleeve friction prediction from geophysics based on a coupled geo-statistical and machine learning process. Artificial Intelligence in Geosciences, 6, https://doi.org/10.1016/j.aiig.2025.100110.
    [Google Scholar]
  3. Carpentier, S., Peuchen, J., Paap, B., Boullenger, B., Meijninger, B., Vandeweijer, V., Van Kesteren, W. and Van Erp, F.2021. Generating synthetic CPTs from marine seismic reflection data using a neural network approach. In: Second EAGE Workshop on Machine Learning. 1–3., https://doi.org/10.3997/2214-4609.202132008.
    [Google Scholar]
  4. Chen, J., Vissinga, M., Shen, Y., Hu, S., Beal, E. and Newlin, J.2021. Machine Learning-Based Digital Integration of Geotechnical and Ultrahigh-Frequency Geophysical Data for Offshore Site Characterizations. Journal of Geotechnical and Geoenvironmental Engineering, 147, https://doi.org/10.1061/(ASCE)GT.1943-5606.0002702.
    [Google Scholar]
  5. Cox, P., Boylan, C., SmithC., Dalgaard, E., Horn, F., Stuyts, B., Bronkhorst, J., Wezenberg, S., Dyer, N. and Blacker, K.2024. Nederwiek Zuid (NL) Integrated Ground Model: AVO-Compliant UHRS Processing for Elastic Pre-Stack Inversion. In: First EAGE/SUT Workshop on Integrated Site Characterization for Offshore Renewable Energy. 1–6., https://doi.org/10.3997/2214-4609.202480019.
    [Google Scholar]
  6. Fugro. 2020. Geotechnical Survey - Seafloor In Situ Test Locations Report: Ten noorden van de Waddeneilanden Wind Farm Zone. https://offshorewind.rvo.nl/files/view/a72bda26-d6ca-4169-beb3-fc783c2847c5/1609857560tnw_20210105_fnlm_seafloor%20in%20situ%20test%20locations-fzip
    [Google Scholar]
  7. GWEC. 2024. Global Offshore Wind Report 2024. https://www.gwec.net/reports/globalofffshorewindreport.
    [Google Scholar]
  8. ISO. 2023. Oil and gas industries including lower carbon energy - Offshore structures, Marine Soil Investigations ISO 19901-8:2023, ISO.
    [Google Scholar]
  9. Klinkvort, R.T., Sauvin, G., Dujardin, J., Griffiths, L., Vardy, M.E. and Vanneste, M.2024. Cone Penetration Testing Prediction Using Seismo-Acoustic Data. In: 85th EAGE Annual Conference & Exhibition. 1–5., https://doi.org/10.3997/2214-4609.2024101434.
    [Google Scholar]
  10. MMT. 2020. 3D Geophysical Ultra High Resolution Survey (UHRS) Report: Ten noorden van de Waddeneilanden Wind Farm Zone. https://offshorewind.rvo.nl/files/view/e64ddc9a-05a1-4476-b9cd-37454ee105b4/tnw_20200508_mmt_gp_ultra-high-resolution-survey-uhrs-report.pdf
    [Google Scholar]
  11. NGI. 2022. Ten noorden van de Waddeneilanden Wind Farm Zone Integrated Ground Model. Doc No. 20190798-04-R. Client RVO.
    [Google Scholar]
  12. Robertson, P.K.1990. Soil classification using the cone penetration test. Canadian Geotechnical Journal, 27, 151–158, https://doi.org/10.1139/t90-014.
    [Google Scholar]
  13. Robertson, P.K.2009. Performance based earthquake design using the CPT. In: Proceedings of the International Conference on Performance-Based Design in Earthquake Geotechnical Engineering. 3–20., https://doi.org/10.1201/NOE0415556149.ch1.
    [Google Scholar]
  14. Robertson, P.K. and Wride, C.E.1998. Evaluating cyclic liquefaction potential using the cone penetration test. Canadian Geotechnical Journal, 35, 442–459, https://doi.org/10.1139/t98-017.
    [Google Scholar]
  15. Sauvin, G., Vanneste, M., Vardy, M.E., Klinkvort, R.T. and Forsberg, C.F.2019. Machine Learning and Quantitative Ground Models for Improving Offshore Wind Site Characterization. In: Offshore Technology Conference, https://doi.org/10.4043/29351-MS.
    [Google Scholar]
  16. Shoukat, G., Michel, G., Coughlan, M., Malekjafarian, A., Thusyanthan, I., Desmond, C. and Pakrashi, V.2023. Generation of Synthetic CPTs with Access to Limited Geotechnical Data for Offshore Sites. Energies, 16, 3817, https://doi.org/10.3390/en16093817.
    [Google Scholar]
  17. Siemann, L., Masoudi, P., Maraka, R., Opris, R., Pande, Y., Römer-Stange, N., Morales, N. and Mörz, T.2024. Comparison of Different Prediction Methods to Derive Synthetic CPT Profiles - An Offshore Wind Farm Case Study from the German North Sea. In: 7th International Conference on Geotechnical and Geophysical Site Characterization, https://doi.org/10.23967/isc.2024.233.
    [Google Scholar]
  18. Suryasentana, S.K. and Lehane, B.M.2014. Numerical derivation of CPT-based p-y curves for piles in sand. Géotechnique, 64, 186–194, https://doi.org/10.1680/geot.13.P.026.
    [Google Scholar]
  19. Truong, P. and Lehane, B.M.2014. Numerically derived CPT-based p-y curves for a soft clay modeled as an elastic perfectly plastic material. In: Proceedings of 3rd International Conference on Cone Penetration Testing - International Symposium on Cone Penetration Testing. 975–982.
    [Google Scholar]
  20. Vardy, M.E.2015. Deriving shallow‐water sediment properties using post‐stack acoustic impedance inversion. Near Surface Geophysics, 13, 143–154, https://doi.org/10.3997/1873-0604.2014045.
    [Google Scholar]
  21. Vardy, M.E., Vanneste, M., Henstock, T.J., Clare, M.A., Forsberg, C.F. and Provenzano, G.2017. State‐of‐the‐art remote characterization of shallow marine sediments: the road to a fully integrated solution. Near Surface Geophysics, 15, 387–402, https://doi.org/10.3997/1873-0604.2017024.
    [Google Scholar]
  22. Vardy, M.E., GSauvin, R.T.Klinkvort, M.Vanneste, A.Kort, and C.F.Forsberg. 2023a. Capturing Uncertainty in Quantitative Ground Models In: 9th International SUT Offshore Site Investigation Geotechnics Conference Proceedings – Innovative Geotechnologies for Energy Transition. 2012–2019, https://doi.org/10.3723/QIUW9196.
    [Google Scholar]
  23. Vardy, M.E., G.Sauvin, R.T.Klinkvort, M.Vanneste, and N.Dyer. 2023b. How many CPTs does it take to make a synthetic CPT? In: 9th International SUT Offshore Site Investigation Geotechnics Conference Proceedings – Innovative Geotechnologies for Energy Transition. 385–392., https://doi.org/10.3723/WASQ1942.
    [Google Scholar]
/content/papers/10.3997/2214-4609.202521144
Loading
/content/papers/10.3997/2214-4609.202521144
Loading

Data & Media loading...

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error