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Generation of Synthetic CPTs with Access to Limited Geotechnical Data for Offshore Sites

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  • Gohar Shoukat

    (UCD Centre for Mechanics, Dynamical Systems and Risk Laboratory, School of Mechanical and Materials Engineering, University College Dublin, D04 V1W8 Dublin, Ireland
    Gavin & Doherty Geosolutions, D14 X627 Dublin, Ireland)

  • Guillaume Michel

    (Gavin & Doherty Geosolutions, D14 X627 Dublin, Ireland)

  • Mark Coughlan

    (Gavin & Doherty Geosolutions, D14 X627 Dublin, Ireland
    School of Earth Sciences, Science Centre West, University College Dublin, D04 V1W8 Dublin, Ireland
    SFI Research Centre in Applied Geosciences (iCRAG), O’Brien Centre for Science (East), University College Dublin, Belfield, D04 V1W8 Dublin, Ireland)

  • Abdollah Malekjafarian

    (Structural Dynamics and Assessment Laboratory, School of Civil Engineering, University College Dublin, D04 V1W8 Dublin, Ireland)

  • Indrasenan Thusyanthan

    (Gavin & Doherty Geosolutions, D14 X627 Dublin, Ireland)

  • Cian Desmond

    (Gavin & Doherty Geosolutions, D14 X627 Dublin, Ireland)

  • Vikram Pakrashi

    (UCD Centre for Mechanics, Dynamical Systems and Risk Laboratory, School of Mechanical and Materials Engineering, University College Dublin, D04 V1W8 Dublin, Ireland)

Abstract

The initial design phase for offshore wind farms does not require complete geotechnical mapping and individual cone penetration testing (CPT) for each expected turbine location. Instead, background information from open source studies and previous historic records for geology and seismic data are typically used at this early stage to develop a preliminary ground model. This study focuses specifically on the interpolation and extrapolation of cone penetration test (CPT) data. A detailed methodology is presented for the process of using a limited number of CPTs to characterise the geotechnical behavior of an offshore site using artificial neural networks. In the presented study, the optimised neural network achieved a predictive error of 0.067 . Accuracy is greatest at depths of less than 10 m . The pitfalls of using machine learning for geospatial interpolation are explained and discussed.

Suggested Citation

  • Gohar Shoukat & Guillaume Michel & Mark Coughlan & Abdollah Malekjafarian & Indrasenan Thusyanthan & Cian Desmond & Vikram Pakrashi, 2023. "Generation of Synthetic CPTs with Access to Limited Geotechnical Data for Offshore Sites," Energies, MDPI, vol. 16(9), pages 1-23, April.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:9:p:3817-:d:1136221
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    References listed on IDEAS

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    1. Barrett, Michelle & Farrell, Niall & Roantree, Barra, 2022. "Energy poverty and deprivation in Ireland," Research Series, Economic and Social Research Institute (ESRI), number RS144, June.
    2. Mark Coughlan & Mike Long & Paul Doherty, 2020. "Geological and geotechnical constraints in the Irish Sea for offshore renewable energy," Journal of Maps, Taylor & Francis Journals, vol. 16(2), pages 420-431, December.
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