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

Gohar Shoukat, Guillaume Michel, Mark Coughlan, Abdollah Malekjafarian, Indrasenan Thusyanthan, Cian Desmond and Vikram Pakrashi ()
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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
Guillaume Michel: Gavin & Doherty Geosolutions, D14 X627 Dublin, Ireland
Mark Coughlan: Gavin & Doherty Geosolutions, D14 X627 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

Energies, 2023, vol. 16, issue 9, 1-23

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.

Keywords: renewable energy; geotechnics; CPT; machine learning; ANNs (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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