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; Vikram Pakrashi

LAPSE

Living Archive for Process Systems Engineering

LAPSE:2023.35755

Published Article

LAPSE:2023.35755

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, Vikram Pakrashi

May 23, 2023

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.

Record ID

LAPSE:2023.35755

Keywords

ANNs, CPT, geotechnics, Machine Learning, Renewable and Sustainable Energy

Subject

Energy Systems

Suggested Citation

Shoukat G, Michel G, Coughlan M, Malekjafarian A, Thusyanthan I, Desmond C, Pakrashi V. Generation of Synthetic CPTs with Access to Limited Geotechnical Data for Offshore Sites. (2023). LAPSE:2023.35755

Author Affiliations

Shoukat G: 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 [ORCID]
Michel G: Gavin & Doherty Geosolutions, D14 X627 Dublin, Ireland
Coughlan M: 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
Malekjafarian A: Structural Dynamics and Assessment Laboratory, School of Civil Engineering, University College Dublin, D04 V1W8 Dublin, Ireland [ORCID]
Thusyanthan I: Gavin & Doherty Geosolutions, D14 X627 Dublin, Ireland
Desmond C: Gavin & Doherty Geosolutions, D14 X627 Dublin, Ireland [ORCID]
Pakrashi V: UCD Centre for Mechanics, Dynamical Systems and Risk Laboratory, School of Mechanical and Materials Engineering, University College Dublin, D04 V1W8 Dublin, Ireland [ORCID]

Journal Name

Energies

Volume

16

Issue

9

First Page

3817

Year

2023

Publication Date

2023-04-28