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

• Main Authors: Coughlan, M. (Author), Desmond, C. (Author), Malekjafarian, A. (Author), Michel, G. (Author), Pakrashi, V. (Author), Shoukat, G. (Author), Thusyanthan, I. (Author)
• Format: Article
• Language: English
• Published: MDPI 2023
• Subjects: ANN; ANNs; Cone penetration testing; CPT; Design phase; Geotechnical data; Geotechnical mapping; geotechnics; Geotechnics; Initial design; Interpolation; machine learning; Machine learning; Machine-learning; Neural networks; Offshore oil well production; Offshore wind farms; Offshores; Renewable energies; renewable energy; Seismology
• Online Access: View Fulltext in Publisher
• ISBN: 19961073 (ISSN)
• ISSN: 19961073 (ISSN)
• DOI: 10.3390/en16093817

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 (Formula presented.). Accuracy is greatest at depths of less than 10 (Formula presented.). The pitfalls of using machine learning for geospatial interpolation are explained and discussed. © 2023 by the authors.