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Related Experiment Video

Updated: Jun 21, 2026

Measuring and Mapping Patterns of Soil Erosion and Deposition Related to Soil Carbonate Concentrations Under Agricultural Management
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AI-enhanced soil classification with incomplete CPT data for offshore wind farm.

Cheng-Yu Ku1, Ting-Yuan Wu1, Chih-Yu Liu2,3

  • 1Department of Harbor and River Engineering, National Taiwan Ocean University, Keelung, 202301, Taiwan.

Scientific Reports
|March 30, 2026
PubMed
Summary

This study introduces an AI framework for robust soil classification using incomplete cone penetration test (CPT) data, crucial for offshore wind farm foundation design. The random forest model demonstrated high accuracy, even with missing CPT parameters.

Keywords:
Artificial intelligenceCone penetration testMachine learningOffshore wind farmSoil classification

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Area of Science:

  • Geotechnical Engineering
  • Artificial Intelligence in Geosciences
  • Offshore Renewable Energy

Background:

  • Accurate soil classification is critical for offshore wind farm foundation design.
  • Conventional cone penetration test (CPT) methods struggle with incomplete datasets in offshore environments.
  • Developing robust AI solutions for geotechnical data analysis is essential.

Purpose of the Study:

  • To present an artificial intelligence (AI) enhanced framework for soil classification using the Robertson Classification.
  • To emphasize the framework's robustness with incomplete cone penetration test (CPT) data.
  • To provide a practical solution for offshore wind farm geotechnical design.

Main Methods:

  • Generated a synthetic CPT database of 229,808 samples using diverse sampling strategies.
  • Evaluated four machine learning models, including random forest.
  • Simulated missing CPT input parameters and employed Monte Carlo simulations for uncertainty analysis.

Main Results:

  • The random forest model achieved the highest performance with R² of 0.99 and 92.53% classification accuracy.
  • Reliable soil classification predictions were maintained despite incomplete CPT data.
  • Cone tip resistance (qc), sleeve friction (fs), and effective stress (σ'v) were identified as key features.

Conclusions:

  • The AI-enhanced framework offers a robust solution for CPT-based soil classification with incomplete datasets.
  • This approach is practical for offshore wind farm geotechnical design.
  • The study highlights the potential of AI in overcoming data limitations in geotechnical investigations.