Accurate soil classification is fundamental to offshore wind farm foundation design, yet conventional
cone penetration test (CPT) based methods often require complete datasets that are costly and challenging to obtain in offshore environments. This study presents an artificial intelligence (AI) enhanced framework for soil classification based on the Robertson Classification, with a particular emphasis on robustness under incomplete CPT data. A comprehensive synthetic CPT database comprising 229,808 samples was generated using both uniform and statistically distributed sampling strategies to represent a wide range of realistic soil conditions. Among the four evaluated machine learning models, the random forest model achieved the best performance, with an R² of 0.99 and a classification accuracy of 92.53%. Simulations of missing CPT input parameters reveal that reliable predictions can be maintained even under incomplete data scenarios. Feature importance indicates that cone tip resistance (qc), sleeve friction (fs) and effective stress (σ’v), are the dominant factors governing soil classification. Prediction uncertainty using Monte Carlo simulations shows model performance within a 95% confidence interval. Overall, the proposed AI-enhanced framework provides a robust and practical solution for CPT-based soil classification using incomplete datasets for offshore wind farm geotechnical design.
Keywords Offshore wind farm, Soil classification, Cone penetration test, Artificial intelligence, Machine
learning
🔑 研究核心
- 提出一個 AI輔助的土壤分類方法，專門解決：
👉 CPT資料不完整時仍可準確分類 的問題
🤖 方法與架構
- 建立 大型合成資料庫（229,808筆）
- 採用 Robertson分類法 作為基礎
- 比較四種機器學習模型：
- 隨機森林（RF）
- 類神經網路（ANN）
- 支援向量回歸（SVR）
- 決策樹（DT）
🏆 主要成果
- 隨機森林（RF）表現最佳
- R² ≈ 0.99
- 分類準確率 ≈ 92.53%
- ANN次之，SVR與DT表現較弱
📊 關鍵影響因子
最重要的三個參數：
- 錐尖阻力（qc）
- 套管摩擦力（fs）
- 有效應力（σ′v）
🧪 不完整資料表現
- 即使資料缺失，模型仍具備良好穩定性
- 影響最大：
- 缺 qc 或 fs → 準確率大幅下降
- 影響較小：
- 缺孔隙水壓（u₂、u₀）
🌊 實務驗證
- 使用 台灣 + 荷蘭 offshore CPT資料（229,808筆）
- 準確率仍達 92.53%
👉 顯示方法具備實務可行性
📈 額外發現
- 資料量越大 → 模型越穩定
- Monte Carlo 模擬顯示：
- 預測結果落在 95%信賴區間內
- 模型定位：
👉 輔助工具（decision-support）
👉 非取代傳統方法