Prediction for Lateral Response of Monopiles: Deep Learning Model on Small Datasets Using Transfer Learning
Bibliographic record
Abstract
This paper presents a novel approach for predicting the lateral capacity of large-diameter monopiles in multi-layered soil using deep learning and transfer learning techniques. With the increasing interest in offshore wind energy, the cost-effective design of offshore wind turbine foundations is crucial. Traditional methods such as the p-y method have limitations in analyzing larger diameter monopiles. As an alternative to high-fidelity numerical models (e.g., finite element analysis and finite volume analysis), deep learning models have gained popularity for load response predictions and design purposes. Yet, one major challenge of deep learning models is that insufficient data can result in poor model performance. In this study, a deep learning model incorporating convolutional and fully connected layers was developed to capture the complex interactions between pile geometry parameters and soil conditions. To address the challenge of limited dataset size, transfer learning was employed, leveraging a pretrained model on a large dataset. The results demonstrate that the proposed model can provide accurate predictions, even with a small dataset. Transfer learning significantly reduces the required data size while preserving high prediction accuracy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".