A Deep Learning Model to Predict the Lateral Capacity of Monopiles
Bibliographic record
Abstract
The load response of large-diameter monopiles is highly nonlinear and site-dependent. The design of laterally loaded piles has traditionally relied on the p-y method, which was originally developed for long, slender piles. Direct use of the p-y method for large-diameter monopiles (with small slenderness ratios) may lead to large errors in the estimated lateral pile capacity. Machine learning (ML) methods are superior in solving highly nonlinear problems. In this paper, we developed a hybrid neural network model that predicts the lateral capacity of large-diameter monopiles based on the cone penetration test (CPT) data, pile geometries, and loading conditions. We constructed a hybrid neural network model by combining the convolutional neural network (CNN) and fully connected (FC) deep learning neural network. The model is efficient in training and can generate high-accuracy predictions. A large number of synthesized data obtained based on rigorous finite element modeling were used to train the constructed hybrid neural network model. The developed DL model is able to predict the load-rotation response of monopiles in multi-layered sandy soil. The average relative error in the predicted lateral capacity is 3.1%. A design example is given to demonstrate the performance of the proposed method.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".