Improving Driveability Predictions for Offshore Piles Using Bayesian Optimisation
Bibliographic record
Abstract
Pile driveability predictions require information on the pile geometry, impact hammer and the soil resistance to driving (SRD). Current methods to predict SRD are based on databases of long slender piles and have been shown to provide poor predictions when applied to geometries outside of their original calibration spaces. New, robust and adaptable methods are required to predict SRD for current offshore pile geometries. An optimisation framework to update uncertain model parameters in existing axial static design methods to calibrate SRD is described. The optimisation is undertaken using a robust Bayesian approach to dynamically update uncertain variables during driving. The framework is demonstrated using a case study from a German offshore wind site. The static method is shown to perform well for piles with geometries that reflect the underlying database such that only minimal optimisation is required. For larger diameter piles, relative to the prior best estimate, optimised results are shown to provide significant improvements in the mean calculations, and associated variance, of pile driveability as more data is acquired. When compared with results determined using the conventional design approach used by industry, the optimised parameters provided notable improvements in the agreement between measured and calculated values. The optimised parameters can be used to predict SRD in similar profiles where large datasets are available, the demonstrated framework may be used to develop new SRD methods.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".