Bayesian Optimization for CPT-Based Prediction of Impact Pile Drivability
Bibliographic record
Abstract
Pile drivability predictions require information on the pile geometry, impact hammer, and the soil resistance to driving (SRD). Current SRD prediction methods are based on databases of long slender piles from the oil and gas industry and new, robust, and adaptable methods are required to predict SRD for current offshore pile geometries. This paper describes an optimization framework to update uncertain model parameters in existing axial static design methods to calibrate SRD. The approach is demonstrated using a case study from a German offshore wind site. The optimization process is undertaken using a robust Bayesian approach to dynamically update uncertain variables during driving to improve simulations. The existing method is shown to perform well for piles with geometries that reflect the underlying database such that only minimal optimization is required. For larger diameter piles, relative to the prior best estimate, optimized results are shown to provide significant improvements in the mean calculations and associated variance of pile drivability as more data is acquired. The optimized parameters can be used to predict SRD for similar piles in analogous ground conditions. The demonstrated framework is adaptable and can be used to develop site-specific calibrations and advance new SRD methods where large pile driving data sets are available.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 | 0.000 |
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".