A cone penetration test (CPT) approach to cable plough performance prediction based upon centrifuge model testing
Bibliographic record
Abstract
Cable ploughing is an important technique for burying and protecting offshore cables. The ability to predict the required tow force and plough performance is essential to allow vessel selection and project programming. Existing tow force models require calibration against full-scale field testing to determine empirical parameters, a requirement that may hinder their use. In this study the factors controlling the plough resistance were investigated using a series of dry and saturated 1/50th scale model cable plough tests in sand in a geotechnical centrifuge at 50g at a range of target trench depths, sand relative densities, and plough velocities. An improved model for predicting cable plough tow force that separates out the key components of resistance and allows tow force prediction without the use of field-derived empirical coefficients is presented. It is demonstrated that the key parameters in this model can be easily determined from in situ cone penetration tests (CPTs), allowing it to be used offshore where site investigation techniques may be more limited compared to onshore locations. The model is validated against the centrifuge cable plough tests, demonstrating it can correctly predict both the static (dry) and rate effect (saturated) tow forces.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".