Guidelines for offshore in situ testing and interpretation in deepwater soft clays
Bibliographic record
Abstract
Offshore developments for hydrocarbon resources have now progressed to water depths approaching 3000 m, with geotechnical design increasingly focused on soft sediments in the upper 30 m or so of the seabed. Due to the difficulties and high cost in recovering high-quality samples from deepwater sites, there is increasing dependence on in situ testing techniques for determining the geotechnical design parameters. This paper summarizes the findings from a joint industry project, undertaken by the Norwegian Geotechnical Institute and the Centre for Offshore Foundation Systems at The University of Western Australia, on the use of in situ testing for the characterization of deepwater soft soils. The project focused on theoretical and empirical studies for the interpretation of piezocone, T-bar, and ball penetration test data, linking the penetration resistance to shear strengths determined from in situ vane tests and laboratory tests on high-quality samples. Guidelines are summarized for interpreting in situ test data, particularly for estimating intact and remoulded undrained shear strengths from the penetration resistance measured by each type of penetrometer. Suggestions are also given for future development of in situ testing techniques to maximize the potential of in situ testing in characterization of deepwater soft soils.
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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 | 0.020 | 0.046 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.008 | 0.003 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.014 | 0.021 |
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".