Effects of cross-correlations between soil properties on pullout capacity of strip anchors
Bibliographic record
Abstract
Plate anchors are increasingly used for floating offshore structures due to their high capacity-to-weight ratio and cost-effectiveness, but their performance is significantly influenced by spatially variable soil properties. While previous studies have considered the effects of spatial variability, the impact of cross-correlations between input soil parameters remains largely unexplored. This study investigates how the cross-correlations between soil undrained shear strength and submerged unit weight for undrained soil conditions, and between soil friction angle and submerged unit weight for drained soil conditions, influence the mean and standard deviation of anchor pullout capacity factors. Using the random finite element method a range of cross-correlation coefficients from −1 to 1 was considered. The results show that the cross-correlations have a minimal effect on the mean pullout capacity factors. However, the standard deviations increase approximately proportionally with cross-correlations, implying the importance of accurately estimating these dependencies. Assuming independence between soil parameters may lead to unconservative failure probability estimates. These findings provide insights into the role of cross-correlations in the probabilistic analysis of offshore anchors.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".