A reliability-based assessment framework for drag anchors
Bibliographic record
Abstract
Drag anchors are often employed in offshore floating facility moorings. The standard drag anchor design approach is based on a deterministic load and resistance factor design (LRFD) framework that considers characteristic design “low” and “high” estimates of soil strength and other geotechnical parameters, combined with code-specified partial factors. A disadvantage of this approach is that the resulting anchor designs may not achieve a consistent level of reliability. This paper describes a study that addresses this limitation by developing and demonstrating a generalised drag anchor probability of failure analysis framework for inclusion in a reliability-based assessment (RBA) of a mooring. A feature of the study is the inclusion of consolidation and cyclic loading effects in the anchor analysis. The study highlights the benefits an RBA approach can offer to the drag anchor design process, including reduced anchor size and preloading requirements, and increased confidence in the anchor design and estimate of anchor performance. For temporarily moored facilities, this approach offers the potential to exploit expanded weather windows for operations. For permanently moored floating offshore wind developments, this approach may allow adoption of reduced levels of target reliability, thereby reducing costs for systems with a large number of anchors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".