Holding capacity of dynamically installed anchors in normally consolidated clay under inclined loading
Bibliographic record
Abstract
This paper describes a design framework for inclined tensile loading capacity (holding capacity) of dynamically installed anchors in soft clay. Centrifuge model test and numerical results indicate that, while ultimate inclined holding capacity increases with a loading angle smaller than 45° (to the vertical at anchor padeye), anchor failure is controlled predominantly by the ultimate vertical holding capacity, which can be predicted using the American Petroleum Institute method put forth in 2007 with interface friction ratio of 0.25–0.34 and 0.60–0.82 for short- and long-term conditions, respectively. For a loading angle larger than about 65°, anchor failure is controlled largely by the ultimate horizontal holding capacity. It is proposed herein that the ultimate lateral holding capacity can be estimated by generalizing Broms’ method put forth in 1964 for lateral anchor capacity to variable cross section; thereby allowing anchor geometry to be considered in a rational manner. Good agreement can be obtained between the estimated ultimate horizontal holding capacity and that computed using the finite element method by using a lateral resistance of 10.5s u , where s u is the undrained shear strength. For a loading angle between 45° and 65°, there is significant interaction between vertical and horizontal failure modes. This allows a normalized horizontal–vertical interaction curve to be defined by fitting data from this and previous studies.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".