Numerical and experimental study on influence of installation effects on behaviour of helical anchors in very dense sand
Bibliographic record
Abstract
The use of helical anchors has been increasing in recent years; however, the current understanding of its behaviour does not allow for reliable estimates of uplift capacity and load–displacement response. The need for improvement of the design methods has motivated the present study, which investigates the effect of installation on the helical anchor response in very dense sand. This work is divided into three parts. The first part describes the results of centrifuge model tests and microtomographic analysis performed to examine the uplift response of a single-helix anchor and the soil disturbance caused by the installation process. The aim of these tests was to define appropriate hypotheses for the numerical prediction of helical anchor performance. In the second part, the load–displacement curve of a tested helical anchor was numerically simulated with the finite difference software FLAC3D, considering the installation effect observed experimentally. In the third part of the paper, the adjusted numerical model was used in a parametric analysis to provide some guidelines for the design of helical anchors in dense sand. Additionally, from the results of the numerical investigation and experimental observations, two different approaches are presented for improving the accuracy of analytical predictions of helical anchor capacity. Both suggested methods showed good agreement with centrifuge and field testing data.
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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 | 0.001 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".