Insight into centrifuge modelling errors in predicting embedment depths of dynamically installed anchors
Bibliographic record
Abstract
In centrifuge modelling of dynamic events, a common practice is to preserve the inertial effect. This will lead to a loss of similitude in the strain rate between model and prototype. For centrifuge tests of dynamic installation of anchors, such strain rate dissimilarity may give rise to significant discrepancy in the anchor embedment depth between the model and the prototype, i.e., modelling error. Large-deformation finite element analyses that simulate both the model and the prototype were undertaken in this study to provide insight into this phenomenon. The calculation results revealed that the centrifuge model tends to underpredict the anchor embedment depth of the prototype. A parametric study of the influence of anchor geometry and weight, soil strength and stiffness, strain softening and rate parameters, and centrifuge acceleration level was subsequently conducted. It was found that such underprediction depends heavily upon the strain rate parameter. Based on analyses results, practical advices were given for centrifuge modellers to minimize the modelling error. The results presented and advices given in this study may facilitate the design of centrifuge model tests especially when the anchor embedment depth is an essential detail to be replicated in these tests.
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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 | 0.002 | 0.010 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".