An analytical method to predict the pullout response of geotextiles
Bibliographic record
Abstract
ABSTRACT: Understanding the pullout mechanism of geotextiles is a key consideration in analyzing the stability of geotextile/reinforced-soil structures. The analytical models developed based on linear behavioral assumptions for the geotextile material stress–strain response have significant limitations in capturing the highly nonlinear pullout response observed in relatively extensible geotextiles. A new analytical model was developed combining the nonlinear responses of the geotextile and soil–geotextile interface characteristics. The interface behavior was modeled considering the changes in normal stress on the planar geotextile due to constrained dilation of soil and the subsequent frictional degradation behaviors at the interface. The analytical solution is useful in predicting the pullout resistances, strain, and mobilized frictional length along the geotextile for a given magnitude of displacement. The suitability of the analytical formulation was verified by predicting the experimentally observed performance of several geotextile pullout tests conducted with varying geotextile material and burial conditions. A simple chart and equation capable of predicting strain and mobilized frictional length, respectively, for a given magnitude of relative geotextile displacement are proposed, based on the research findings.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".