Influence of geosynthetic stiffness on analytical solutions for reinforced fill over void
Bibliographic record
Abstract
Analytical solutions for geosynthetic reinforced fills over a void have appeared in the literature starting in the 1980s. Current solutions pay little or no attention to the influence of the creep-reduced stiffness of the geosynthetic reinforcement under tensile loading. This paper addresses this gap by introducing a reinforcement stiffness limit state in the design of these systems. The choice of reinforcement stiffness is based on a simple two-parameter hyperbolic isochronous load–strain model developed by the authors and applied to a large database of uniaxial and biaxial geogrids and woven geotextiles. The paper provides a design chart procedure that can be used with four well-known analytical solutions to compute the maximum reinforcement load. In addition to the stiffness limit state, the design chart approach includes vertical deformation and reinforcement strain serviceability limit states, and a tensile strength limit state. A novel feature of the design charts is a quantitative link to the ultimate strength of the reinforcement to estimate the isochronous stiffness of the reinforcement for different elapsed loading times and strains. There are many instances in the literature where the reinforcement stiffness was taken from a constant rate-of-strain tensile test. The paper shows that this is non-conservative for design.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".