Probabilistic analysis for the reinforced fill over void problem
Bibliographic record
Abstract
Analytical and numerical solutions for the problem of geosynthetic-reinforced fills over a void have been the subject of investigation for the last four decades. A common feature of this prior work is that all methods have treated the analytical solutions as deterministic. While the treatment of some input parameters must be taken as deterministic, there are other parameters that have uncertainty. Furthermore, the underlying mechanistic models for load and resistance terms in the limit state equations for the reinforced fill over a void problem can be expected to have different accuracy. This paper revisits the problem of geosynthetic-reinforced fills over voids from a probabilistic point of view for reinforcement tensile strain, tensile strength, and geosynthetic stiffness limit states. Particular attention is paid to the method used to select the isochronous stiffness of the reinforcement and the associated uncertainty in the magnitude of that value. The paper demonstrates how the factor of safety from deterministic past practice can be linked quantitatively to the reliability index used in contemporary probabilistic design. Finally, the paper demonstrates the advantage of using product-specific constant-load creep test results to maximise margins of safety for strength and stiffness limit states in both deterministic and probabilistic frameworks.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".