Evaluation of tensile load model accuracy for PET strap MSE walls
Bibliographic record
Abstract
There are conflicting opinions regarding whether or not mechanically stabilized earth (MSE) walls constructed with polyester (PET) straps should be classified as relatively inextensible type, and thus fall into the same category as steel reinforced soil walls, or whether they should be viewed as relatively extensible and grouped with MSE walls that are designed with geosynthetic sheet reinforcement such as geogrids and geotextiles. The differences in predicted loads using models associated with each category can be significant. The writers collected 71 measurements from eight different instrumented PET strap wall projects. The data showed that these PET strap walls behaved more like extensible geosynthetic walls constructed with geogrids and geotextiles (or at least at the stiffer end of these walls) rather than MSE walls with relatively inextensible (steel) reinforcement. The accuracy of the Coherent Gravity Method, the Simplified Method and the Simplified Stiffness Method was assessed statistically using analysis of bias values, where bias is the ratio of measured to predicted load. The Coherent Gravity Method and the Simplified Method resulted in conservative (safe) estimates of reinforcement loads under operational conditions on average. However, the Simplified Stiffness Method was the most accurate and did not result in an excessive level of conservativeness.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| 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".