Evaluation of Soil-Geogrid Pullout Models Using a Statistical Approach
Bibliographic record
Abstract
Abstract The accuracy of the current Federal Highway Administration (FHWA) in-soil geogrid pullout model was examined using a statistical approach applied to a large database of pullout test results. The accuracy of data interpretation and model type was quantified using the mean and coefficient of variation (COV) of model bias values and possible hidden dependencies identified using the Spearman rank correlation coefficient. Model bias values were computed as the ratio of measured to predicted pullout capacity. When project-specific pullout test data were used to fit a linear approximation to dimensionless interaction coefficients, the result was judged to be an acceptably accurate model (mean bias value of one and a small spread in bias values, i.e., COV=0.13). However, in many cases project-specific pullout data are not available. If the current FHWA model with default values is used, the prediction accuracy is very poor based on the same quantitative measures (mean of bias=2.23 and COV=0.55). Two new models were examined to overcome this deficiency. One model is bi-linear and the other is non-linear. The non-linear model was shown to be more accurate with a mean bias value close to one and COV=0.36. The non-linear model also has the advantage of being smoothly continuous with practically no detectable hidden dependencies. Finally, the large number of test results in the database allows recommendations to be made on how to select reinforcement lengths during the experimental design to increase the likelihood of a pullout mode of failure in the laboratory.
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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 | 0.013 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".