Accuracy assessment of default and modified Federal Highway Administration (FHWA) simplified models for estimation of facing tensile forces of soil nail walls
Bibliographic record
Abstract
The accuracy of the default Federal Highway Administration (FHWA) simplified model for estimation of facing tensile forces for soil nail walls under in-service conditions was evaluated using a large quantity of measured long-term and short-term facing tensile force data collected from the literature. The estimation accuracy was quantified by the mean and coefficient of variation (COV) of the bias where bias is defined as the ratio of measured to calculated facing tensile force. Based on the available data, the default FHWA simplified model equation was found to overestimate long- and short-term facing tensile forces by about 15% and 23% on average, respectively. The corresponding spreads in estimation accuracy expressed as the bias COV were about 43% and 67%. Undesirable correlations between bias values and calculated facing tensile forces using the default FHWA simplified model equation were detected. A modified FHWA simplified model was proposed to improve the on average accuracy, reduce the spread in estimation accuracy, and remove the hidden correlations noted above. In addition, the modified equation has fewer empirical coefficients than the current formulation (i.e., four versus five). The facing tensile force equations developed in this study are a contribution to the design of facing of soil nail walls within the current FHWA soil nail wall design framework.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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 | 0.002 | 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".