Bibliographic record
Abstract
On sag vertical curves underpassing a structure, the fascia of the structure may cut the line of sight and limit the available sight distance. The current AASHTO model for symmetric sag curves with overpasses is intended for those that lie close to the point of vertical intersection on long sag curves. This paper presents an improved model that provides the exact minimum sight distance ( S m ) for any conditions including non-centered overpasses and short sag curves, which are typical in urban areas. The model explicitly includes as variables the overpass location, the overpass width, and the configuration of the first and second grades of the sag vertical curve. The model is formulated by using mathematical optimization that involves an objective function and constraints. The objective function minimizes the available sight distance subject to constraints involving the geometry of the curve and the overpass. The model is spreadsheet based and is solved with powerful optimization software. Application of the model is illustrated with some examples and the results show that the AASHTO model may underestimate S m by 10% to 30%. A closed-form model for S m on single-arc asymmetric sag curves is also presented for S m > L. The proposed model provides a friendly, flexible, and accurate tool for sight distance analysis and should be of interest to highway designers and practitioners.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".