Assessment of horizontal curves of an existing road using reliability concepts
Bibliographic record
Abstract
Horizontal curves on road are commonly analyzed under design speed point of view, where it is assumed that the maximum speed of a vehicle in a curve is the design speed. The empirical evidence has demonstrated that when the design speed is low, the operating speed tends to be higher. This happens because of an available remaining lateral (or transverse) friction for speeds over design speed. This condition is determined by a speed limit, obtained from the demand and supply equilibrium of friction of a pavement. The difference between operating and design speeds is usually considered as the margin of safety of a horizontal curve on a road. In this study, a methodology to determine the margin of safety of an existing curve is proposed. The methodology is based on the reliability theory by which reliability of operational conditions can be analyzed by using a reliability index as a margin of safety. A case study for light vehicles is evaluated to determine high impact variables over reliability, such as, macrotexture, skid resistance, curve radius, and superelevation. The results obtained in this study demonstrated that curve radius, skid resistance, and macrotexture are variables with high impact over failure probability. In constrast, superelevation has little effect on the failure probability.Key words: reliability, horizontal curves, operating speed, skid resistance, pavement texture.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".