Reliability Analysis of Horizontal Curves Using Geometric Design Consistency Assessment Criterion
Bibliographic record
Abstract
Road accidents have always been one of the important reasons for fatalities and financial losses. Since road accidents on rural highways cause more serious injuries than those on urban highways, providing a suitable method to increase safety in the curves can be a significant contributor to preventing these damages. Although speed is one of the most important variables affecting highway safety, numerous studies have been performed on the reliability analysis of horizontal curves without taking the speed variable into account. The aim of this research is reliability (probability of noncompliance) assessment in the horizontal curve design using geometric design consistency criteria. The radius, superelevation, and operating speed of 19 horizontal curves were collected by field research on the Mashhad‐Torbat Heydarieh highway in Iran. Three different approaches were defined based on the geometric design consistency criterion of a single horizontal curve, and consecutively, the probability of noncompliance was calculated using these approaches. According to the obtained results, this study showed that radius enhancement increases the probability of noncompliance and the consistency level of the geometric design. Finally, the high values of the probability of noncompliance (failure) indicate that the geometric design guidelines need calibration in the design of horizontal curves, especially for higher radii.
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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 | 0.010 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".