Bibliographic record
Abstract
The potential usefulness of reliability analysis has recently been stressed in many engineering applications. Given the variability in the design parameters, a reliability-based probabilistic approach is well suited to replace the current deterministic highway design practice. However, progress in this regard is generally slow. In this study, the reliability analysis was used to estimate the probability of hazard (POH) that might result from insufficiency of sight distances. As an application, the available sight distance was checked against required stopping sight distance on an assumed road segment. Variation of the design parameters was addressed with Monte Carlo simulation using 100,000 sets of design parameters based on distributions available in the literature. A computer program was developed to use these sets of design parameters to calculate the profiles of available and required stopping sight distances in two- and three-dimensional projections as well as the profile of POH. The approach was applied to a horizontal curve overlapping with flat grade, crest curves, and sag curves in a cut section where the side slope would restrict the sightline. The analysis showed that the current deterministic approach yields very conservative estimates of available and required stopping sight distance, resulting in very low POH. The application example also showed the change of POH with the change of vertical alignment parameters.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".