Reliability-based analysis of highway geometric Elements: A systematic review
Bibliographic record
Abstract
Conventional highway design approaches have primarily focused on the use of guidelines in the design of highways. These design guidelines provide nominal safety where conservative percentile values of the design inputs are used to account for the uncertainty associated with the inputs. Reliability-based analysis (RBA) been one of the elements of reliability, availability, maintainability, and safety (RAMS) has been identified as an effective method to account for the uncertainty in the design input and to assess the risk related to a particular design. RBA approaches have effectively been used for certain purposes in other disciplines. In highway geometric design literature, these methods were also investigated and showed promise. Given the compelling importance of RBA in highway design, this paper provides a systematic analysis and evaluation of RBA applications for ten highway geometric elements: stopping sight distance, passing sight distance, intersection sight distance, horizontal curve design, vertical curve design, number of freeway lanes, highway grade length, truck escape ramp, and design guide calibration. The review consists of four parts: the concept of RAMS, background on reliability theories, applications in highway geometric design, and guidelines for the use of reliability analysis. The literature review revealed that the application of reliability-based analysis in highway geometric design leads to significant improvements in traffic safety. It is our hope that this paper will serve as a source of information on RBA for highway designers and practitioners, promoting its development and application in highway geometric design.
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".