System reliability as a surrogate measure of safety for horizontal curves: methodology and case studies
Bibliographic record
Abstract
Reliability analysis has been advocated to account for the uncertainty in geometric design and to evaluate the risk associated with various design options. Most of the previous studies using reliability-analysis in highway design evaluated only one-mode of noncompliance. This study assesses the performance of horizontal curves using a system of multi-modal noncompliance (insufficient sight distance, vehicle skidding, and vehicle rollover). Five case studies of a highway in British Columbia are considered. Two approaches were used: (1) second-order reliability-bounds with FORM analysis (First-Order Reliability Method), (2) Monte-Carlo Simulation (MCS). A calibrated design chart that accommodates heavy-trucks on horizontal-curves with sharp-radii is provided. The results show that the differences in the system probability of noncompliance between one-mode and system of multi-modes of noncompliance are more pronounced for heavy-trucks. Results also show that the probability of noncompliance associated with vehicle rollover is significantly affected by the stability-ratio compared to height-ratio and roll-rate.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".