Investigating the impact of correlation on system multimode reliability-based analysis of highway geometric design
Bibliographic record
Abstract
Reliability analysis has been used to account for uncertainties and evaluate the risk of highway geometric-designs. Despite the existence of correlations between the input design-variables, the majority of the studies applying reliability-analysis have ignored their correlations. The objective of this paper is to quantify the influence of input design-variable correlations on reliability-based highway geometric-design. Three modes of failure are considered: insufficient-sight-distance, vehicle-skidding, and vehicle-rollover, for passenger cars and heavy trucks. A series-system reliability problem of the failure modes is used to account for the joint occurrence of the failure mechanisms. Results show that ignoring the correlations between input-variables can lead to inaccurate estimation of the noncompliance probability for both the individual modes and the series-system reliability. The effect is more pronounced for the vehicle-skidding failure mode than the other modes. The input-variables' correlation significantly changes the multivariate distributions of the performance functions, leading to more extreme events in the failure domain.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".