Investigating the impact of correlation on system multimode reliability-based analysis of highway geometric design
Why this work is in the frame
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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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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 | 0.007 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.070 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 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 it