Incorporating design consistency into risk-based geometric design of horizontal curves: a reliability-based optimization framework
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Reliability theory has recently been utilized to consider the uncertainty in highway geometric design and optimize highway cross-section design safety by reducing crash risk. However, despite the importance of design consistency among successive highway segments and its impact on safety, most previous studies optimized road segments individually. This approach could limit the applicability and transferability of the optimization frameworks in practice. Thus, a system reliability-based optimization framework is proposed in this study to design successive highway cross-section elements while achieving overall design consistency and safety. A sequential search procedure with the basin-hopping stochastic algorithm is adopted to optimize the successive horizontal curve segments. A case study of a 12 km segment with 94 horizontal curves in the Sea-to-Sky Highway, Canada, is considered. The results show that the proposed optimization framework provided higher consistency successive cross-section elements design while minimizing the number of expected crashes and providing consistent crash risk levels.
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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 | 0.025 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.070 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.000 |
| 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 it