Data‐driven distributionally robust optimization of railway alignments in earthquake‐prone regions considering active fault zone risks
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
Railway alignment design in earthquake-prone regions faces many challenges, among which an active fault zone threat is a dominant factor. However, slight attention has been devoted in this field to the complex fault zone risks affecting alignment optimization (AO). To this end, the first-known AO model that estimates active fault zone risks is proposed according to the distributionally robust optimization (DRO) theory. In this model, a data-driven minimax DRO function is formulated to compute the uncertain fault zone risks while optimizing railway alignments. In addition, a degree-of-regret (DoR) chance constraint is developed to trade off solution quality and search conservatism during optimization. To solve this DRO model, a particle swarm algorithm is improved in two ways. First, a Monte Carlo simulation is customized based on several alignment refinement analyses to assess possible railway losses due to uncertain fault zone damages. Afterward, a solution selection operator is devised to determine the best alignment alternatives while tackling the DoR constraint. Ultimately, the proposed DRO model and algorithm are applied to a real-world railway example. Their effectiveness is verified through two sensitivity analyses and by being compared with the best solution found by human designers.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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 it