Data‐driven distributionally robust optimization of railway alignments in earthquake‐prone regions considering active fault zone risks
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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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".