Optimizing mountain railway alignments with a potential field guided 3D-RRT-star algorithm
Bibliographic record
Abstract
Developing railway alignments becomes increasingly challenging in undulating terrain with densely-distributed obstacles. Traditional alignment optimization methods, which employ a design process characterized as “search for alignment-favorable environments based on trial alignments”, struggle to efficiently generate optimized alignments under these conditions. To address this problem, an environmental suitability analysis is first implemented by abstracting the study area as a set of voxels within various structural layers. The Environmental Suitability (ES) for each voxel is then formulated based on its location in different structural layers. Then, by considering the spatial distribution and the ES values, alignment-favorable regions and alignment-unfavorable regions are identified through a kernel density analysis and k-mean clustering. These regions are further represented by Potential Fields (PFields), which are integrated with a 3D Rapidly-exploring Random Tree-star (3D-RRT-star) to create a PField-RRT-star search method. Through application to a real-world mountain railway case, the PField-RRT-star method demonstrates improved search efficiency and solution quality.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".