Alignment planning and network optimization of auxiliary roads for overhead power transmission line facility construction
Bibliographic record
Abstract
To deploy overhead power transmission lines in a mountainous region, an auxiliary road (AR) network must be built to interconnect the site for pylons and the nearby existing roads, which poses challenges in safety, cost, environment, and efficiency. This research proposed a two-phase methodology: (1) auxiliary road alignment optimization (ARAO); (2) road network layout optimization (RNLO). ARAO devised the improved Dijkstra algorithm (IDA) to plan, under geometric design constraints, the AR alignments with minimal construction costs. RNLO utilized the genetic algorithm (GA) to screen out the AR network layout with minimal gross cost. The case studies substantiated the methodological superiority: compared with the human-planned design, the IDA-generated design curtailed excavation volume and total road length substantially. IDA planned the alignments with uplifted efficiency and shortened project duration. The IDA-plus-GA can optimize the network layout with minimal gross cost and robust adaptiveness to environmental constraints (e.g., forestry, waterbody, etc.).
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".