Numerical Modelling and Intervention Measures for Snow Avalanche Protection of the Blattbach Railway Tunnel
Bibliographic record
Abstract
Snow avalanches represent an undeniable reality in the Swiss Alps both as a geomorphic process and as a type of hazard, causing fatalities, damage to structures and infrastructures.The potential damage of snow avalanches must be seriously taken into consideration when new infrastructure is planned in snow-avalanche prone areas, or to protect existing ones.The Matterhorn Gotthard Bahn railway line is a Swiss infrastructure periodically subjected to snow avalanche hazard in some critical area.This research, in the frame of the practical applications that SUPSI (University of applied sciences of southern Switzerland) promotes within the civil engineering bachelor's degree program, provides a preliminary study aimed at investigating the best protection measure for avalanche risk mitigation of the infrastructure.Snow avalanche simulation are performed and calibrated based on available data obtained from historical events, allowing to estimate the avalanche debris height, length, and speed.On these basis, three solutions are proposed to handle avalanche situations, respectively an increase of the existing tunnel length before and after the critical area, some deviation earth-compacted embankments, or avalanche protection steel barriers.Different solutions are analysed and compared in terms of efficiency and costs.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".