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Record W4366492381 · doi:10.11159/icgre23.144

Numerical Modelling and Intervention Measures for Snow Avalanche Protection of the Blattbach Railway Tunnel

2023· article· en· W4366492381 on OpenAlexvenueno aff
Mirko Fasolino, Matteo Giani, Christian Ambrosi, Alessandro De Pedrini, Manuel Lüscher, Gianluca Di Bella

Bibliographic record

VenueProceedings of the World Congress on Civil, Structural, and Environmental Engineering · 2023
Typearticle
Languageen
FieldMedicine
TopicWinter Sports Injuries and Performance
Canadian institutionsnot available
FundersStrong
KeywordsSnowIntervention (counseling)EngineeringForensic engineeringComputer scienceEnvironmental scienceMeteorologyPsychologyGeography

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.347
Threshold uncertainty score0.317

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.012
GPT teacher head0.213
Teacher spread0.201 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the World Congress on Civil, Structural, and Environmental EngineeringSame topicWinter Sports Injuries and PerformanceFrench-language works237,207