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Record W2335153319 · doi:10.1061/40621(254)34

Deck Wearing Surfaces for the Yukon River Bridge

2002· article· en· W2335153319 on OpenAlexaboutno aff
J. Leroy Hulsey, Lutfi Raad, Billy Connor

Bibliographic record

VenueCold Regions Engineering · 2002
Typearticle
Languageen
FieldEngineering
TopicConcrete Corrosion and Durability
Canadian institutionsnot available
Fundersnot available
KeywordsDeckTruckSnowAbrasion (mechanical)Bridge (graph theory)Pipeline transportEnvironmental scienceTrailerStructural engineeringEngineeringGeologyMechanical engineering

Abstract

fetched live from OpenAlex

The Yukon River Bridge in interior Alaska is recognized as an outstanding civil engineering design. The structure is a six-span 884.4-m steel bridge on a 6 percent grade. Two 155.9-cm wide by 414.0-cm deep steel box girders support an orthotropic steel deck. The structure carries the oil pipeline, vehicles, tourism busses and heavy trucks. In addition to highway traffic and the oil pipeline, there is potential for the structure to carry a future gas line. Weather can vary from harsh winter temperatures combined with snow and ice to mild summer temperatures. This structure was designed in the early 70's with a temporary 127-mm two-layer timber deck-wearing surface. Since then, timber running planks have been replaced three times with similar timbers (1981, 1992, and 1999). In 1992, in addition to running planks, timber deck planks were replaced and the steel deck was cleaned and coated against corrosion. A successful wearing surface for this bridge must be cost effective, lightweight, abrasion resistant and provide traction; especially during winter months. A wearing surface selection criterion is discussed and wearing surfaces on similar bridges are examined. Two examples illustrate a design procedure for selecting a wearing surface on this structure; design charts are used. The design charts account for truckloads and temperature change. No charts are available for traction or abrasion.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.308
Threshold uncertainty score0.569

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 head0.033
GPT teacher head0.204
Teacher spread0.171 · 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

Citations2
Published2002
Admission routes1
Has abstractyes

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