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Record W2508294860 · doi:10.1139/cjce-2015-0377

Assessment of low temperature exposure for design and evaluation of elastomeric bridge bearings and seismic isolators in Canada

2016· article· en· W2508294860 on OpenAlexafffundvenueabout
Louis-Piérick Guay, Najib Bouaanani

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2016
Typearticle
Languageen
FieldEngineering
TopicStructural Health Monitoring Techniques
Canadian institutionsPolytechnique Montréal
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsStiffeningStructural engineeringBridge (graph theory)Environmental scienceThermalElastomerComputer scienceEngineeringMaterials scienceComposite materialMeteorologyGeography

Abstract

fetched live from OpenAlex

This paper presents key data and relevant analyses assessing low temperature exposure for design and evaluation of elastomeric bridge bearings and isolators in Canada. A large database of temperature records is processed to investigate the potential for instantaneous thermal stiffening and crystallization. The results mainly show that: (i) temperature conditions at some locations lead to a significant potential for instantaneous stiffening in terms of intensity and frequency; (ii) in warmer locations, thermal stiffening is highly improbable; (iii) the number of consecutive days below a given low temperature is very variable geographically; and (iv) the crystallization testing criteria prescribed in CSA S6-14 can be too conservative. The results are also illustrated using contour maps. Important differences in temperature variation trends between eastern and western Canada are highlighted. The proposed methodology and obtained results constitute efficient tools to determine site-specific temperature conditions for enhanced performance-based design and evaluation of bridges in Canada.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.772
Threshold uncertainty score0.949

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 head0.238
Teacher spread0.226 · 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 designObservational
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

Citations12
Published2016
Admission routes4
Has abstractyes

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