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Record W2611751134 · doi:10.1680/jbren.16.00022

Assessment of a bascule lift bridge using digital image correlation

2017· article· en· W2611751134 on OpenAlexafffundabout
Adam Hoag, Neil A. Hoult, W. Andy Take

Bibliographic record

VenueProceedings of the Institution of Civil Engineers - Bridge Engineering · 2017
Typearticle
Languageen
FieldEngineering
TopicStructural Health Monitoring Techniques
Canadian institutionsQueen's University
FundersPublic Works and Government Services CanadaGovernment of Canada
KeywordsBridge (graph theory)Lift (data mining)Structural engineeringRehabilitationEngineeringChord (peer-to-peer)Digital image correlationLoad testingDisplacement (psychology)Civil engineeringComputer sciencePhysical therapyMaterials sciencePsychology

Abstract

fetched live from OpenAlex

The LaSalle Causeway lift bridge is a 100 year old highway bridge that is integral to the transportation network of Kingston, Ontario, Canada. Rehabilitation of the bridge was planned to address corrosion of the bottom chord and a gap identified at the support of the lifting end. To determine the effectiveness of this rehabilitation, the displacements of the bridge were monitored before and after the rehabilitation, during a static load test, and under regular traffic loading. Since the bridge crosses the Cataraqui River, there are no stationary reference points near midspan from which to measure displacement using conventional sensors. For this reason, digital image correlation was selected as an appropriate monitoring technology. Displacements at the midspan and supports of the bridge were recorded and used to assess the performance of the bridge. The results of this research indicate that the bridge complies with displacement limits and that the rehabilitation of the bridge was successful in rehabilitating the support conditions of the bridge.

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 categoriesMeta-epidemiology (narrow)
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 score0.422
Threshold uncertainty score1.000

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 communication0.0000.001
Open science0.0010.000
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 head0.018
GPT teacher head0.274
Teacher spread0.255 · 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.

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

Citations8
Published2017
Admission routes3
Has abstractyes

Explore more

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