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Record W1972789488 · doi:10.1139/l07-073

Long-term structural health monitoring of the Crowchild Trail Bridge

2008· article· en· W1972789488 on OpenAlexafffundvenueabout
Tim R. VanZwol, J. J. Roger Cheng, Gamil Tadros

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2008
Typearticle
Languageen
FieldEngineering
TopicStructural Health Monitoring Techniques
Canadian institutionsRead Jones Christoffersen (Canada)BP (Canada)University of Alberta
FundersMinistère des TransportsUniversity of Alberta
KeywordsServiceability (structure)Bridge deckDeckStructural health monitoringStructural engineeringBridge (graph theory)EngineeringCrackingStructural loadForensic engineeringCivil engineeringMaterials scienceComposite material

Abstract

fetched live from OpenAlex

Steel-free deck design is a relatively new approach to bridge deck construction. Several steel-free deck bridges constructed in Canada exhibited satisfactory behaviour immediately after construction. However, the long-term health of many of these structures has not been investigated. Significant cracking has been observed in many of these structures after only a few years in service. Long-term structural health monitoring of these structures is key in determining their long-term performance and future adoption of this new technology. Structural health monitoring data were obtained from the steel-free deck of the Crowchild Trail Bridge during its first seven years in service. Field monitoring included ambient vibration tests, static and dynamic load tests, and deck crack mapping. Despite significant amounts of cracks in the concrete deck and barriers, the overall behaviour of the Crowchild Trail Bridge has remained satisfactory and consistent. Future steel-free deck designs should consider crack control to meet serviceability requirements.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.018
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.021
GPT teacher head0.247
Teacher spread0.225 · 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 source (direct Gemma or distilled Codex), 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
Published2008
Admission routes4
Has abstractyes

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Same venueCanadian Journal of Civil EngineeringSame topicStructural Health Monitoring TechniquesFrench-language works237,207