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Record W2092045282 · doi:10.1139/l06-141

Developing a structural-health-monitoring model to monitor cracking in steel-free concrete deck slabs

2007· article· en· W2092045282 on OpenAlexfundvenueaboutno aff
Chaochao Yang, John Newhook

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2007
Typearticle
Languageen
FieldEngineering
TopicStructural Health Monitoring Techniques
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCrackingDeckStructural health monitoringStructural engineeringBridge (graph theory)SlabGirderStructural loadLoad distributionEngineeringCivil engineeringMaterials science

Abstract

fetched live from OpenAlex

The ISIS Canada Networks of Centres of Excellence (NCE) program has focused on two main themes to improve civil engineering infrastructure, namely innovative construction technologies, and structural health monitoring (SHM). The former began with the construction of the first field application of the innovative steel-free concrete bridge deck slab technology at the Salmon River bridge, Nova Scotia, in 1995. Although this bridge has continued to function safely under heavy traffic loads, it has developed characteristic longitudinal cracking of the concrete between adjacent girders due to fatigue. This paper describes the recent research to develop an SHM model for monitoring the impact and stability of this cracking. Theoretical and experimental models were used to examine the change in response as cracking develops. A global load distribution matrix was proposed, and the variation in load distribution values with cracking was used to develop a cracking index that can be employed in monitoring the field structure.Key words: structural health monitoring, bridges, concrete, deck slabs, cracking, load distribution.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.680
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.025
GPT teacher head0.281
Teacher spread0.256 · 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

Citations3
Published2007
Admission routes3
Has abstractyes

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