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Record W2096673926 · doi:10.1139/l06-106

Digital image correlation analysis of crack behavior in a reinforced concrete beam during a load test

2006· article· en· W2096673926 on OpenAlexfundvenueno aff
Marc Jolin, Josée Bastien, Franck Perez, François Hild

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2006
Typearticle
Languageen
FieldComputer Science
TopicOptical measurement and interference techniques
Canadian institutionsnot available
FundersMinistère des Transports
KeywordsDigital image correlationStructural engineeringDurabilityDisplacement (psychology)Beam (structure)Load testingDisplacement fieldMaterials scienceReinforced concreteShear (geology)Composite materialEngineeringFinite element method

Abstract

fetched live from OpenAlex

A displacement-measuring technique using digital image cross-correlation was applied to study the in situ behavior of a shear crack in a reinforced concrete beam during a bridge static load test. A numerical approach allowed measurement of the displacement field at the location of the crack on a 110 mm × 130 mm surface with a resolution of the order of 10 µm. Results of the analysis indicate that crack loading varies significantly with load position and sequence of applications to the structure. These results further indicate that damage cannot be attributed to the repetition of a single load cycle, as is often the case. The findings should improve the understanding of the fatigue behavior of reinforced concrete structures under operating conditions.Key words: displacement field, crack opening displacement, digital image correlation, static loading test, fatigue, durability, reinforced concrete, monitoring.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.007
GPT teacher head0.191
Teacher spread0.185 · 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 designBench or experimental
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

Citations91
Published2006
Admission routes2
Has abstractyes

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