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Record W4380449565 · doi:10.52202/069179-0214

CHARACTERIZING THE FIRE PERFORMANCE OF ADHESIVES USED IN GLUED-IN RODS CONNECTIONS

2023· article· en· W4380449565 on OpenAlexaff
Diego Rafael Martins Flores, Christian Dagenais, Pierre Blanchet

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMechanical Behavior of Composites
Canadian institutionsUniversité LavalCanadian Wood Council
Fundersnot available
KeywordsRodAdhesiveMaterials scienceComposite materialFire performanceBrittlenessTension (geology)LimitingStructural engineeringFire resistanceMechanical engineeringCompression (physics)Engineering

Abstract

fetched live from OpenAlex

Glued-in rods are an aesthetically and performant type of connection that has seen its usage increase in the last few years. However, there is still a lack of data concerning the fire performance of glued-in rods, limiting its integration in standards. Recent studies suggest that the adhesive is critical to the fire performance of glued-in rods, since its capacity is greatly reduced when the temperature exceeds its glass transition temperature (Tg), ranging between 45-65 o C for most structural adhesives. To validate these findings, dynamic mechanical analysis (DMA) tests were performed to better assess the Tg and other thermomechanical properties of the adhesives used in this research. Axial tension tests at stabilized temperature were performed on 67 glued-in rods specimens using five different adhesives (three epoxies and two polyurethanes) and various sets of temperatures at the glue line interface. Most of these specimens have shown a ductile failure mode for temperatures below the Tg of the adhesive and a brittle failure mode for temperatures above the Tg. This research helps determine guidelines for the fire design of glued-in rods and related testing, and ultimately leading to a design method for providing fire resistance to connections made of glued-in rods.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.630
Threshold uncertainty score0.205

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 head0.023
GPT teacher head0.245
Teacher spread0.221 · 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 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

Citations1
Published2023
Admission routes1
Has abstractyes

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