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Record W2150932733 · doi:10.3141/2044-11

Effect of Bituminous Material Rheology on Adhesion

2008· article· en· W2150932733 on OpenAlexaff
Imad L. Al‐Qadi, Eli H Fini, Jean‐François Masson, Kevin M. McGhee

Bibliographic record

VenueTransportation Research Record Journal of the Transportation Research Board · 2008
Typearticle
Languageen
FieldEngineering
TopicAsphalt Pavement Performance Evaluation
Canadian institutionsNational Research Council Canada
FundersFederal Highway Administration
KeywordsSealantMaterials scienceAsphaltViscoelasticityComposite materialRheologyAdhesiveAdhesionViscosityPaint adhesion testingGlass transitionBond strengthPolymerLayer (electronics)

Abstract

fetched live from OpenAlex

Bituminous materials are used in many civil engineering applications in which adhesion to a substrate is essential for good performance. Yet it is not possible to predict the adhesion of these materials. The particular case of bituminous crack sealants is of interest; the effect of sealant viscosity, aging, test temperature, and loading rates was investigated by means of a blister test. This test provided the bonding characteristics to a model aggregate in relation to interfacial fracture energy (IFE). From testing of several sealants, it was found that pouring viscosity affects adhesion and that higher viscosities help to attain higher IFEs. Temperature was found to play a key role on bonding characteristics and failure mechanism because it affected the viscoelastic properties of the sealant. The glass transition temperature (T g ) was found to have a governing role on bonding characteristics. At temperatures above T g , bond strength was found to be affected by sealant flow such that failure was flow related; that is, cohesive failure prevailed. At temperatures below the T g , at which sealants were stiff and bulk deformation was low, stress was directed toward the interface so that failure tended to be adhesive. In taking into account temperature and test rates, an IFE master curve was obtained for a sealant. Such a curve may be used in predicting and comparing sealant IFE.

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)0.005
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.112
Threshold uncertainty score0.729

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
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 head0.065
GPT teacher head0.366
Teacher spread0.301 · 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 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

Citations15
Published2008
Admission routes1
Has abstractyes

Explore more

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