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Record W7001247611

Interfacial fracture energy: an indicator of the adhesion of bituminous materials

2010· article· en· W7001247611 on OpenAlexvenueno aff

Bibliographic record

VenueNPARC · 2010
Typearticle
Languageen
FieldArts and Humanities
TopicFrench Historical and Cultural Studies
Canadian institutionsnot available
FundersFederal Highway Administration
KeywordsAsphaltBrittlenessAdhesiveSealantShear (geology)Strain energy release rateDeflection (physics)Fracture (geology)
DOInot available

Abstract

fetched live from OpenAlex

This paper demonstrates that the pressurized blister test can be an effective method to predict binder-aggregate bonding. Recently, the blister test has been introduced as a reliable approach to predict the bonding between bituminous sealant and aggregate. Since this test measures a geometry-independent parameter that is an inherent property of the interface, the test can be applied to any bituminous material, from the softest bituminous crack sealant to the most brittle binder. With very brittle material, cohesive failure becomes a concern. Such a failure can be easily prevented by an increase in the thickness of the adhesive specimen. However, an increase in specimen thickness also gives rise to shear forces that cannot be neglected in the analysis. Utilizing theoretical and experimental analyses, this paper presents the effect of shear forces on the interfacial fracture energy (IFE) of adhesive bituminous materials. The effect of shear forces on blister deflection is shown as a function of material thickness. In addition, the dependence of IFE of bituminous materials on temperature and rate of loading was investigated through laboratory testing. An optimum temperature and loading rate can be identified for each material where IFE is optimized. This may help select appropriate binder/sealant - aggregate pairs for improved performance under defined environmental conditions

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 categoriesInsufficient payload (model declined to judge)
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 score0.610
Threshold uncertainty score0.995

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)0.0060.000

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 head0.016
GPT teacher head0.198
Teacher spread0.182 · 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 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
Published2010
Admission routes1
Has abstractyes

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