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Record W1984698396 · doi:10.1002/masy.200551004

Time Scales in Polymer Modified Asphalts

2005· article· en· W1984698396 on OpenAlexaff
Giovanni Polacco, Jiri Stastna, Zora Vlachovicova, Ludovit Zanzotto, Pierluigi Magagnini

Bibliographic record

VenueMacromolecular Symposia · 2005
Typearticle
Languageen
FieldEngineering
TopicAsphalt Pavement Performance Evaluation
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsViscoelasticityAsphalteneAsphaltPolymerMaterials scienceRheologyComposite materialViscosityNonlinear systemThermodynamicsChemistryOrganic chemistryPhysics

Abstract

fetched live from OpenAlex

Abstract Historically, Maxwell was probably the first one who recognized the importance of time scales for understanding the mechanical response of asphalt. In instantaneous response asphalt behaves as an elastic, solid‐like material, on the other hand its long time response is that of a viscous, fluid‐like material. In the linear viscoelastic region asphalt behaves as a low molecular weight polymer. However, in the nonlinear region of high strains or rates of strain the behavior of some asphaltic systems can be rather complicated. In asphalts, asphaltenes, resins and alkanes compose a complex colloidal system, in which alkanes act as a solvent, asphaltenes as micelles and the polar resins as stabilizers. In order to enhance the mechanical properties of asphalts they are frequently modified by blending them with appropriate polymers. Changes in the impermanent network that can be formed in some of these blends can lead to an unexpected behavior of the steady shear viscosity function. Several different time scales emerge from this behavior. A possible relation of these “nonlinear” time scales to the linear viscoelastic time scales is discussed and examples of anomalous behavior of polymer‐modified asphalts are given.

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 score0.451
Threshold uncertainty score1.000

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)0.0000.001

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 head0.005
GPT teacher head0.214
Teacher spread0.208 · 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

Citations7
Published2005
Admission routes1
Has abstractyes

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