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Record W4387503150 · doi:10.3390/ma16206631

Mechanism and Development of Thermo-Rheological Properties of Asphalts Modified by Reactive Polymer Systems

2023· article· en· W4387503150 on OpenAlexafffund
Juan Sebastian Perez Jaimes, Esteban Felipe Tellez Vega

Bibliographic record

VenueMaterials · 2023
Typearticle
Languageen
FieldEngineering
TopicAsphalt Pavement Performance Evaluation
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsRheologyMaterials scienceMechanism (biology)PolymerComposite materialPolymer scienceChemical engineeringEngineeringPhilosophy

Abstract

fetched live from OpenAlex

The new class of reactive polymers is designed to modify asphalt through chemical reactions with asphalt components. The complexity of such systems and the long experience with thermoplastic elastomers as well as with some other "classical" modifiers, and to a degree that our present testing methods and even specifications revolve around these materials, might obscure the fact that we are dealing with rather different modification systems and possibly with new emerging asphalt paving technologies. Our present work attempted to compare two different reactive polymer systems with the "classical" system which uses thermoplastic elastomer. The impact of reactive polymer systems on asphalt was studied through material properties manifested by specification tests and through the development of thermo-rheological properties in linear and non-linear viscoelastic regions. As expected, the behavior of reactive polymeric systems with different chemistries also differed among themselves. The available results showed that the reactive groups of polymers react with polar components of asphalt leading to higher stiffness at elevated pavement temperatures and differing impact on low temperature properties. The data point to a significantly improved resistance to plastic deformation of pavement in the case of reactive polymers, despite the fact that elastic recovery-based specification tests failed to identify this improvement.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.284

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 head0.045
GPT teacher head0.232
Teacher spread0.186 · 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

Citations11
Published2023
Admission routes2
Has abstractyes

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