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Record W4280531797 · doi:10.1080/14680629.2022.2064903

Rheological multi-scale evaluation of RAP and RAS binders mobilisation in hot mix asphalt

2022· article· en· W4280531797 on OpenAlexaff
Abdeldjalil Daoudi, Anne Dony, Daniel Perraton, Alan Carter

Bibliographic record

VenueRoad Materials and Pavement Design · 2022
Typearticle
Languageen
FieldEngineering
TopicAsphalt Pavement Performance Evaluation
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsAsphaltAsphalt pavementRheologyScale (ratio)ViscoelasticityMaterials scienceEnvironmental scienceGeotechnical engineeringComposite materialEngineering

Abstract

fetched live from OpenAlex

The use of Reclaimed Asphalt Pavement (RAP) and Recycled Asphalt Shingles (RAS) in asphalt mixes is a constructive solution allowing economic, environmental and technical benefits. The objective here is evaluating the binder mobilisation rate of recycled materials by predicting the viscoelastic (LVE) behaviour of the asphalts from the LVE of their theoretical binder. The multi-scale approach adopted here goes from the binder scale to the mix scale. At the binder scale, after the extraction/recovery of RAP and RAS binders, several bitumen blends were produced and characterised by conventional and rheological tests. At the asphalt scale, the LVE behaviour of several mixes containing RAP and RAS was characterised using complex modulus test. Then, Shift-Homothety-Shift-time-Shift-transformation (SHStS) was applied allowing verifying the correspondence degree between the experimental behaviour and the predicted one. The results confirm that the totality of the RAP binder was mobilised and up to 50% in the case of RAS.

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)0.004
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 score0.476
Threshold uncertainty score0.974

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
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)0.0010.000

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 head0.076
GPT teacher head0.282
Teacher spread0.206 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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