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Record W2930293069 · doi:10.1177/0361198119834908

Effects of Preheating on the Rheological Properties of Rejuvenated Asphalt Binder

2019· article· en· W2930293069 on OpenAlexaff
Yang Liu, Hainian Wang, Susan Tighe, Guangyuan Zhao, Zhanping You

Bibliographic record

VenueTransportation Research Record Journal of the Transportation Research Board · 2019
Typearticle
Languageen
FieldEngineering
TopicAsphalt Pavement Performance Evaluation
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsAsphaltDynamic shear rheometerRheologyMaterials scienceRutCrackingFatigue crackingComposite materialAsphalt pavementRheometerCreepGeotechnical engineeringEnvironmental scienceGeology

Abstract

fetched live from OpenAlex

Hot in-place recycling (HIR) is a process that reuses reclaimed asphalt materials to benefit the environment and conserve natural resources. HIR has not yet been widely applied in China because of technical drawbacks such as reclaimed materials’ fatigue properties and poor cracking resistance. Preheating is the first procedure in HIR and significantly affects the diffusion of rejuvenators within the aged asphalt and the remixing of virgin and aged mixtures. This study focuses on the effects of preheating on the rheological properties of rejuvenated asphalt binder using dynamic shear rheometer (DSR) testing. Three types of rejuvenators (Reju-A, Reju-B and Reju-C), three types of base asphalt binder (#50, #70, and #90), recovered asphalt from recycled asphalt pavement, and laboratory-aged asphalt were investigated to analyze the rheological properties under different heating conditions. The results show that (1) rejuvenators contain little in the way of viscous characteristics and have a viscosity-reducing effect on aged asphalt; (2) field-aged asphalt loses cohesion and gains rutting resistance during aging; and (3) preheating has a significant effect on diffusion of rejuvenators within aged asphalt as well as on rheological properties. In short, a good controlled preheating operation is critical to the quality of the asphalt rejuvenation process.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.430
Threshold uncertainty score0.703

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
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 head0.080
GPT teacher head0.333
Teacher spread0.253 · 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

Citations5
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueTransportation Research Record Journal of the Transportation Research BoardSame topicAsphalt Pavement Performance EvaluationFrench-language works237,207