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Record W3199644883 · doi:10.1520/jte20210168

Comparative Evaluation of Aging Effect Difference between Aging Patterns for Asphalt Binder and Mixture

2021· article· en· W3199644883 on OpenAlexfundno aff
Hongming Huang, Zihao Chen, Juechi Li, Junzhuo Wang, Yang Fang, Chaofan Wu, Henglong Zhang

Bibliographic record

VenueJournal of Testing and Evaluation · 2021
Typearticle
Languageen
FieldEngineering
TopicAsphalt Pavement Performance Evaluation
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of China
KeywordsAsphaltMaterials scienceComposite materialAccelerated agingForensic engineeringEngineering

Abstract

fetched live from OpenAlex

Abstract Aging simulation methods for asphalt materials can be generally classified into two types: for asphalt binder and for asphalt mixture. Nevertheless, there exists the aging effect difference between aging patterns for asphalt binder and mixture due to differences in aging experimental conditions. The comparison of the aging effect difference between aging patterns for asphalt binder and mixture contributes to building the relationship and thus achieving a conversion of asphalt aging degree between its binder and mixture aging patterns. In this research, aging effect differences between aging patterns for binder and mixture were investigated in terms of short-term aging, long-term aging, and ultraviolet radiation (UV) aging. The binders were collected before and after different aging patterns and then characterized by physical, rheological, and chemical tests. The results indicate that the aging effect difference between thin film oven test and short-term oven aging test is slight, whereas the aging effect of pressure aging vessel test significantly outweighs the effects of long-term aging patterns with loose or compacted specimens. As for photo oxidation aging, the aging effect ranking from the highest to the lowest is UV aging with loose specimen, UV aging for binder, and UV aging with compacted specimen. In addition, regardless of long-term or UV aging pattern for mixture, the aging degree of binder in loose specimen is more serious than that in compacted specimen.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.486
Threshold uncertainty score0.473

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
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 head0.109
GPT teacher head0.365
Teacher spread0.256 · 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 designObservational
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

Citations2
Published2021
Admission routes1
Has abstractyes

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