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Record W3120678361 · doi:10.1139/cjce-2020-0644

Laboratory evaluation of cracking resistance for asphalt mixtures modified with nanoclay and nanocellulose

2021· article· en· W3120678361 on OpenAlexafffundvenue
Thomas Johnson, Nura Bala, Alireza Bayat, Leila Hashemian

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2021
Typearticle
Languageen
FieldEngineering
TopicAsphalt Pavement Performance Evaluation
Canadian institutionsUniversity of Alberta
FundersUniversity of AlbertaAlberta-Pacific Forest Industries
KeywordsAsphaltMaterials scienceCrackingDynamic shear rheometerComposite materialNanocelluloseNanomaterialsRheometerRheologyAsphalt concreteRutCelluloseNanotechnologyEngineeringChemical engineering

Abstract

fetched live from OpenAlex

Cracking failure is one of the major distress modes associated with asphalt pavement. Asphalt modification has been identified as an effective method to improve pavement performance. In this study, nanomaterials including bentonite and halloysite nanoclays and nanocellulose are added to PG 64-28 straight run asphalt binder for modification. The potential of these nanomaterials for improving the cracking performance of asphalt pavements at intermediate pavement service temperatures is investigated. Rheological evaluation is conducted using a dynamic shear rheometer (DSR), and the cracking resistance of the asphalt mixtures is determined through indirect tension asphalt cracking test (IDEAL-CT). A high-shear mixer is used to disperse the nanomaterial in the asphalt and the field emission scanning electron microscope (FESEM) analysis shows a relatively good dispersion of the nanomaterials. Furthermore, the results of the IDEAL-CT show an improvement in cracking test index by as much as 47%–114% through nanomaterial modification.

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)0.001
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.227
Threshold uncertainty score0.762

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.014
GPT teacher head0.217
Teacher spread0.203 · 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 designSimulation or modeling
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
Published2021
Admission routes3
Has abstractyes

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