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Record W4378894895 · doi:10.1061/jmcee7.mteng-15035

Investigating the Addition of Organomontmorillonite Nanoclay and Its Effects on the Performance of Asphalt Binder

2023· article· en· W4378894895 on OpenAlexaff
Liniker Monteiro, Taher Baghaee Moghaddam, Mohammad Shafiee, Leila Hashemian

Bibliographic record

VenueJournal of Materials in Civil Engineering · 2023
Typearticle
Languageen
FieldEngineering
TopicAsphalt Pavement Performance Evaluation
Canadian institutionsNational Research Council CanadaUniversity of AlbertaCanadian Natural Resources
Fundersnot available
KeywordsDynamic shear rheometerMaterials scienceAsphaltComposite materialRheologyMontmorilloniteRutServiceability (structure)Asphalt pavementStructural engineering

Abstract

fetched live from OpenAlex

Asphalt binder aging is a complex phenomenon that plays an essential role in reducing flexible pavement serviceability. The aging resistance of asphalt binder can be enhanced through the binder modification. This research investigates the impact of aging on unmodified and organo-montmorillonite (OMMT) nanoclay modified asphalt binders. In this study, laboratory aging is carried out for asphalt binders modified with two types of OMMT nanoclays at different dosages (2% and 4% by weight of the binder). The asphalt binders are subjected to short-term and long-term aging using a rolling thin-film oven and pressure aging vessel equipment. Rheological properties of the aged and unaged binders are determined using a dynamic shear rheometer. In addition, storage stability, full performance grading, and rutting resistance of the binders are evaluated and compared. Aging indices were calculated to investigate the aging behavior of the unmodified and modified binders. The results showed that the montmorillonite nanoclay-modified asphalts had improved resistance to aging compared with the unmodified binder.

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)0.002
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 score0.230
Threshold uncertainty score0.329

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.218
Teacher spread0.205 · 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

Citations9
Published2023
Admission routes1
Has abstractyes

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