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Record W2653306013 · doi:10.6000/1927-5129.2017.13.55

Exploring the Properties of Recycled Tyre Rubber for Flexible Asphalt Pavement

2017· article· en· W2653306013 on OpenAlexvenueno aff
Nadia Khan, Asra Nafees, Ashhar Hussain, Gulshan Zaidi, Muhammad Osama, Seyed Hamidreza Ghaffar

Bibliographic record

VenueJournal of Basic & Applied Sciences · 2017
Typearticle
Languageen
FieldEngineering
TopicAsphalt Pavement Performance Evaluation
Canadian institutionsnot available
Fundersnot available
KeywordsAsphaltNatural rubberAsphalt pavementEnvironmental scienceCrumb rubberAsphalt concreteMaterials scienceWaste managementGeotechnical engineeringComposite materialEngineering

Abstract

fetched live from OpenAlex

In consideration of the issue related to the poor performance of asphalt pavements against climatic stresses and the need to contribute to the solution of the ever-increasing environmental hazards, the study paved the way for the recognition of using waste tyre as an asphalt binder modifier for pavement construction to settle the rising issue on waste disposal, while also improving the pavement properties.A series of experiments were conducted using different levels of recycled tyre rubber (RTR) substituted in asphalt binder. Marshall Stability and Marshall Immersion test were selected as basis to evaluate the properties. The results of experiments conducted on Marshall Mix samples demonstrated that stability of the pavements increased in a quadratic fashion with increasing fraction of RTR, and were found to be maximum at 10% rubber in asphalt mix, contributing an average enhancement of approximately 35% as compared to the conventional pavements. Furthermore, retained stability of samples was found to increase with increasing ratio of rubber substituted, making it a suitable candidate for modification in highly humid and rainy areas.

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 score0.287
Threshold uncertainty score0.372

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 communication0.0000.001
Open science0.0010.000
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 head0.201
GPT teacher head0.300
Teacher spread0.099 · 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

Citations2
Published2017
Admission routes1
Has abstractyes

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