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Optimisation of Recoverable Horizontal Deformation and Size Ratio of Compacted Rubberized Stone Mastic Asphalt Based on Rubber and Binder Contents Using Response Surface Methodology

2022· article· en· W4220724084 on OpenAlexaff
Sajjad Noura, Abdulnaser M. Al Sabaeei, Gailan Ismat Safaeldeen, Sina Mirzapour Mounes, Ratnasamy Muniandi, Ramez A. Al-Mansob, Alan Carter

Bibliographic record

VenueIOP Conference Series Earth and Environmental Science · 2022
Typearticle
Languageen
FieldEngineering
TopicAsphalt Pavement Performance Evaluation
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsNatural rubberCompactionMaterials scienceAsphaltDeformation (meteorology)Composite materialGeotechnical engineeringModulusGeology

Abstract

fetched live from OpenAlex

Abstract The use of waste materials such as rubber powder in stone mastic asphalt (SMA) has improved the structural performance to some extent, and the research on the improvement is still ongoing. Rubberised stone mastic asphalt has shown good performance in terms of resilient modulus. In this research, response surface methodology was utilised to increase the efficiency in determining the recoverable horizontal deformation and samples compaction level using optimum rubber and binder content. Thirty-one tests were performed on different binders and rubber contents, and the recoverable horizontal deformation and ratio of height to the diameter of Marshall compacted samples were recorded. The ANOVA analysis showed a low P-value with a high correlation coefficient, and the optimisation showed that adding almost 3% rubber powder to SMA could improve the compaction level and desirable recoverable deformation.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.543
Threshold uncertainty score0.485

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 communication0.0000.001
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 head0.048
GPT teacher head0.252
Teacher spread0.204 · 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

Citations1
Published2022
Admission routes1
Has abstractyes

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