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Record W2951420150 · doi:10.1139/cjce-2019-0093

Study of warm mix asphalt using reclaimed asphalt pavement and copper slag: a review

2019· review· en· W2951420150 on OpenAlexvenueno aff
Ishfaq Mohi Ud Din, Mohammad Shafi Mir

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2019
Typereview
Languageen
FieldEngineering
TopicAsphalt Pavement Performance Evaluation
Canadian institutionsnot available
Fundersnot available
KeywordsAsphaltCopper slagAsphalt pavementEnvironmental scienceRoad constructionWaste managementCivil engineeringEngineeringCopperMaterials scienceMetallurgy

Abstract

fetched live from OpenAlex

The incorporation of copper slag (CS) in asphalt pavements proves to be a good substitute in replacing the natural aggregates. This reduces pavement construction cost and plays a key role in protecting the environment. The CS can be used with recycled asphalt pavement (RAP) material especially in warm mix asphalt pavements. The use of RAP not only leads to environmental benefits but also considerably saves natural resources and decreases the requirement to use virgin bitumen. The use of CS in pavement construction sector eliminates the disposal as well as leaching problems associated with it. This paper focuses on the review of studies carried out on the use of CS along with RAP in road construction. It presents and discusses the work done on the use of CS in the pavement construction sector and the use of RAP in providing the necessary stiffening effect to the asphalt pavements. It analyzes the requirement and advantage of using CS with RAP in asphalt pavements in light of previous research findings and its influence on various engineering properties in pavements. This paper also reviews the work done to study the environmental impact of using CS in asphalt mixes.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.789
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.070
GPT teacher head0.301
Teacher spread0.231 · 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.

Study designNot applicable
Domainnot available
GenreReview

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

Citations23
Published2019
Admission routes1
Has abstractyes

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