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Record W2025761922 · doi:10.5539/mas.v5n4p166

Evaluation of VMA and Film Thickness Requirements in Hot-Mix Asphalt

2011· article· en· W2025761922 on OpenAlexvenueno aff
Haydar Raheem Hmoud

Bibliographic record

VenueModern Applied Science · 2011
Typearticle
Languageen
FieldEngineering
TopicAsphalt Pavement Performance Evaluation
Canadian institutionsnot available
Fundersnot available
KeywordsAsphaltAggregate (composite)Materials scienceVoid (composites)Composite materialWearing courseAsphalt pavementForensic engineeringEngineering

Abstract

fetched live from OpenAlex

The asphaltic mixture consists of three main parts: the aggregate, the binder and air voids between the granules. The percentage of air void is important, where high air voids will produce crack sensitive pavement due to oxidation of asphalt or filling the voids by water which will cause striping of aggregate. The asphalt content is important too, where low content will increase stiffness of pavement and high content will increase skidding problems, therefore; the sum of air voids and asphalt content is called Voids in Mineral Aggregate (VMA), where it had been found by several researchers and transportation departments that VMA depends on maximum aggregate size where, for 19 mm maximum aggregate size, VMA is 14% and 13% for 12.5 mm aggregate size. Besides VMA, it had been found that average thickness of asphalt film coating the aggregate of 8 microns will produce durable mixture. VMA limit has not been included in Iraqi standards, therefore, this study focused on collecting literatures on these parameters, analyzing mixtures prepared under current standards to evaluate VMA and film thickness where, for surface course (12.5 mm aggregate size) VMA was 10% and film thickness was 9 microns and for binder course (19 mm aggregate size) VMA was 10% and film thickness was 9.6 microns. The final conclusion was to use VMA and film thickness as parameters in the design of asphaltic mixture as well as current standards and the study included the necessary equations to be used in the calculations of VMA and average film thickness.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.086
GPT teacher head0.292
Teacher spread0.206 · 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 source (direct Gemma or distilled Codex), 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

Citations24
Published2011
Admission routes1
Has abstractyes

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