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Record W4405016106 · doi:10.1139/cjce-2024-0263

Multi-criteria optimization of aggregate gradation based on thermal, surface, and mechanical properties of the asphalt solar collector

2024· article· en· W4405016106 on OpenAlexvenueno aff
Marco Pasetto, Andrea Baliello, Giovanni Giacomello, Emiliano Pasquini

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2024
Typearticle
Languageen
FieldEngineering
TopicAsphalt Pavement Performance Evaluation
Canadian institutionsnot available
Fundersnot available
KeywordsGradationAggregate (composite)AsphaltThermalMaterials scienceComposite materialAsphalt concreteAsphalt pavementComputer science

Abstract

fetched live from OpenAlex

The aggregate gradation of asphalt concrete for a road pavement is a critical element since it influences its strength and mechanical properties, and also has a major role on the thermal properties. An experimental study was set up to develop a multi-criteria optimization of AC12 dense-graded asphalt mixtures designed for asphalt solar collector layers according to the Bailey method (fine, middle, or coarse gradation). Samples were tested in terms of thermal potential, surface properties, and mechanical performance. It was found that a coarser gradation increased the heat concentration within the asphalt (more efficient mixture for asphalt solar collector), thanks to lower specific surface area, thicker asphalt films, and fewer interconnected internal voids. The surface and mechanical performance of all asphalt systems matched the common technical prescription limits for surface road layers. Overall, the multi-criteria evaluation was effective in optimizing the characteristics of the solar collector asphalt concrete.

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)0.000
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.087
Threshold uncertainty score0.349

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.018
GPT teacher head0.206
Teacher spread0.188 · 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 designSimulation or modeling
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
Published2024
Admission routes1
Has abstractyes

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