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Compactability and mechanical properties of cold recycled mixes prepared with different nominal maximum sizes of RAP

2022· article· en· W4225377282 on OpenAlexaff
P. Orosa, Gabriel Orozco, Jean‐Claude Carret, A. Carter, Ignacio Pérez Pérez, A.R. Pasandín

Bibliographic record

VenueConstruction and Building Materials · 2022
Typearticle
Languageen
FieldEngineering
TopicAsphalt Pavement Performance Evaluation
Canadian institutionsÉcole de Technologie Supérieure
FundersUniversidade da CoruñaMinisterio de Ciencia e Innovación
KeywordsMaterials scienceUltimate tensile strengthComposite materialStiffnessModulusAsphaltVoid (composites)CrackingCompression (physics)

Abstract

fetched live from OpenAlex

The use of cold recycled asphalt mixtures (CRM) has been soaring during recent years. Reclaimed Asphalt Pavement (RAP) is the main component of CRM, and despite the numerous studies on CRM, the impact of different RAP types has not been deeply studied. This study compares the volumetric and several mechanical properties of CRM prepared with RAP from two different sources and with various nominal maximum sizes (NMS). The mix design was fixed, and specimens were prepared using gyratory and impact compactors. Densities were measured before and after accelerated curing. Stiffness of CRM was investigated with Indirect Tensile Stiffness Modulus, tension–compression, and dynamic tests. Additionally, the cracking behavior was evaluated with Indirect Tensile Strength and Semi-Circular Bending tests. The particle size distribution was a key factor in the compactability of the CRMs studied. Together with temperature, the most influential factor on the studied mechanical properties was the air void content, while the differences in NMS showed no clear trends.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.285

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 head0.015
GPT teacher head0.209
Teacher spread0.194 · 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

Citations20
Published2022
Admission routes1
Has abstractyes

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