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Effect of RAP Source on Compactability and Behavior of Cold-Recycled Mixtures in the Small Strain Domain

2021· article· en· W3125328988 on OpenAlexaff
Simone Raschia, Taher Baghaee Moghaddam, Daniel Perraton, Hassan Baaj, Alan Carter, Andrea Graziani

Bibliographic record

VenueJournal of Materials in Civil Engineering · 2021
Typearticle
Languageen
FieldEngineering
TopicAsphalt Pavement Performance Evaluation
Canadian institutionsUniversity of WaterlooÉcole de Technologie Supérieure
Fundersnot available
KeywordsGradationCompactionMaterials scienceAsphaltRheologyAggregate (composite)ModulusComposite materialComputer science

Abstract

fetched live from OpenAlex

Cold recycled materials (CRMs) are products of modern recycling techniques that are used in maintenance and rehabilitation of pavement structures with significant economical and environmental benefits. CRM mixes are produced at ambient temperature using bitumen emulsion or foamed bitumen as a binding agent, and the aggregate phase is composed mainly of reclaimed asphalt pavement (RAP). This paper investigated the compactability of two RAP sources and their effects on the behavior of CRM mixtures tested in the small strain domain. The compactability was studied using experimental results and the compressible packing model (CPM). Complex modulus tests of CRM mixtures were conducted, and the results were modeled using the Di Benedetto–Neifar (DBN) model. Findings showed that in CRM mixtures with the same gradation and formulation, one RAP source required almost half of the compaction energy of the other source to reach the design air voids content. The rheological analysis results highlighted the impact of the RAP source on the behavior of the CRM mixes in the small strain domain.

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)0.002
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 score0.102
Threshold uncertainty score0.412

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.010
GPT teacher head0.242
Teacher spread0.233 · 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

Citations13
Published2021
Admission routes1
Has abstractyes

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