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Record W7111029049 · doi:10.1520/jte20240602

Early-Age Stiffening of Cold Recycled Bituminous Materials Using Shear Wave Velocity

2025· article· en· W7111029049 on OpenAlexaff

Bibliographic record

VenueJournal of Testing and Evaluation · 2025
Typearticle
Languageen
FieldEngineering
TopicAsphalt Pavement Performance Evaluation
Canadian institutionsUniversité de SherbrookeÉcole de Technologie Supérieure
Fundersnot available
KeywordsStiffeningCompactionAsphaltStiffnessShear (geology)Drop (telecommunication)Void (composites)

Abstract

fetched live from OpenAlex

ABSTRACT The characterization of cold recycled bituminous materials (CRMs) at a very young age, shortly after compaction, is inherently challenging due to the nature of the material. The granular aspect of CRM at this stage and its high-water content render the use of conventional mechanical techniques impractical. Following previous work, the use of a nondestructive technique based on the frequency analysis of mechanical elastic shear waves, piezoelectric ring actuator technique (P-RAT), has enabled assessment of the behavior of cold in-place recycled material treated with bitumen emulsion from 10 min after compaction to 30 days of curing. Emphasis on shear wave velocity (Vs) measurements during the early age confirmed the rapid stiffening of the mix along with the departure of water present in the mix. A 6 °C drop of the surface temperature is observed along with this rapid increase of Vs and water loss. The initial and final Vs values range from 287 to 330 m s−1 and from 461 to 578 m s−1, respectively. To assess the capabilities of P-RAT, specimens with different void contents were tested, mainly 12, 15, and 17 %. It was observed that in each tested specimens, a similar behavior was exhibited during the first few hours of curing. Based on these observations, hypotheses are put forth regarding the phenomena governing the increase in stiffness during this period. Finally, the influence of the compaction of the specimens on the Vs values is consistent and comparable with the information available in the literature for such CRM.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.715
Threshold uncertainty score0.365

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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 head0.083
GPT teacher head0.310
Teacher spread0.228 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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