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Record W3187223545 · doi:10.1139/cjce-2021-0078

Gradation characteristics-based interlayer mixture design method for enhanced rutting resistance of asphalt pavements

2021· article· en· W3187223545 on OpenAlexvenueno aff
Bongsuk Park, Cristian Cocconcelli, Sang‐Hyun Chun

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2021
Typearticle
Languageen
FieldEngineering
TopicAsphalt Pavement Performance Evaluation
Canadian institutionsnot available
FundersFlorida Department of Transportation
KeywordsRutGradationAsphaltPorosityMaterials scienceStiffnessComposite materialCrackingAggregate (composite)Geotechnical engineeringAsphalt pavementRange (aeronautics)Geology

Abstract

fetched live from OpenAlex

The major role of interlayer mixtures is to mitigate reflective cracking by absorbing or dissipating concentrated stress, and relatively low-stiffness materials are typically used. However, there is a concern that interlayer mixtures may increase the risk of rutting because of these low-stiffness materials. The dominant aggregate size range (DASR) porosity has been successfully applied for structural mixtures to ensure enhanced rutting performance. This study mainly focused on developing new DASR porosity requirements for interlayer mixtures that ensures acceptable rutting performance in the mix design phase. Ten interlayer mixtures with a broad range of DASR porosities were evaluated using the asphalt pavement analyzer test. Our results indicated that the gradation characteristics of interlayer mixtures played an important role in rutting performance. Also, a relationship between DASR porosity and the rutting potential of interlayer mixtures was identified that resulted in the establishment of the preliminary DASR porosity requirements.

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)0.001
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: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.886
Threshold uncertainty score0.792

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.017
GPT teacher head0.245
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 designSimulation or modeling
Domainnot available
GenreMethods

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
Published2021
Admission routes1
Has abstractyes

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