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Record W3126678858 · doi:10.1080/14680629.2021.1925577

Mechanical performance of rehabilitated bituminous layers with paving fabric under cyclic loading

2021· article· en· W3126678858 on OpenAlexafffund
Ehsan Solatiyan, Nicolas Bueche, Alan Carter

Bibliographic record

VenueRoad Materials and Pavement Design · 2021
Typearticle
Languageen
FieldEngineering
TopicAsphalt Pavement Performance Evaluation
Canadian institutionsÉcole de Technologie SupérieureUniversité du Québec à Montréal
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAsphaltForensic engineeringGeologyGeotechnical engineeringEngineeringMaterials scienceComposite material

Abstract

fetched live from OpenAlex

The application of paving fabric along with a fresh asphalt overlay has proved its effectiveness in terms of delaying the propagation of reflective cracks. Nevertheless, this application requires an in-depth knowledge of its mechanical performance under cyclic loading during the design life. In this context, rutting resistance and fatigue failure are commonly employed as major performance criteria in mechanistic-based design methods. The objective of this study was to understand the necessary changes in distress prediction models embedded in mechanistic design methods in the presence of paving fabric. Rutting resistance was evaluated using the French Laboratory Rut Tester (FLRT) device and adhesion bond at the interface was studied via a method initially developed in Département de Génie Civil et Bâtiment (DGCB). The results showed that the composite structures including paving fabric had lower rutting resistance, especially at higher number of cycles, but 30 percent enhanced adhesion bond at the interface.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.689

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 head0.022
GPT teacher head0.224
Teacher spread0.202 · 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
Published2021
Admission routes2
Has abstractyes

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