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Record W3191976499 · doi:10.1080/14680629.2021.1957000

Evaluation of the effect of the loading frequency on Wöhler’s curve parameters of a high modulus asphalt concrete (HMAC)

2021· article· en· W3191976499 on OpenAlexaff
Mohamed Mounir Boussabnia, Daniel Perraton, Sébastien Lamothe, Hervé Di Benedetto, Charles Neyret, Alan Carter, Bertrand Pouteau, Marc Proteau

Bibliographic record

VenueRoad Materials and Pavement Design · 2021
Typearticle
Languageen
FieldEngineering
TopicAsphalt Pavement Performance Evaluation
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsAsphaltMaterials scienceComposite materialModulusUltimate tensile strengthAsphalt concreteTensile strainGeotechnical engineeringEngineering

Abstract

fetched live from OpenAlex

The fatigue's behaviour of bituminous materials is affected by the loading frequency. For hot mix asphalt (HMA) with unmodified bitumen, previous research showed a decrease in fatigue life, under strain-control mode, by increasing loading frequency. However, the effect of loading frequency on polymer-modified HMA has not been investigated extensively and researchers have found opposite results using the same testing method in strain-controlled mode. This paper looks at how the loading frequency impacts Wöhler's law parameters (slope and intercept) of a high modulus asphalt concrete (HMAC) made with a polymer-modified bitumen. Under strain-controlled mode, Tensile-Compression tests (TC) on cylindrical samples were carried out at isothermal conditions (θ = 10°C) and three distinct frequencies (5, 10 and 25 Hz). The analysis of covariance (ANCOVA) was considered to compare Wöhler's law parameters from different frequencies. The results showed no statistical difference in the slope and the intercept values from all studied frequencies.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.034
GPT teacher head0.256
Teacher spread0.223 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations10
Published2021
Admission routes1
Has abstractyes

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