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Record W4297398419 · doi:10.1111/ffe.13849

Validation of time temperature superposition principle for high modulus asphalt concrete in the linear viscoelastic and fatigue domains

2022· article· en· W4297398419 on OpenAlexafffund
Mohamed Mounir Boussabnia, Daniel Perraton, Hervé Di Benedetto, Sébastien Lamothe, Marc Proteau

Bibliographic record

VenueFatigue & Fracture of Engineering Materials & Structures · 2022
Typearticle
Languageen
FieldEngineering
TopicAsphalt Pavement Performance Evaluation
Canadian institutionsÉcole de Technologie Supérieure
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsTime–temperature superpositionViscoelasticityMaterials scienceSuperposition principleStructural engineeringModulusAsphaltAsphalt concreteComposite materialTension (geology)Time domainCompression (physics)EngineeringMathematicsComputer scienceMathematical analysis

Abstract

fetched live from OpenAlex

Abstract Validation of time–temperature superposition principle (TTSP) in the fatigue domain for a high modulus asphalt concrete (HMAC) is presented in this paper. All tests were performed in tension‐compression under strain control mode. First, TTSP was validated in the linear viscoelastic domain. Then, fatigue tests were performed under three loading conditions, 9.2°C and 5 Hz, 11.0°C and 10 Hz and 12.9°C and 20 Hz, which are equivalent regarding TTSP. Two fatigue protocols were adopted: continuous fatigue test (FT) and fatigue test with rest period (FTRP). For FT, three samples were tested at 180μm/m for each loading condition whereas for FTRP, one sample was tested at 100 μm/m. The data were analysed by comparing the dynamic modulus evolution as a function of time or the fatigue life duration. The results showed that HMAC with fatigue damage remains thermorheologically simple (i.e., respects the TTSP) in the studied temperatures range.

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)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.010
GPT teacher head0.245
Teacher spread0.235 · 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 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

Citations14
Published2022
Admission routes2
Has abstractyes

Explore more

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