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Record W2889053856 · doi:10.1139/cjce-2018-0299

Evaluation of the effect of deflection waveform on fatigue performance of asphalt mixture in the four point bending beam test

2018· article· en· W2889053856 on OpenAlexvenueno aff
Mariana Gaertner Pintarelli, João Victor Staub de Melo

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2018
Typearticle
Languageen
FieldEngineering
TopicAsphalt Pavement Performance Evaluation
Canadian institutionsnot available
FundersLaboratório Central de Microscopia Eletrônica, Universidade Federal de Santa CatarinaUniversidade Federal de Santa CatarinaPetrobras
KeywordsDeflection (physics)WaveformStructural engineeringAsphaltBeam (structure)AmplitudeMaterials scienceViscoelasticityEngineeringComposite materialPhysicsOpticsElectrical engineering

Abstract

fetched live from OpenAlex

An experimental study was conducted to determine the effect of deflection waveform on four-point flexural fatigue test results for hot mix asphalt. This paper reports how the waveform affects the fatigue resistance of an asphalt mixture and, consequently, the fatigue models of the material. The mix was tested at different strain levels under both haversine and sinusoidal deflection-controlled modes. The findings indicate that haversine displacement control testing results in a sinusoidal strain response of half the intended amplitude. This outcome was attributed to the viscoelastic nature of asphalt mixes. In the deflection controlled haversine test, permanent deformations lead to a new equilibrium neutral position of the beam and the force output follows a sinusoidal waveform. This produces erroneous fatigue results since the test assumptions do not match the actual test conditions. It is recommended to use a sinusoidal waveform to obtain consistent results.

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)0.003
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.324
Threshold uncertainty score0.554

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.019
GPT teacher head0.238
Teacher spread0.219 · 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
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

Citations4
Published2018
Admission routes1
Has abstractyes

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