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Record W2149514542 · doi:10.1139/cjce-2011-0354

Investigating the resistance of asphaltite containing hot mix asphalts against fatigue and permanent deformation by cyclic tests

2012· article· en· W2149514542 on OpenAlexvenueno aff
Mehmet Yılmaz, Baha Vural Kök, Necati Kuloğlu

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2012
Typearticle
Languageen
FieldEngineering
TopicAsphalt Pavement Performance Evaluation
Canadian institutionsnot available
Fundersnot available
KeywordsStiffnessUltimate tensile strengthCreepComposite materialDeformation (meteorology)Materials scienceAsphaltTensile testing

Abstract

fetched live from OpenAlex

In this study, the aim was to determine the influence of asphaltite addition on the stiffness of the hot mix asphalts as well as on their resistance against fatigue and permanent deformation through cyclic tests. The asphaltite was included as filler into the hot mix asphalt specimens at five different proportions. The cyclic tests were performed on both the short-term aged and the long-term aged specimens. The study demonstrates that the optimal asphaltite content for utilization as filler with respect to volumetric design is 3% by weight. It was inferred from the results of indirect tensile stiffness modulus test that asphaltite increases the stiffness of hot mix asphalts. Similarly, the indirect tensile fatigue tests demonstrate that the use of asphaltite improves the fatigue life of the hot mix asphalts, albeit causing them to exhibit a more brittle behavior. The cyclic creep tests conducted on the specimens point out that the strength against permanent deformation increases by prolonged aging durationş and the mixtures display less elastic behavior with the use of asphaltite.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.198
Threshold uncertainty score0.604

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 communication0.0000.001
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 head0.220
Teacher spread0.203 · 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

Citations8
Published2012
Admission routes1
Has abstractyes

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