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Record W2743606431 · doi:10.1139/cjce-2017-0155

Multi-scale characterization of hydrated lime mastics

2017· article· en· W2743606431 on OpenAlexvenueno aff
Aboelkasim Diab, Zhanping You, Yang Xu, Amr M. Wahaballa

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2017
Typearticle
Languageen
FieldEngineering
TopicAsphalt Pavement Performance Evaluation
Canadian institutionsnot available
Fundersnot available
KeywordsViscoelasticityMaterials scienceAsphaltCrackingCreepComposite materialStiffnessViscosityShear (geology)Shear stressStress (linguistics)

Abstract

fetched live from OpenAlex

This study is devoted to characterize the behavior of the hydrated lime-asphalt binder mastic (HLM) using the viscosity characteristics (relative viscosity versus shear rate profile) and viscoelastic properties. In addition, the multiple stress creep recovery, linear amplitude sweep, and asphalt binder cracking device tests were also performed, for the sake of characterization of permanent deformation, fatigue cracking, and low temperature cracking, respectively. The large amplitude oscillatory shear test was utilized to characterize the nonlinear behavior of HLMs using Lissajous-Bowditch plots and associated local viscoelastic measures (minimum and large strain complex shear moduli). Overall, the HLM is able to improve the stiffness-related properties of pavement; on the other hand, this mastic can help resist load and non-load associated cracking. In addition, the nonlinear behavior revealed higher stress response of the HLMs compared to base binder even after increased number of cycles which could be an indicator of the ability to sustain high stresses and strains.

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)0.000
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 score0.455
Threshold uncertainty score0.492

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.016
GPT teacher head0.209
Teacher spread0.193 · 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

Citations5
Published2017
Admission routes1
Has abstractyes

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