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Record W2943808512 · doi:10.33736/jcest.1266.2019

COMPARISON OF TWO ASPHALT MIXTURES USING COMPLEX MODULUS TEST IN LIBYAN WEATHER

2019· article· en· W2943808512 on OpenAlexafffund
Khlifa Saad El atrash, Gabriel J. Assaf

Bibliographic record

VenueJournal of Civil Engineering Science and Technology · 2019
Typearticle
Languageen
FieldEngineering
TopicAsphalt Pavement Performance Evaluation
Canadian institutionsÉcole de Technologie Supérieure
FundersÉcole de technologie supérieure
KeywordsGradationAsphaltDynamic modulusRheologyPhase angle (astronomy)Aggregate (composite)ModulusMaterials scienceCrackingComposite materialAsphalt pavementPhase (matter)Geotechnical engineeringDynamic mechanical analysisGeologyChemistryPolymerComputer science

Abstract

fetched live from OpenAlex

The complex modulus test is dependent on temperature and loading frequency. Thus, the results produced from this test will give a more accurate representation of traffic load effects on asphalt pavement. Laboratory experiments were conducted on two different asphalt mixtures for road research projects (Libya/Roads). All specimens had the same mixtures of aggregate gradation GB-20 incorporated with two different asphalt binders PG70-10 and B (60/70). To obtain the master curve, there were some errors at low temperatures (-25, -10 ºC) and high temperature (54 ºC), so these values were discarded. In addition, 2-complex modulus (CM) and phase angles (Phi) in the test were measured at temperatures of -25, -10, -5, 10, 25, 35, and 54ºC, as well as frequencies of 25, 10, 5, 1, 0.5 and 0.1 Hz. The results displayed the influence of the type of binder on the rheology of the mixtures and gradation on the intensity. Hence, using binder PG 70-10 in Libyan asphalt roads may reduce the binder content, increase the mixture workability, and decrease the thermal cracking. The intrinsic characteristics related to binder properties and weather temperature exhibited the most significant impact on the predicted dynamic modulus.
 Keywords: complex modulus, frequencies, temperatures, sinusoidal, phase angles

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.520
Threshold uncertainty score0.388

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.020
GPT teacher head0.293
Teacher spread0.274 · 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

Citations1
Published2019
Admission routes2
Has abstractyes

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