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Record W4220976863 · doi:10.7250/bjrbe.2022-17.551

Experimental Investigation of High Temperature Behaviour of an Asphalt Binder Modified with Laval University Silica Based on Multiple Stress Creep and Recovery Test

2022· article· en· W4220976863 on OpenAlexaboutno aff
Reza Fallah, Gholamali Shafabakhsh, Zohreh Bahrami

Bibliographic record

VenueThe Baltic Journal of Road and Bridge Engineering · 2022
Typearticle
Languageen
FieldEngineering
TopicAsphalt Pavement Performance Evaluation
Canadian institutionsnot available
Fundersnot available
KeywordsAsphaltDynamic shear rheometerCreepMaterials scienceComposite materialRheometerPenetration testPenetration (warfare)HomogenizerRheologyRutChemistryChromatographyEngineering

Abstract

fetched live from OpenAlex

For experimental investigation of the high temperature behaviour of an asphalt binder modified with Laval university silica (LUS-1) nanostructured particle, four different asphalt binders were produced using a mixture of 2, 4, 6 and 8 wt% of this additive and a neat bitumen at 170 °C. The neat bitumen was yielded from crude oil refining and had a penetration grade of 85–100. After a 20 min vibration, the produced mixtures were mechanically mixed for 30 min in a high-shear homogenizer mixer with an angular velocity of 4500 rpm. Then, the modified binders and neat bitumen were subjected to multiple stress creep and recovery (MSCR) test, after the aging process in rolling thin film oven (RTFOT) test. The results of this study, which were in agreement with the results of the dynamic shear rheometer (DSR) test, indicated that LUS-1 could improve the high temperature behaviour of binders. The greatest improvement occurred using 4 wt% of LUS-1, where this improvement was more pronounced at high stress levels. Elevating the levels of stress and temperature led to diminished traffic grade and more viscous behaviour in asphalt binders modified with LUS-1.

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 score0.267
Threshold uncertainty score0.354

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 head0.009
GPT teacher head0.186
Teacher spread0.177 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueThe Baltic Journal of Road and Bridge EngineeringSame topicAsphalt Pavement Performance EvaluationFrench-language works237,207