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Record W4289754899 · doi:10.1080/14680629.2022.2106293

Impact of shear stress levels on validity of MSCR tests

2022· article· en· W4289754899 on OpenAlexaff
Gabriel Skronka, Marek Blascik, Otakar Vacin

Bibliographic record

VenueRoad Materials and Pavement Design · 2022
Typearticle
Languageen
FieldEngineering
TopicAsphalt Pavement Performance Evaluation
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsAsphaltRutShear stressMaterials scienceComposite materialShear (geology)Stress (linguistics)Direct shear testGeotechnical engineeringStructural engineeringEngineering

Abstract

fetched live from OpenAlex

Although the MSCR test has become an improvement over the Superpave® |G*|/sinδ parameter, shear stress levels specified in the MSCR test might prove to be too low to successfully represent the stresses occurring in the pavement. To address this hypothesis, five conventional asphalt binders and a total of twelve polymer-modified asphalt blends were tested by MSCR at two different temperatures (50°C and 60°C) as well as five different shear stress levels of 0.1, 3.2, 6.4, 12.8, and 25.6 kPa. The rut results of hot mix asphalts were correlated with the MSCR results. Consequently, better correlations were obtained at higher shear stress levels used in performing MSCR. Moreover, it was shown that MSCR test overestimated the positive effects elasticity on the asphalts’ rut resistance and, thus, more consideration should be given to the asphalt’s ability to resist the applied stresses than to its elastic recovery.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.021
metaresearch head score (Gemma)0.061
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.110

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0210.061
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.083
GPT teacher head0.303
Teacher spread0.220 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations12
Published2022
Admission routes1
Has abstractyes

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