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Record W4308727541 · doi:10.1520/jte20220288

Comparison of Parameters from a New MSCR Approach with Classical MSCR and LAS Parameters for Simplified Binder Selection

2022· article· en· W4308727541 on OpenAlexaff
Aline Vale, Lucas Sassaki Vieira da Silva, Juceline Batista dos Santos Bastos, Lucas Feitosa de Albuquerque Lima Babadopulos, Jorge Barbosa Soares, Hassan Baaj

Bibliographic record

VenueJournal of Testing and Evaluation · 2022
Typearticle
Languageen
FieldEngineering
TopicAsphalt Pavement Performance Evaluation
Canadian institutionsUniversity of Waterloo
FundersConselho Nacional de Desenvolvimento Científico e Tecnológico
KeywordsAsphaltFatigue crackingMaterials scienceDeformation (meteorology)Material selectionRutFracture (geology)Selection (genetic algorithm)CreepCrackingIndex (typography)Stress (linguistics)Composite materialStructural engineeringComputer scienceEngineeringArtificial intelligence

Abstract

fetched live from OpenAlex

ABSTRACT Fatigue and rutting are the most studied pavement distresses. There are different material testing protocols whose compatibilities must be systematically analyzed for proper material selection and performance prediction. They usually focus on either rutting or on the fatigue susceptibility of the materials. In asphalt binders, the idea is to assess material behavior that may relate to the behavior of the corresponding asphalt mixtures in the field. However, a new multiple stress creep recovery (MSCR) approach suggests the use of an index obtained from a modified testing protocol (B-index) that may relate to fatigue. This paper compares the parameters obtained by a new MSCR test methodology, which gives parameters that may also relate to fatigue, with parameters from the standard MSCR test and linear amplitude sweep (LAS) test to analyze both binder permanent deformation and fatigue cracking. This study was conducted with a PG (Performance Grade) 64-28 neat binder and three levels of styrene-butadiene-styrene (SBS) modification by weight of the binder: 2 %, 3 %, and 4 %. The results from the new MSCR protocol indicates that the higher the modifier content, the higher the fatigue resistance based on the B-index. This finding is also supported by the LAS parameters, fatigue area factor of binder (FAFB) and af (fracture length). The study concluded that a preliminary choice based on MSCR’s B-index might overcome time-consuming testing.

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 score0.068
Threshold uncertainty score0.374

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 communication0.0000.000
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 head0.133
GPT teacher head0.332
Teacher spread0.199 · 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

Citations2
Published2022
Admission routes1
Has abstractyes

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