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Record W2004963275 · doi:10.3141/1766-03

Sample Preparation for Direct Tension Testing: Improving Determination of Asphalt Binder Failure Stress and Test Repeatability

2001· article· en· W2004963275 on OpenAlexafffund
S Ho, Ludo Zanzotto

Bibliographic record

VenueTransportation Research Record Journal of the Transportation Research Board · 2001
Typearticle
Languageen
FieldEngineering
TopicAsphalt Pavement Performance Evaluation
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Calgary
KeywordsAsphaltTension (geology)Materials scienceStress (linguistics)Composite materialCeramicSample (material)RepeatabilityForensic engineeringEngineeringUltimate tensile strengthChemistryChromatography

Abstract

fetched live from OpenAlex

During Superpave direct tension testing (DTT) of asphalt binders, the failure stress and failure strain values were found to depend strongly on the molecules of asphalt in the DTT mold forming a stable network. An asphalt specimen in which molecules have formed a network with aligned dipole moments has a higher DTT failure stress value. A sample preparation method based on this theory was developed. In this method, ceramic tiles holding the DTT molds were heated in an oven with controlled temperatures specific to the performance grade (PG) of the binder. These heated ceramic tiles provided a means for the poured asphalt to stay fluid and cool slowly and uniformly. The theory, detailed method development, and sample preparation procedure are described. With this method, the DTT failure stress of asphalt binder is higher than in samples prepared without controlled cooling. The variability of failure stress and failure strain values of the six specimens within the same run is usually less than 15 percent for the softer asphalt, for example, PG52–34, and somewhat higher for the harder asphalt, for example, PG64–28. The average results of the best four out of six specimens usually agreed with another run within 10 percent, regardless of asphalt type. Important considerations during preparation of DTT samples are discussed. The incorporation of these details for the sample preparation method in the DTT procedure will lead to better agreement of direct tension results among different laboratories.

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)0.005
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.055
Threshold uncertainty score0.734

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.090
GPT teacher head0.374
Teacher spread0.283 · 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 designObservational
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

Citations19
Published2001
Admission routes2
Has abstractyes

Explore more

Same venueTransportation Research Record Journal of the Transportation Research BoardSame topicAsphalt Pavement Performance EvaluationFrench-language works237,207