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Performance Evaluation of Joint and Crack Sealants in Cold Climates Using DSR and BBR Tests

2008· article· en· W2142874970 on OpenAlexaffabout
Haithem Soliman, Ahmed Shalaby, Leonnie Kavanagh

Bibliographic record

VenueJournal of Materials in Civil Engineering · 2008
Typearticle
Languageen
FieldEngineering
TopicAsphalt Pavement Performance Evaluation
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsSealantDynamic shear rheometerCreepMaterials scienceRheometerAsphaltStiffnessComposite materialJoint (building)Geotechnical engineeringStructural engineeringEngineeringRutRheology

Abstract

fetched live from OpenAlex

Joint sealants are used widely in Canada to protect pavements from infiltration of water and incompressible materials. Sealants are typically selected based on field studies, which are commonly repeated on a 10-year cycle. This paper examines a laboratory evaluation method based on two laboratory tests that are commonly used for testing asphalt binders: dynamic shear rheometer (DSR) and bending beam rheometer (BBR). Creep stiffness, rate of change in creep stiffness, and rate of change in complex shear modulus with temperature were used to evaluate sealant performance in cold climates. A sealant ranking system was proposed based on the calculation of a sealant index, which combines the proposed evaluation criteria. This method can potentially provide a cost-effective and rapid alternative to field studies. Eight hot-pour sealants were evaluated using this method. Results were verified from an ongoing field study that started in 2004. A good correlation was found between the proposed simplified evaluation method and the existing method.

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)0.002
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 score0.287
Threshold uncertainty score0.523

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.051
GPT teacher head0.266
Teacher spread0.215 · 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

Citations27
Published2008
Admission routes2
Has abstractyes

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