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Record W4239882989 · doi:10.3141/1958-09

Viscosity Determination of Hot-Poured Bituminous Sealants

2006· article· en· W4239882989 on OpenAlexaff
Eli Fini, Mostafa A. Elseifi, J.-F. Masson, Kevin K. McGhee

Bibliographic record

VenueTransportation Research Record Journal of the Transportation Research Board · 2006
Typearticle
Languageen
FieldEngineering
TopicAsphalt Pavement Performance Evaluation
Canadian institutionsNational Research Council Canada
FundersFederal Highway Administration
KeywordsSealantAsphaltViscosityMaterials scienceForensic engineeringComposite materialGeotechnical engineeringEngineering

Abstract

fetched live from OpenAlex

Hot-poured bituminous sealants are typically selected on the basis of empirical standard tests such as penetration, resilience, flow, and bond to cement concrete briquettes (ASTM D3405). Yet there is no indication of the pertinence of these standard tests to predict field performance. To bridge the gap between sealant fundamental properties and field performance, performance-based guidelines for selection of hot-poured crack sealants are currently being developed. A procedure to measure sealant viscosity is proposed as part of that effort. Using a sealant with an appropriate consistency at the recommended installation temperature would provide a better crack filling and would ensure appropriate bond strength. Therefore, to ensure that sealant–crack wall adhesion is achieved and that the sealant penetrates hot-mix asphalt during installation, a testing procedure for bituminous-based crack sealant viscosity at installation temperature is suggested. This paper proposes use of a rotational viscometer to measure viscosity of hot-poured crack sealant materials. From results of this study, the measured viscosity of hot-poured crack sealant using a SC4-27 spindle at 60 rpm at the recommended installation temperature is reasonably representative of sealant viscosity at shear rates resembling field application. To ensure measurement consistency and stability, a 20-min melting time and 30-s waiting time before data collection are recommended. Repeatability of measurements was acceptable, with an average coefficient of variation of less than 5%. Variability between operators and variability between sealant samples were acceptable.

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)0.004
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.232
Threshold uncertainty score0.986

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.062
GPT teacher head0.357
Teacher spread0.295 · 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

Citations5
Published2006
Admission routes1
Has abstractyes

Explore more

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