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Record W4238306713 · doi:10.1177/0361198105193300112

Variations in Composition and Rheology of Bituminous Crack Sealants for Pavement Maintenance

2005· article· en· W4238306713 on OpenAlexaff
J-F. Masson, Peter Collins, S. Bundalo-Perc, John R. Woods, Imad L. Al‐Qadi

Bibliographic record

VenueTransportation Research Record Journal of the Transportation Research Board · 2005
Typearticle
Languageen
FieldEngineering
TopicAsphalt Pavement Performance Evaluation
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsSealantAsphaltRheologyDurabilityMaterials scienceComposite materialForensic engineeringEngineering

Abstract

fetched live from OpenAlex

Bituminous crack sealants are used for the preventive maintenance of asphalt concrete pavements. The selection of a durable sealant can be difficult, however, mainly because of the lack of correlation between standard sealant specifications and field performance. Hence, an approved list of materials based on past performance is sometimes used to select sealants. However, sealant durability and performance vary over time. To investigate the effect of sealant lot variation on sealant properties, six lots of two sealants from different suppliers were analyzed for filler and polymer contents and rheological response. It was found that the difference in composition and rheology between lots can be similar to that between sealants produced by different manufacturers. Hence, sealant lot-to-lot variation can partly explain the variation in the field performance of sealants. Therefore, lists of approved products drawn from the field performance of past years are ineffective in the selection of sealants for future maintenance. The application of segregated sealants was also investigated, including assessing the effect of melter stirring on sealant homogeneity and measuring the segregation of sealant upon cooling. It was found that sealants do not segregate after their application and subsequent cooling and that a rapid circumferential stirring of 25 revolutions per minute in the heating kettle allowed for the remixing of a segregated sealant.

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 score0.261
Threshold uncertainty score0.765

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.068
GPT teacher head0.368
Teacher spread0.300 · 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

Citations6
Published2005
Admission routes1
Has abstractyes

Explore more

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