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Record W2078703518 · doi:10.3141/1795-04

Analysis of Bituminous Crack Sealants by Physicochemical Methods: Relationship to Field Performance

2002· article· en· W2078703518 on OpenAlexaff
J-F. Masson, Peter Collins, Jim Margeson, Gary Polomark

Bibliographic record

VenueTransportation Research Record Journal of the Transportation Research Board · 2002
Typearticle
Languageen
FieldEngineering
TopicAsphalt Pavement Performance Evaluation
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsMaterials scienceComposite materialDifferential scanning calorimetrySealantAsphaltUltimate tensile strengthViscometerFiller (materials)Viscosity

Abstract

fetched live from OpenAlex

Bituminous crack sealants were analyzed by viscometry, fluorescence microscopy, infrared spectroscopy, thermogravimetry, modulated differential scanning calorimetry, and low-temperature tensile testing. The results indicate that sealants are blends of bitumen, oil, copolymer, and filler. Upon blending, these components produce a three-phase system that consists of a polymer-modified bitumen (PMB) matrix, a filler, and a filler-PMB interface. Spectroscopy and microscopy indicate that the PMB phase is rich in styrene-butadiene copolymer, that the filler is recycled rubber, sometimes mixed with calcium carbonate, and that the interface depends on the filler and the oil content in the sealant. The physicochemical methods were used to predict the short- and medium-term performance of sealant mixtures. The short-term performance predicted from viscometry and microscopy correlated well with the 1-year field performance of the sealants. Sealants showed two glass transition temperatures ( T g ’s), and a reasonable correlation was also found between low T g and medium-term performance in a wet-freeze climate. However, because T g measurements do not account for stress relaxation and aging effects, correlation was not perfect.

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 score0.098
Threshold uncertainty score0.749

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.005
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.122
GPT teacher head0.412
Teacher spread0.290 · 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

Citations32
Published2002
Admission routes1
Has abstractyes

Explore more

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