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Record W2289647919 · doi:10.1139/cjce-2017-0058

Rheological characterization of asphalt binders treated with bio sealants for pavement preservation

2018· article· en· W2289647919 on OpenAlexvenueno aff
Debaroti Ghosh, Mugurel Turos, Ed Johnson, Mihai Marasteanu

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2018
Typearticle
Languageen
FieldEngineering
TopicAsphalt Pavement Performance Evaluation
Canadian institutionsnot available
FundersMinnesota Department of TransportationU.S. Department of Transportation
KeywordsSealantRheologyAsphaltRheometerMaterials scienceDynamic shear rheometerComposite materialCrackingGeotechnical engineeringGeology

Abstract

fetched live from OpenAlex

Pavement preservation is playing an increasingly significant role in maintaining our aged pavement infrastructure under severe budget constraints. One important component is the use of surface treatments based on application of sealants. Recently, a number of new products, called bio sealants, have been used to treat aging pavement surfaces. The objective of this study is to investigate rheological properties of the binders treated with these materials to understand the mechanism by which they may improve pavement performance. One plain asphalt binder and four types of sealants, two oil-based sealants, one water-based sealant, and one traditional emulsion were used in the experimental work. The results obtained using a dynamic shear rheometer and a bending beam rheometer were used to determine the changes in rheological properties and the change in performance grade. It was observed that the oil-based sealants have a significant softening effect of the control binder compared to the water-based sealants. The transverse cracking histories from field investigation were used to verify the laboratory findings.

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)0.000
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 score0.239
Threshold uncertainty score0.448

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.019
GPT teacher head0.210
Teacher spread0.191 · 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

Citations8
Published2018
Admission routes1
Has abstractyes

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