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Record W630703795

Rheological Testing of Asphalt Cements Recovered from an Ontario Pavement Trial

2009· article· en· W630703795 on OpenAlexaboutno aff
Simon A.M. Hesp, Irsan Kodrat, D.W. Scafe, Abdolrasoul Soleimani, Sathish Subramani, L Whitelaw

Bibliographic record

VenueSixth International Conference on Maintenance and Rehabilitation of Pavements and Technological Control (MAIREPAV6)International Society for Maintenance and Rehabilitation of Transportation InfrastructureTransportation Research Board · 2009
Typearticle
Languageen
FieldEngineering
TopicAsphalt Pavement Performance Evaluation
Canadian institutionsnot available
Fundersnot available
KeywordsAsphaltRheologyCementCrackingRheometerMaterials scienceComposite materialEnvironmental scienceAsphalt pavement
DOInot available

Abstract

fetched live from OpenAlex

This paper describes how seven asphalt cements were recovered from field cores taken from a five-year-old, northeastern Ontario pavement tests. The original laboratory-aged materials all graded in a narrow range between –35oC and –36oC, according to the current bending beam rheometer (BBR) protocol. However, their low temperature performance showed early and significant variability after the pavement was exposed to a cold spell in early 2004. Two of the test sections showed virtually no distress while the remaining five were cracked to significant degrees. Conventional rheological characterization revealed important differences between laboratory-aged and recovered materials, but the results could only partially explain the observed performance ranking. Additional testing after low temperature conditioning assisted in clearing up the most important inconsistencies. It was found that premature and excessive cracking is associated with the presence of problematic additives in the asphalt cement (e.g., waste engine oils, air blowing catalysts, acids, and the like).

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.189
Threshold uncertainty score0.376

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.031
GPT teacher head0.313
Teacher spread0.282 · 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 source (direct Gemma or distilled Codex), 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

Citations9
Published2009
Admission routes1
Has abstractyes

Explore more

Same venueSixth International Conference on Maintenance and Rehabilitation of Pavements and Technological Control (MAIREPAV6)International Society for Maintenance and Rehabilitation of Transportation InfrastructureTransportation Research BoardSame topicAsphalt Pavement Performance EvaluationFrench-language works237,207