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Record W2044738280 · doi:10.1139/l09-066

Triaxial frequency sweep characterization of Saskatchewan hot mix asphaltic concrete across asphalt cement binder type

2009· article· en· W2044738280 on OpenAlexaffvenueabout
Curtis Berthelot, Diana Podborochynski, Colin Wandzura, Colin Prang, Brent Marjerison

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2009
Typearticle
Languageen
FieldEngineering
TopicAsphalt Pavement Performance Evaluation
Canadian institutionsUniversity of SaskatchewanSaskatoon City HospitalSaskatchewan Ministry of Agriculture
Fundersnot available
KeywordsAsphaltAsphalt pavementGeotechnical engineeringAsphalt concreteCementSweep frequency response analysisChristian ministryMaterials scienceWearing courseEnvironmental scienceComposite materialEngineering

Abstract

fetched live from OpenAlex

The purpose of this research was to determine how asphalt cement binder types influence the mechanical behaviour of hot mix asphalt concrete mixes. This research employed triaxial frequency sweep characterization of a typical City of Saskatoon Hot Mix Type A2 and a typical Saskatchewan Ministry of Highways and Infrastructure Hot Mix Type 70 across a range of triaxial load frequencies and stress states representative of Saskatchewan field state conditions. The asphaltic mixes were evaluated across four asphalt cement binder types typically employed by Saskatchewan road agencies. Based on the findings of this study, the mechanical behaviour of both asphalt mixes evaluated were found to be highly sensitive to load rate, stress state, as well as asphaltic binder type. It was concluded that load rate, field stress state, and asphalt binder type should be incorporated into the structural design of hot mix asphalt concrete pavements to ensure adequate mix performance, particularly when placed in severe field conditions.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.468
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.013
GPT teacher head0.224
Teacher spread0.211 · 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.

Study designBench or experimental
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

Citations3
Published2009
Admission routes3
Has abstractyes

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