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Record W4387011872 · doi:10.1139/cjce-2023-0027

Environmental impact on critical responses of lightweight cellular concrete subbase flexible pavements

2023· article· en· W4387011872 on OpenAlexafffundvenueabout
Abimbola Grace Oyeyi, Hanaa Khaleel Alwan Al-Bayati, Frank Mi-Way Ni, Susan Tighe

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2023
Typearticle
Languageen
FieldEngineering
TopicAsphalt Pavement Performance Evaluation
Canadian institutionsMcMaster UniversityUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSubbaseSubgradeGeotechnical engineeringAsphaltAsphalt concreteTensile strainEnvironmental scienceGranular materialRutUltimate tensile strengthAsphalt pavementCompactionStress (linguistics)Materials scienceEngineeringComposite material

Abstract

fetched live from OpenAlex

Previous analytical studies have demonstrated that low-density lightweight cellular concrete (LCC) subbase pavements can support up to 20 times more traffic loads than unbound granular subbase pavements while protecting the pavement subgrade from adverse freeze–thaw effects in cold regions. This study examines the possibility of providing better-performing pavements on the field through the construction, instrumentation, and monitoring of flexible pavement sections incorporating three LCC densities (400, 475, and 600 kg/m³) as subbase material compared with unbound granular material in Canada. The effects of daily and seasonal temperatures on pavement critical responses to stress and strains were evaluated. The findings showed that these LCC pavements reduced asphalt concrete tensile strain by over two times compared with unbound granular pavements, and that strain increased with a daily temperature increase. Daily subgrade pressure (stress) change was reduced by up to 68%. The study concluded that longer life pavements could be achieved with LCC subbase thicknesses ≥250 mm.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.341
Threshold uncertainty score0.778

Codex and Gemma teacher scores by category

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

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 head0.014
GPT teacher head0.238
Teacher spread0.224 · 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

Citations3
Published2023
Admission routes4
Has abstractyes

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