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Record W186929899

Performance of Permeable Pavements Under Low Volume Traffic Loads

2015· article· en· W186929899 on OpenAlexaboutno aff
Kerwin G Modeste, Lynne Cowe Falls, Peter Y. Park

Bibliographic record

VenueTransportation Research Board 94th Annual MeetingTransportation Research Board · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicUrban Stormwater Management Solutions
Canadian institutionsnot available
Fundersnot available
KeywordsSubgradeGeotechnical engineeringPervious concreteTraffic volumeCompactionFrost (temperature)Stress (linguistics)Frost heavingEnvironmental scienceAsphalt pavementFrost weatheringGeologyAsphaltEngineeringMaterials scienceComposite materialSoil waterCement
DOInot available

Abstract

fetched live from OpenAlex

Most research on permeable pavements focused on the hydrological performance and was done primarily in mild climates, with little information available for the pavement structural response in cold climates. Hence, this research was commissioned in order to evaluate the performance of permeable pavements under low volume traffic loads in Calgary. The research facility located at the Currie Barracks in Southwest Calgary was divided into three individual cells (cell No.1 to 3) with pervious concrete pavement (PCP), permeable interlocking concrete pavement (PICP), and porous asphalt concrete pavement (PACP) as the wearing course, respectively. The highest and lowest stress response on the subgrade was recorded in the PACP and the PCP, respectively; with the exception of the frozen condition where the stress response for the PICP was slightly lower than the stress response for the PCP. The maximum stress response on the subgrade was recorded at the lowest speed of 5 kmph; while the stress response on the subgrade generally decreased with an increase in the traffic speed. The stress response was highest when the pavement structure was unsaturated; and lowest when the pavement structure was frozen. The findings indicated that the PCP showed the best structural performance with the exception of some ravelling. The impact of the frost heave was highest in the PICP. There was a significant increase in the surface deformation in the PICP subsequent to truck traffic for the flooded pavement structure.

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)0.007
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.228
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.003
Science and technology studies0.0010.002
Scholarly communication0.0000.002
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.002

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 head0.066
GPT teacher head0.332
Teacher spread0.266 · 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; both teacher heads agree on what is shown here.

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

Citations2
Published2015
Admission routes1
Has abstractyes

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