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Record W296857462 · doi:10.33593/iccp.v10i1.363

Performance of Pervious Concrete Pavement in Freeze-Thaw Conditions and With Winter Maintenance

2025· article· en· W296857462 on OpenAlexaboutno aff
Vimy Henderson, Susan Tighe, Jacques Bertrand

Bibliographic record

VenueProceedings of the International Conference on Concrete Pavements · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicEcology and Conservation Studies
Canadian institutionsnot available
Fundersnot available
KeywordsPervious concreteGeotechnical engineeringEnvironmental scienceEngineeringMaterials scienceComposite material

Abstract

fetched live from OpenAlex

In 2007 the Cement Association of Canada, industry members and the Centre for Pavement and Transportation Technology at the University of Waterloo partnered to carry out a study to evaluate the behavior of pervious concrete pavement in the Canadian climate. Field sites have been constructed and monitored and laboratory testing has also been carried out on pervious concrete pavement. This paper focuses on the behavior that has been demonstrated by pervious concrete in the laboratory when exposed to two factors: freeze-thaw cycling and moisture; and winter maintenance. The behavior was assessed in terms of permeability and surface distress development. Freeze-thaw cycling and moisture proved to on occasion alter the interior structure of the pervious concrete pavement. The extent to which this occurs is deemed to be related to the paste characteristics. Salt solution as a winter maintenance technique was found to be very damaging to the pervious concrete samples. Sand as a winter maintenance method led to minimal surface distress development. Permeability was decreased with the use of sand and salt solutions as winter maintenance techniques; however, remained adequate with both. This laboratory research presents results that should be applied to field sites for verification in full scale scenarios. In general the presence of freeze-thaw cycles, moisture and winter maintenance did not lead to poor performance of the pervious concrete pavement in the laboratory.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.112
Threshold uncertainty score0.172

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 head0.023
GPT teacher head0.239
Teacher spread0.216 · 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 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

Citations0
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the International Conference on Concrete PavementsSame topicEcology and Conservation StudiesFrench-language works237,207