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Record W4399859748 · doi:10.1139/cjce-2023-0386

Laboratory mix preparation and investigation of mechanical behaviour of polyurethane-bound porous rubber pavement

2024· article· en· W4399859748 on OpenAlexafffundvenue
Tamanna Kabir, Hanaa Khaleel Alwan Al-Bayati, Susan Tighe

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicUrban Stormwater Management Solutions
Canadian institutionsMcMaster UniversityUniversity of Waterloo
FundersMitacs
KeywordsPolyurethaneNatural rubberPorosityComposite materialMaterials scienceElastomerForensic engineeringEngineering

Abstract

fetched live from OpenAlex

With the rise in stormwater runoffs, permeable pavement like polyurethane-bound porous rubber pavement (PRP) offers a viable solution for urban stormwater management. Composed of stone and recycled crumb rubber aggregates bound by polyurethane, PRP features high air voids. This study explores PRP’s mechanical behaviour and freeze–thaw durability in cold climates, essential for sustainable urban infrastructure. We developed methods for sample preparation and air void assessment in PRP and conducted tests on compressive and indirect tensile strength, plus moisture-induced damage. Two scenarios were investigated: one examined four new mixes of varied compositions, and the other focused on mixes with different binders (aliphatic and aromatic). Our investigation reveals insights into PRP’s mechanical behaviour under colder climate conditions. The study shows that higher proportions of stone aggregates and binders enhance mechanical strength, while increased rubber content improves freeze–thaw durability. Furthermore, we found that different binders affect strength variations. The study concludes PRP is effective for low-traffic pavements in colder, freeze–thaw regions.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.491
Threshold uncertainty score0.990

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 head0.008
GPT teacher head0.190
Teacher spread0.182 · 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

Citations5
Published2024
Admission routes3
Has abstractyes

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