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Record W4237215945 · doi:10.32920/ryerson.14652480.v1

Salt Scaling of Pervious Concrete: Optimizing a Test Method and The Effects of Different Deicers

2021· preprint· en· W4237215945 on OpenAlexafffund
Chehong Tsang

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicSmart Materials for Construction
Canadian institutionsToronto Metropolitan University
FundersGovernment of Ontario
KeywordsPervious concreteScalingSalt (chemistry)Aggregate (composite)CylinderGeotechnical engineeringMaterials scienceChemistryComposite materialEngineeringMathematicsGeometry

Abstract

fetched live from OpenAlex

There is currently no standard test method for evaluating the resistance of pervious concrete to salt scaling. In this study, a number of test methods were evaluated to determine an effective test method. A number of parameters were varied, including sample geometry, cycle length, level of submersion, and the effects of various deicers. The optimum cycle length was found to be 24 hours with the submersion of the bottom 10 mm of the pervious concrete samples made with 14 mm aggregate. Cylinder samples with a diameter of 100 mm and height of 150 mm are recommended as they provide consistent results. Using these samples, a mass loss failure criterion of 3 to 5% is proposed. The deicers that caused the most damage are NaCl, CaCl2, and urea, followed by MgCl2, K acetate, Na acetate, and CMA. More testing is needed to validate the effects of different deicers. i

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.045
Threshold uncertainty score0.519

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.006
GPT teacher head0.228
Teacher spread0.222 · 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 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

Citations0
Published2021
Admission routes2
Has abstractyes

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