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Record W4404087127 · doi:10.1617/s11527-024-02495-8

Bulk adhesion of ice to concrete–strength

2024· article· en· W4404087127 on OpenAlexafffund
Anne Barker, Stephen Bruneau, Bruce Colbourne, Austin Bugden

Bibliographic record

VenueMaterials and Structures · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicSmart Materials for Construction
Canadian institutionsNational Research Council CanadaMemorial University of Newfoundland
FundersNational Research Council CanadaNatural Sciences and Engineering Research Council of Canada
KeywordsSolid mechanicsMaterials scienceComposite materialGeotechnical engineeringAdhesionGeology

Abstract

fetched live from OpenAlex

Abstract This paper presents the results of a laboratory test program designed to investigate the adhesive effects of large-scale (bulk) ice on concrete. Medium-strength concrete cylinders were sawn into discs, and attached to a sample table. Freshwater ice samples, frozen using smaller, standard-sized concrete cylinders, were adhered to the concrete with both varying bond times and added weight during bonding. Shear strength tests were conducted at a set displacement rate, under a number of temperatures. The effect of these variables on the adhesive strength of ice to concrete was examined, as well as whether there was any noticeable removal of concrete cement paste or aggregate during testing. The tests indicate that the adhesive strength is negligible when the method of adhesion is “dry” (no liquid layer at the onset of adhesion). Tests with “wet” adhesion indicated a significantly higher strength. The nominal versus the apparent contact area had significant implications for the determination of the adhesive strength of the bond between the ice and the concrete. Removal of cement paste was evident in a number of tests, however the amount was not significant. The results have relevance for design of structures in a marine environment, such as revetement dams or rubblemound breakwaters, as well as for the standardization of adhesion tests with ice and concrete.

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 categoriesInsufficient payload (model declined to judge)
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 score0.040
Threshold uncertainty score0.998

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)0.0030.000

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 head0.219
Teacher spread0.213 · 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.

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

Citations2
Published2024
Admission routes2
Has abstractyes

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