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Record W3190523752

Adhesion of Ice to Concrete: Bonds and their Influence on Abrasion Mechanisms

2019· article· en· W3190523752 on OpenAlexvenueno aff
Anne Barker, Stephen Bruneau, Bruce Colbourne

Bibliographic record

VenueNPARC · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicSmart Materials for Construction
Canadian institutionsnot available
Fundersnot available
KeywordsAbrasion (mechanical)AdhesionBondMaterials scienceComposite materialForensic engineeringBusinessEngineering
DOInot available

Abstract

fetched live from OpenAlex

Maintenance and repair of ice-worn concrete structures in marine environments are ongoing challenges. Practical solutions for reducing ice-wear for large-scale applications have had marginal success rates to date. We still do not really know the relative degrees of abrasion caused by mechanical wear, freeze-thaw cycling, pore water pressure, or seawater chemical effects. What is happening at the interface between ice and concrete and is there a link between wear and adhesion processes? Many studies have examined ice and concrete adhesion: twist, push and pull tests on concrete piles frozen into ice; direct shear tests of ice on concrete; and investigations into the frictional wear of concrete by ice. How do contact mechanics influence the shear and tensile adhesion bonds between these two substances? This paper outlines a programme that seeks to inform not only our knowledge of adhesion loading, but also how adhesion may influence the initial high rate of wear that is common to ice abrasion of concrete. The presented test programme is using a suite of methodologies to examine ice-concrete adhesion. Three approaches are outlined, with an eye towards answering the questions above, for a comprehensive evaluation of ice-concrete adhesion bonding.

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 score0.098
Threshold uncertainty score1.000

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)0.0010.001

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 head0.005
GPT teacher head0.199
Teacher spread0.193 · 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

Citations1
Published2019
Admission routes1
Has abstractyes

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