Adhesion of Ice to Concrete: Bonds and their Influence on Abrasion Mechanisms
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".