Bulk adhesion of ice to concrete–strength
Bibliographic record
Abstract
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.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".