Local Scour around Side-by-Side Double Piers in Channel Bends under Ice-Covered Conditions—An Experimental Study
Bibliographic record
Abstract
The pier scour process is normally intensified in the presence of an ice cover, which poses risks to the longevity and safety of bridges. In the present study, the impact of the densimetric Froude number, locations, and pier spacing of side-by-side piers on the local scour depth under ice-covered flow conditions were investigated based on clear water scour experiments in an S-shaped laboratory flume. The results demonstrated that the local scour at piers along the convex bank was more substantial than that along the concave bank when other factors stayed identical. The densimetric Froude number clearly has more impact on local scour at piers along the convex bank than that along the concave bank. Different from the mechanism of the pier scour in a straight channel, the scour depth around a pier along the convex bank in the S-shaped flume increases as the distance between two piers (or pier spacing) increases, while it decreases around the piers along the concave bank. Similar scour patterns were observed when the side-by-side piers were installed at different bend apex cross-sections. The maximum local scour depths at piers along the convex bank measured at different bend apex cross-sections were relatively unchanged when other influencing factors were held constant. However, the maximum scour depth around piers along the concave bank decreased as the bends increased toward downstream.
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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 | 0.000 |
| 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) | 0.001 | 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".