Analysis of Local Scour around Double Piers in Tandem Arrangement in an S-Shaped Channel under Ice-Jammed Flow Conditions
Bibliographic record
Abstract
The stability of bridge foundations is affected by local scour, and the formation of ice jams exacerbates local scour around bridge piers. These processes, particularly the evolution of ice jams and local scour around piers, are more complex in curved sections than in straight sections. This study, based on experiments in an S-shaped channel, investigates how various factors—the flow Froude number, ice–water discharge rate, median particle diameter, pier spacing, and pier diameter—affect the maximum local scour depth around double piers in tandem and the distribution of ice jam thickness. The results indicate that under ice-jammed flow conditions, the maximum local scour depth around double piers in tandem is positively correlated with the ice–water discharge rate, pier spacing, and pier diameter and negatively correlated with median particle diameter. The maximum local scour depth is positively correlated with the flow Froude number when it ranges from 0.1 to 0.114, peaking at 0.114. Above this value, the correlation becomes negative. In curved channels, the arrangement of double piers in tandem substantially influences ice jam thickness distribution, with increases in pier diameter and spacing directly correlating with greater ice jam thickness at each cross-section. Furthermore, ice jam thickness is responsive to flow conditions, escalating with higher ice–water discharge rates and decreasing flow Froude numbers.
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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 | 0.000 |
| 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".