Impact of armour layer on the depth of scour hole around side-by-side bridge piers under ice-covered flow condition
Bibliographic record
Abstract
Abstract In the present study, experiments were conducted in a large-scale flume to investigate the issue of local scour around side-by-side bridge piers under both ice-covered and open flow conditions. Three non-uniform sediments were used in this experimental study. Analysis of armour layer in the scour holes around bridge piers was performed to inspect the grain size distribution curves and to study the impact of armour layer on scour depth. Assessments of grain size of deposition ridges at the downstream side of bridge piers have been conducted. Based on data collected in 108 experiments, the independent variables associated with maximum scour depth were assessed. Results indicate that the densi-metric Froude number was the most influential parameter on the maximum scour depth. With the increase in grain size of the armour layer, ice cover roughness and the densimetric Froude number, the maximum scour depth around bridge piers increases correspondingly. Equations have been developed to determine the maximum scour depth around bridge piers under both open flow and ice covered conditions.
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 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".