Use of spur dikes with different permeability levels for protecting bridge abutment against local scour under unsteady flow conditions
Bibliographic record
Abstract
Local scour around abutments is one of the most important reasons for a bridge collapse, making it a major concern for hydraulic and river engineers. Available studies on scour control around abutments have been limited to steady flow conditions. In this light, the present experimental study investigates using a single spur dike with different levels of permeability (0%, 35%, 50%, and 65%) to reduce scour around a short vertical-wall abutment (abutment length/flow depth ≤ 1) under unsteady flow conditions. The hydrographs with Gaussian distribution and different base flow times (i.e., duration of 15, 30, 60, and 90 min) were used to simulate the unsteady flow. Abutment scour depth variations with time showed that the final scour depth always appeared after the peak discharge of the hydrograph regardless of whether a spur dike was used. It was shown that the spur dike has successfully reduced local scour around the abutment. The maximum depth of scour hole around abutment was reduced by 47%, 32%, and 11%, when spur dikes with 35%, 50%, and 65% permeability were, respectively, used. Using an impermeable spur dike not only prevented the scour upstream nose of the abutment but also led to some deposition that raised the bed level by nearly 0.47 La (where La is the abutment's length) in that area. However, the maximum depth of scour hole around the impermeable spur dike was nearly identical to that occurred around the abutment for the experiments without a spur dike, making it necessary to arrange for scour protection around the spur dike.
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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".