Local scour around bridge abutments protected by angled spur dikes under ice-covered flow conditions
Bibliographic record
Abstract
Scouring around bridge piers and abutments presents a critical threat to bridge stability, necessitating effective mitigation strategies. Based on laboratory experiments in a large-scale flume which is 2.0-m wide and 38.5-m long, this study investigates the impact of non-submerged spur dikes with varying alignment angles of 45°, 60°, and 90° under different ice cover conditions on reducing local scour around bridge abutments located downstream of spur dikes. Results of this study reveal that the spur dike with an alignment angle of 90° relative to the downstream direction positioned 25 cm upstream of the abutment can effectively prevent the local scour around bridge abutment by redirecting flow and reducing vortex-induced erosion. The maximum depth of scour holes around bridge abutments increases with the increase in flow Froude number, relative roughness of ice cover and the dike alignment angle. On the other hand, the maximum depth of the scour hole decreases with the increase in the particle size of the bed material and the effective length of the dike. An empirical formula has been developed to determine the maximum depth of scour holes around bridge abutments, which are protected by spur dikes located upstream of the abutments. Clearly, the effective mitigation of local scour around bridge abutments varied with flow condition, ice cover roughness, particle size of bed material, alignment angle and the distance from the abutment to the spur dike located upstream of the abutments. • First evaluation of local scour around bridge abutments with spur dikes at varying angles under different ice-covered flow conditions. • Cover roughness, dike angle, and dike-abutment spacing significantly impact channel bed deformation around the bridge abutment. • An empirical formula is derived to estimate max scour depth at abutments, considering dike length, bed material, and flow Froude number.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.002 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".