Numerical Modeling of Local Scour in the Vicinity of Bridge Abutments When Covered with Ice
Bibliographic record
Abstract
The occurrence of bridge collapse is frequently attributed to the prevalent erosion in the vicinity of bridge abutments. Accurate determination of the maximum scour depth in the vicinity of bridge abutments is imperative to ensure a secure and reliable bridge design. The phenomenon of local scour at bridge abutments can exhibit significant variations when compared to open-flow conditions, primarily due to the additional obstacle posed by the presence of ice cover. Research has demonstrated that the erosion of bridge abutments is more pronounced in the presence of ice. The vertical velocity distribution has a direct impact on both the bed shear stress and the resulting scour geometry in ice-covered conditions. This study aims to analyze the effects of flow through open channels and covered flow conditions on the local scour process around semi-circular and square bridge abutments using FLOW-3D V 11.2 software. The utilization of the volume of fluid (VOF) approach is employed for monitoring the free surface, while the Reynolds averaged Navier-Stokes (RANS) equations and the RNG k turbulence model are employed for simulating the flow field in the vicinity of bridge abutments. The sediment transport equations formulated by Meyer-Peter and Müller were employed for the purpose of simulating the movement of sediment particles. The numerical simulation results are compared with the experimental results. The result shows that the presence of ice cover and its roughness can increase the maximum scour depth both in numerical and experimental studies. The results also indicate that the maximum scour depth is located at the upstream section of the bridge abutments. These findings demonstrate the ability of the numerical model to predict the occurrence of local scour in the vicinity of bridge abutments under conditions of ice presence.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".