Local scour around bridge abutments in vegetated beds under ice-covered flow conditions – An experimental study and mathematical assessment using machine learning methods
Bibliographic record
Abstract
• The first investigation of local scour at bridge abutments under ice-covered flow in vegetated channel bed. • The cover roughness, abutment shape and vegetation density are critical factors affecting the scour depth. • Using various machine learning techniques, two formulas have been created to accurately predict the maximum scour depth. Local scour around bridge abutments is a critical process that can cause bridge failures, posing substantial environmental risks. Accurate estimation of the maximum scour depth around bridge abutments is crucial for bridge design. In the current study, extensive experiments have been conducted in a large-scale outdoor flume to investigate local scour around the bridge in the presence of vegetation in the channel bed and ice cover on the water surface. Different layout vegetation patterns and densities, water surface cover conditions, abutment shapes, and particle size of bed material are considered. Based on data collected from laboratory experiments, this study employs machine learning methods, including Artificial Neural Networks (ANN), Support Vector Machines (SVM), Multiple Linear Regression (MLR), and Gene Expression Programming (GEP), to predict the maximum scour depth around the abutments, with the GEP model showing the highest accuracy. Results revealed deeper scour with higher Froude numbers, rectangular abutments, and increased ice roughness, while coarser sediments and dense, staggered vegetation reduced scour. Sensitivity analysis using the Partial Mutual Information (PMI) and SHAP (SHapley Additive exPlanations) captured the effects of key variables affecting the maximum scour depth around abutments, including flow Froude number (Fr), the ratio of median grain size of bed material to flow depth (d 50 /H), the standard deviation of sediments (σ g ), the ratio of ice cover roughness coefficient to that of channel bed (n I /n B ), abutment shape factor (K s ), and vegetation roughness density (λ). Two predictive formulas were developed for use in vegetated, ice-covered channels.
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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 | 0.000 | 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".