Channel deformation, turbulence structure around spur dike, and reduction of local scour at bridge abutments using spur dikes under ice-covered conditions - an experimental study
Bibliographic record
Abstract
,Local scour around bridge abutments and piers presents a significant challenge in hydraulic engineering, threatening the structural integrity of bridges. Scour refers to removing sediment around bridge foundations due to high-velocity flow and turbulence from water passing around these structures. This process can undermine the stability of bridges by exposing and weakening their foundations, potentially leading to failures and catastrophic collapses. Several factors contribute to scour, including water flow characteristics, surface conditions, hydraulic structure features, and riverbed geomorphology. Effective mitigation of scour is essential to ensure bridge safety and longevity. Traditional methods, such as riprap, concrete aprons, and various hydraulic structures, aim to alter flow patterns and reduce erosive forces. However, these methods can be constrained by environmental conditions and site-specific characteristics. This research explores using spur dikes, hydraulic structures extending from the riverbank to redirect the flow, to mitigate local scour at bridge abutments, especially under ice cover conditions. The study utilizes a large-scale outdoor hydraulic flume at the Quesnel River Research Center in British Columbia, Canada. The flume measures 38.5 meters in length, 2 meters in width, and 1.3 meters in depth, with a longitudinal bed slope of 0.2% to replicate natural flow conditions with non-uniform flow characterized by longitudinal variations in water depth. Two sandboxes are filled with natural sediments of different median grain sizes (0.48 mm, 0.60 mm, and 0.90 mm) to replicate riverbed conditions. Spur dikes made from marine plywood were positioned upstream of the abutment (25 cm and 50 cm) and at different alignment angles (45º, 60º, 90º) in the flume. Abutments constructed from galvanized plates were installed in the sandboxes. Styrofoam panels simulated smooth and rough ice cover conditions, with smooth panels representing natural sheet ice and rough panels mimicking ice jams through attached Styrofoam cubes. Flow rate and water depth were measured using a SonTek-IQ Plus, an advanced instrument with six sensors for comprehensive flow field coverage and high-accuracy data collection. Acoustic Doppler Velocimetry (ADV) captured detailed 3D velocity components and turbulence intensities, measuring the velocity of scattering particles in the flow to provide insights into complex flow dynamics around the spur dikes and abutments. This experimental study aims to enhance understanding of scour dynamics by investigating the interactions between different spur dike configurations, flow conditions, and ice cover types. It provides detailed insights into how these factors influence local scour and sediment transport processes. Additionally, the study seeks a comprehensive understanding of the flow field and 3D velocity distribution around spur dikes under various conditions, analyzing the effects of different alignment angles and ice cover on flow patterns and turbulence structure, which are critical for predicting and mitigating scour. Another goal is to develop effective scour mitigation strategies, identifying optimal configurations that provide maximum protection under various hydraulic and environmental conditions. Overall, the combined studies aim to advance the field of hydraulic engineering by offering practical solutions for mitigating scour-related risks, thereby ensuring the stability and safety of bridge abutments in diverse hydraulic environments.
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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".