Developing new equations for maximum scour depth near tandem, side-by-side, and eccentric piers
Bibliographic record
Abstract
There is a significant gap in the study of interference effects on side-by-side, tandem, and eccentric piers, which is critical for bridge design. This study investigates how hydraulic parameters affect the maximum scour depth ( d m ). These are pier spacings, flow intensity, flow shallowness, flow skew angle, sediment coarseness, sediment gradation, and time. Three different sets of equations are proposed to determine the d m around side-by-side, tandem, and eccentric piers, considering multiple factors and comparing them with existing literature. The accuracy of the equations is evaluated using statistical parameters such as correlation coefficient ( R), Nash-Sutcliffe efficiency (NSE), normalized root-mean-square-error (NRMSE), and index agreement (IA). The tandem front pier equation shows the highest accuracy with R = 0.91, NSE = 0.83, NRMSE = 0.23, and IA = 0.95. Likewise, the tandem rear pier equation excels in the highest accuracy with R = 0.83, NSE = 0.69, NRMSE = 0.31, and IA = 0.90. This research improves the robustness of scour prediction equations for two-piers by studying influential parameters.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".