Validation of Some Bridge Pier Scour Formulae and Models Using Field Data
Bibliographic record
Abstract
Estimation of maximum local scour at the pier site is necessary for safety and economy of the designed bridge. Numerous formulae are available for predicting maximum local scour depth at the pier site. Almost all of these formulae were developed based on laboratory data. Validation of these formulae using field data is necessary to recommend the formulae with a reasonable estimation. In this study, four formulae were selected for validation process using field data recorded from bridges subjected to scour in Pakistan, Canada and India and the data obtained from Qadar [7]. The selected formulae which were used for validation process had been proposed by Colorado State University (CSU), Melville and Sutherland, Jain and Fisher, and Lauren and Toch. Three statistical tests were carried out to determine the formulae equation with minimum prediction errors. They were mean absolute error (MAE), root mean square error (RMSE), and Theil’s Coefficient (TC). The statistical tests showed that error in the prediction of maximum local scour depth using CSU formula was minimum compared to the errors in the prediction of the other three formulae. For CSU formula, the computed values of MAE, RMSE and TC were found to be 0.11, 0.93 and 1.24 respectively.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 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".