Estimating live-bed local scour around bridge piers in cohesionless sediments: applicability and bias of selected models
Bibliographic record
Abstract
To design the foundation system of waterway bridges, Load and Resistance Factor Design guidelines suggest use of deterministic scour depth prediction models. Understanding the inherent bias of deterministic scour depth prediction models will advance the development of reliability index-based foundation design regime. Four bridge scour depth prediction models were assessed in terms of two statistical parameters, termed herein mean absolute percentage error (MAPE), and conservatism, percentage of cases the predicted scour depth exceeded the measured scour depth. Live-bed laboratory and field scour depth databases were used in analyses to quantify model scatter by comparatively assessing the computed scour depth versus measured data. For live-bed laboratory data, values of MAPE ranged from 23.5% to 59.8%, whereas conservatism ranged from 28.4% to 97.8%. For live-bed field data, conservatism varied from 93.3% to 95.1%, while MAPE ranged from 205.6% to 319%. Statistical models were applied to ascertain the biasness of the four deterministic models. Accuracy and conservatism of a given model were consequently adjusted through proposed modification factors. The proposed approach allows for the selection of a suitable modification factor to satisfy a target probability of deceedance or a target conservatism.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".