Initial assessment of bridge backwater using an artificial neural network approach
Bibliographic record
Abstract
The assessment of backwater resulting from extra energy losses on flood flows caused by bridge constrictions is of vital interest in hydraulic engineering due to its importance in the design of waterways and management of flooding. Although many detailed methods for estimating bridge backwater have been developed, an initial estimate of the magnitude of bridge backwater using a practical model, such as the multiple linear regression (MLR) technique, has a crucial importance for rapid evaluation of flood damages upstream of the bridge structure. In the current study, first, two artificial neural network (ANN) models using the same amount of input data as that of an MLR approach were developed, and then the ability of these ANN models versus the MLR models was investigated for the initial assessment of bridge backwater, both models having been based on the comprehensive laboratory data of the Hydraulic Research Wallingford in UK. The comparison of the results by the MLR and the ANN approaches revealed that the ANN model gave better predictions than those of the MLR model when applied to these laboratory data. United States Geological Survey (USGS) field data were also used for the validation and comparison of these methods. The results showed that ANN approaches yielded more accurate results than those of the MLR models when applied to these field data including actual flood profiles through many bridges.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".