Iowa's Proactive Approach to Bridge Scour Monitoring
Bibliographic record
Abstract
Scour, which is the result of the erosive action of flowing water excavating and carrying away material from the bed and banks of streams and from around the piers and abutments of bridges, is the most common cause of bridge failure. The structural instability and undermining caused by scouring are affected by factors such as channel and bridge geometry, floodplain characteristics, flow hydraulics, bed material, channel protection and stability, riprap placement, and ice formation and debris. Directing its attention to the scour problem, the Federal Highway Administration issued a Technical Advisory in 1988 and again in 1991, revisiting the National Bridge Inspection Standards to require evaluation of all bridges for susceptibility to damage resulting from scour. Of special concern were scour-critical bridges, or those bridges that could experience catastrophic failure or become structurally unstable as a result of excessive scour caused by a destructive flood event. Over the past 4 years, the Iowa Department of Transportation has devoted significant time to evaluating the bridges under its jurisdiction and assigning scour-classification codes to them. This article discusses the scour evaluation of Iowa's 2,100 waterway bridges, 180 of which have been classified as scour-critical, and the scheduled hydraulic and structural construction countermeasures being taken to prevent their catastrophic failure.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".