Structural Capacity Analysis of Corroded Steel Girder Bridges
Bibliographic record
Abstract
More than 9% of the bridges in the United States were labeled structurally deficient according to the 2017 American Society of Civil Engineers' infrastructure report card. The main causes of bridge deterioration are repeated vehicular loads and adverse environmental exposure. The most dominant deterioration form for steel bridges is corrosion, which is characterized by the loss of metal area resulting in reduction of structural capacity. Corrosion in steel multi-girder bridges is common in cold regions because of the frequent use of deicing chemicals during the winter season as well as leakage caused by bridge joint damage. At times, the rust is serious enough to disconnect the web from the flanges of the girder. This poses significant concerns for load capacity especially at girder ends. The consequences of bridge failure can be disastrous. This research investigates the structural capacity of these corroded steel girders. The mechanical behaviors of deteriorated girders are studied by 3-D finite element models built in ABAQUS and by lab testing. Our analysis is focused on web area loss and web thinning due to corrosion, and their consequences for load capacity reduction. The effects of location, size, and shape of area loss on shear and web buckling resistance will be studied. Lab tests on steel girder models will be conducted to verify the results from finite element modeling. Based on our analysis and findings, a simple and dependable rating method to evaluate deteriorated steel girder bridges will be developed.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".