A Case Study on the Analysis and Rehabilitation of an Existing Through Arch Truss Bridge
Bibliographic record
Abstract
The Seal Island Bridge is a steel through-arch truss bridge in Cape Breton Island, Nova Scotia, Canada with a main span of 152 m. At over 60 years in service, the bridge is near the end of its design life and showing structural and operational difficulties such as a previously failed truss diagonal, cracked floorbeams, steel material property complexities, wind-induced vibrations, and restricted access due to narrow deck geometry. A series of bridge inspections were performed which included visual inspections, non-destructive testing, and material testing. The inspections revealed the presence of tack welds and associated cracking, steel corrosion, concrete deterioration, seized bearings, and vibrating bracing elements. Additionally, a structural health monitoring (SHM) program was implemented to determine the current bridge behaviour. To assess the structure, a finite element (FE) model was created and calibrated using the SHM data and the inspection findings. The FE modelling is the focus of this paper. During preliminary analysis, it was determined that the structure was sensitive to wind loading. Therefore, a detailed wind buffeting analysis was performed to refine the wind loading used in the analysis. Based on the results of the analysis and investigations, a rehabilitation plan is currently being developed to ensure that the bridge can remain in service for an additional 15 years. Additionally, a benefit-cost analysis is being performed to assess potential rehabilitation and replacement options for the Owner, the Province of Nova Scotia.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".