Case Study of Rochdale Canal Bridge Assessment using Non-Linear Finite Element analysis
Bibliographic record
Abstract
Rochdale Canal Bridge is a single skew span railway under bridge to be assessed under Civils Assessment Framework Agreement 2020-2024. The scope of work is limited to the decks supporting the railway lines. The methodology used to develop a finite element model that accurately represents the structure, the complexities of the structure due to load effects from neighbouring decks etc., are also discussed in the paper. Furthermore, material non-linearity was considered along with geometric non- linearity. The most onerous buckling mode derived from linear Eigen value buckling analysis is used for the initial geometry imperfection of structure. Non-linear analysis was carried out at both SLS and ULS to identify the failure modes through excess stress, strain, yielding and buckling. Through this study, the reserve strength of the structure was captured and is found to be adequate for the current loading which is an improvement from previous assessment. The assessment of these kind of bridges and suggesting strengthening measures etc, will help reduce the environmental impact as compared to the new constructions thereby contributing to sustainability.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".