Case study using Non-Linear Finite Element Analysis for Assessment of Slutchers Lane bridge
Bibliographic record
Abstract
200 years of British railway infrastructure is now owned, managed, and developed by Network Rail. Safe and effective management of the rail network needs to have up-to-date information regarding the capacities of these ageing assets. Slutchers Lane bridge (Warrington, UK) is a structure designed to carry rail loads over a public road. According to the 2010 NBSI assessment and the archive drawings, bridge widening took place circa 1907. The previous assessment of this bridge was carried out using simple statics with an idealised line beam approach, the results obtained were inadequate to serve the current load requirements of Network Rail. To get a more accurate theoretical capacity, a more refined analysis was required, incorporating recent site inspection data representing the current condition of the bridge. Performing a nonlinear finite element analysis was found to be suitable for this requirement. For the study, the entire structure was modelled in LUSAS software using shell elements and analysed for various potential failure mechanisms. The finite element model was able to capture all the deteriorations identified during the recent site investigation. A mesh sensitivity study was carried out for the selection of an appropriate mesh size. In the analysis, material nonlinearity is considered along with geometric nonlinearity. The initial geometric imperfection is assigned based on the critical buckling mode identified in the linear Eigenvalue buckling analysis with appropriate scale factor. To determine the results at both ultimate limit state and service limit state, the following outputs were captured, Von Mises stresses, the extent of material yielding, and the propensity for buckling. Recommendations were made based on standard guidance and engineering judgement. From the nonlinear finite element study, it was established that the structure in its current condition is adequate for the current Network rail requirement, which is an improvement on the results of the previous assessment.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 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".