Assessment of the Strengthening of an RC Railway Bridge with CFRP Utilizing a Full-Scale Failure Test and Finite-Element Analysis
Bibliographic record
Abstract
A finite element (FE) model was calibrated using the data obtained from a full-scale test to failure of a 50 year old reinforced concrete (RC) railway bridge. The model was then used to assess the effectiveness of various strengthening schemes to increase the load-carrying capacity of the bridge. The bridge was a two-span continuous single-track trough bridge with a total length of 30 m, situated in Örnsköldsvik in northern Sweden. It was tested in situ as the bridge had been closed following the construction of a new section of the railway line. The test was planned to evaluate and calibrate models to predict the load-carrying capacity of the bridge and assess the strengthening schemes originally developed by the European research project called Sustainable bridges. The objective of the test was to investigate shear failure, rather than bending failure for which good calibrated models are already available. To that end, the bridge was strengthened in flexure before the test using near-surface mounted square section carbon fiber reinforced polymer (CFRP) bars. The ultimate failure mechanism turned into an interesting combination of bending, shear, torsion, and bond failures at an applied load of 11.7 MN (2,630 kips). A computer model was developed using specialized software to represent the response of the bridge during the test. It was calibrated using data from the test and was then used to calculate the actual capacity of the bridge in terms of train loading using the current Swedish load model which specifies a 330 kN (74 kips) axle weight. These calculations show that the unstrengthened bridge could sustain a load 4.7 times greater than the current load requirements (which is over six times the original design loading), whilst the strengthened bridge could sustain a load 6.5 times greater than currently required. Comparisons are also made with calculations using codes from Canada, Europe, and the United States.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 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".