Failure Testing of a Full-Scale Reinforced Concrete T-Girder Bridge
Bibliographic record
Abstract
As a common type of bridge, the reinforced concrete simply supported T-girder bridges have some usual disease problems such as web vertical cracks, incline cracks near the supports and diaphragm damage, and so on injured by transportation loads. In Shanxi province most of T-girder bridges appear to be under strength for current vehicle loads because most of which are designed by conventional methods and the old codes so it is important to evaluate how the existing damage influence the ultimate bearing capacity of the bridge. The destructive test is a directive mean of verifying the ultimate bearing capacity of bridges. This paper provides details of a destructive load test of a T-girder bridge called Zuojiabao Bridge. It describes the analysis performed prior to testing, aimed at predicting the response of the structure at both serviceability and ultimate states, and discusses the results obtained.The development rule of strain, deflection and crack and the ultimate bearing capacity of the bridge were obtained through the destructive test process. The results showthat although the old T-girder bridge has enough bearing capacity to satisfy the new vehicle standard, it is strongly recommended to strengthen or retrofit the bridge. And the finite element analysis can be effectively applied to the failure analysis of the reinforced concrete T-girder bridge subjected to destructive load.
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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) | 0.001 | 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 | 0.001 | 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".