Performance Evaluation of Chinese High-Speed Railway Bridges Under Seismic Loads
Bibliographic record
Abstract
The multi-span simply supported (MSSS) box girder bridge is the most used structural form for the high-speed railway (HSR) in China. In structural design, it is required that the MSSS bridge system has high stiffness and low deflection under the operation loads. With the expansion of the HSR network to regions that are seismically active, the seismic performance of MSSS bridges in these regions is an issue of great concern. In this study, the performance-based earthquake engineering (PBEE) methodology originally developed to quantify the seismic performance of buildings and bridges has been adopted to quantify the seismic performance of the HSR MSSS bridges in China. Typically, a four-span MSSS bridge used in China’s Sichuan–Yunnan HSR lines has been extensively assessed by the PBEE approach. This study is the first of its kind to systematically identify and quantify the damage states, repair actions, repair costs and travel delay losses for China’s HSR MSSS bridge system. The results reveal that the financial loss from the MSSS bridge system is highly dependent on the anchorage capacity of the fixed bearings. Overall, the costs of travel delay outweigh those for structural repair. Most of the financial loss can be attributed to the functional loss of the track-slab components and bearings of the HSR MSSS bridge system.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".