Seismic fragility assessment of long-span cable-stayed bridges in China
Bibliographic record
Abstract
In the existing fragility assessment, most bridges are short-span bridges and a few bridges have long spans such as cable-stayed bridges. In this study, a procedure is proposed to conduct the component and system seismic fragility analysis of long-span cable-stayed bridges. Three critical issues are addressed in the procedure: (1) the optimal intensity measure of cable-stayed bridges, (2) limit state models of various components, and (3) contribution of individual components to the entire system failure of bridges. This study chooses a long-span cable-stayed bridge with the most common configuration in China and builds the numerical model of its multiple components using OpenSEES that can account for their nonlinear response and uncertainties in the ground motion and material properties. Four typical intensity measures are compared with respect to four characteristic properties including efficiency, practicality, sufficiency, and proficiency. Peak ground velocity turns out to be the optimal intensity measure. Limit states of pylon sections are derived by a numerical simulation based on pushover analysis and China’s guidelines. The pushover results indicate that the limit state of their section curvature depends highly on the section type and axial compression coefficient. A joint probabilistic seismic demand model and Monte Carlo simulation are employed to obtain an accurate system fragility estimate of cable-stayed bridges by accounting for the contribution of each component to the overall bridge system. The system fragility curves based on Monte Carlo simulation lie much closer to the upper bound fragilities given small correlation coefficients, implying that seismic demands of various components conditioned on the peak ground velocity are not correlated.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".