Effect of Ground Motion Time–Frequency Non-Stationarity on Seismic Response of High-Speed Railway Simply Supported Bridge Based on Wavelet Packet Transform
Bibliographic record
Abstract
Time–frequency non-stationarity is a ground motion characteristic which is frequently neglected in current seismic design and research. This paper studies its impact on the seismic response of high-speed railway simply supported bridge (HSRSSB). A method for generating time–frequency stationary earthquakes (TFSEs) and time–frequency non-stationary earthquakes (TFNSEs) using wavelet packet transform is proposed. A finite element model of a three-span HSRSSB is established using OpenSees. The seismic response is obtained through non-linear dynamic time history analysis, and the fragility curves of bridge components and system are calculated through incremental dynamic analysis. Finally, the reasons for the differences are analyzed by comparing the differences in seismic response of bridge, component fragility and system fragility under two groups of ground motion. The results show that the time–frequency non-stationarity of ground motion has an effect on the bridge response and fragility under strong earthquakes. TFNSE will lead to larger ground motion response, and the damage probability of bridge components and systems is higher. The reason is related to the damage and period extension of bridges under ground motion. Structures with prolonged period anti-seismic measures need to pay attention to this effect.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".