Gaussian Process Regression Prediction Model for Vortex-Induced Vibration of Suspension Bridges Driven by Real Bridge Monitoring Data
Bibliographic record
Abstract
Vortex-induced vibration (VIV) is a great threat to the safety of vehicles traveling on large-span bridges, and has a certain impact on the structural safety and durability of bridges. It is very important to predict and warn of VIV events in advance. In this paper, the continuous monitoring data of Xihoumen Bridge over the last 4 years were used to identify and intercept 67 VIV events, including their formation, stable oscillation, and subsequent decay phases, as recorded in acceleration segments. Samples with clear labels were obtained using sliding windows, which were complemented by other VIV characteristic data to construct a sample database related to historical VIV events. A VIV prediction model based on the VIV samples was established and trained using Gaussian process regression (GPR). The model realized the dynamic prediction and perception of VIV events, and the validity of the model was verified using two VIV events of real bridges with different development levels.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".