Gaussian Process Regression Prediction Model for Vortex-Induced Vibration of Suspension Bridges Driven by Real Bridge Monitoring Data
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it