VIBRATION-BASED DAMAGE IDENTIFICATION OF REINFORCED CONCRETE ARCH BRIDGES USING KALMAN–ARMA–GARCH MODEL
Bibliographic record
Abstract
To ensure safe operations of bridges, it is necessary to carry out the structural damage identification and safety assessment.To this end, this paper proposes a novel damage identification method based on structural health monitoring data, which is the combination of Kalman filter, autoregressive moving average (ARMA) model and generalized autoregressive conditional heteroskedasticity (GARCH) model.Firstly, the correlation between the system characteristics and the time series model is verified through the theoretical derivation of the system vibration equation.Secondly, Kalman filtering is used to preprocess the acceleration data and reduce the noise disturbance, by which a linear recursive ARMA model can be established to identify the structural damage.Then, a nonlinear recursive GARCH model is introduced to further improve the identification accuracy.Finally, the effectiveness of the proposed method is verified using the time history data obtained from the accelerated corrosion damage dynamic test of the reinforced concrete arch.The results show that: (1) the system vibrations are correlated with the time series model, whose residual variance ratio is demonstrated to be effective in identifying structural damage; (2) in the state of loading damage and corrosion damage, the identification accuracies of Kalman-ARMA are 32.8% and 75.8%, while those of the proposed method can reach 89.1% and 85.5%, respectively and (3) GARCH model can explain the heteroskedasticity hidden in the monitoring data, thereby further improving the accuracy of damage identification.Therefore, the proposed method may provide an innovative measure to assess the bridge structural condition in practice.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".