Early Anomaly Warning of Environment-Induced Bridge Modal Variability through Localized Principal Component Differences
Publication: ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
Volume 8, Issue 4
Abstract
Accurate elimination of environmental variability on bridge modal frequency is a prerequisite for high-quality structural performance evaluation. However, the non-Gaussian and nonlinear characteristics of data distribution associated with variable environments restrict the application of anomaly warning methods with inaccurate or unreliable detection results. Consequently, an early warning method in abnormal modal frequency based on the localized principal component differences model through integrating the slow feature analysis (SFA) and -nearest neighbor rule is proposed in this paper. SFA is first used to extract the measured slowly features of modal frequency for dimensionality reduction and redundant information elimination. Second, the localized modal set of each sample can be automatically searched from the training database based on the Euclidean distance metrics. Third, the estimated slowly features of modal frequency can be calculated using the mean vector of this set. Finally, the environmental variability can be suppressed through the principal component differences between measured and estimated slowly features. After this analysis, an early warning index of modal abnormality (i.e., Mahalanobis distance) is defined for enlarging slight changes in abnormal frequency. The warning results of Z24 bridge indicate that the proposed method discards the environment-induced modal variability without environmental measurements by fully considering both the nonlinearity between modal variables and the non-Gaussianity of data distribution, and the detectability of frequency anomalies outperforms conventional methods under various modal order combinations.
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Data Availability Statement
All data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
This research work was jointly supported by the National Natural Science Foundation of China (Grant Nos. 51978128 and 52078102), and the Fundamental Research Funds for the Central Universities (Grant No. DUT22ZD213).
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Information & Authors
Information
Published In
ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
Volume 8 • Issue 4 • December 2022
Copyright
© 2022 American Society of Civil Engineers.
History
Received: Mar 25, 2022
Accepted: Jun 6, 2022
Published online: Aug 1, 2022
Published in print: Dec 1, 2022
Discussion open until: Jan 1, 2023
ASCE Technical Topics:
- Analysis (by type)
- Automation and robotics
- Bridge components
- Bridge engineering
- Bridge tests
- Continuum mechanics
- Detection methods
- Dynamics (solid mechanics)
- Engineering fundamentals
- Engineering mechanics
- Field tests
- Gaussian process
- Mathematics
- Methodology (by type)
- Modal analysis
- Motion (dynamics)
- Natural frequency
- Nonlinear analysis
- Oscillations
- Probability
- Solid mechanics
- Stochastic processes
- Structural analysis
- Structural engineering
- Systems engineering
- Tests (by type)
Authors
Metrics & Citations
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