AbstractAccurate 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 k-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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