All infrastructure degrades over time, but bridges are especially vulnerable to the combined effects of the environment, traffic loads, and natural disasters. Regular inspection and monitoring help to ensure bridges are structurally sound. Technological advances, including structural health monitoring systems installed on bridges, have helped in monitoring vibration-based damage. However, some environmental conditions can affect bridge modal frequencies used for SHM reporting. Analyzing and separating complicated environmental impacts on bridge frequency is important for structural diagnosis.
Researchers Zhen Wang, Ting-Hua Yi, Dong-Hui Yang, Hong-Nan Li, Guan-Hua Zhang, and Ji-Gang Han propose an early warning method in abnormal bridge modal frequency to eliminate the environmental variability and construct a warning index. In their study, “Early Anomaly Warning of Environment-Induced Bridge Modal Variability through Localized Principal Component Differences” in the ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering, the authors tested their model’s performance on a prestressed concrete bridge and compared their results against existing methods. Learn more about their method and its potential applications for high-quality frequency-anomaly identification and structural performance assessment at https://doi.org/10.1061/AJRUA6.0001269. The abstract is below.
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 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.
Read the paper in full in the ASCE Library: https://doi.org/10.1061/AJRUA6.0001269.
