Steady-State Data Baseline Model for Nonstationary Monitoring Data of Urban Girder Bridges

Abstract

In bridge structural health monitoring systems, an accurate baseline model is particularly important for identifying subsequent structural damage. Environmental and operational loads cause nonstationarity in the strain monitoring data of urban girder bridges. Such nonstationary monitoring data can mask damage and reduce the accuracy of the established baseline model. To address this problem, a steady-state data baseline model for bridges is proposed. First, for observable effects such as ambient temperature, a directional projection decoupling method for strain monitoring data is proposed, which can reduce the nonstationary effect of ambient temperature, and the effectiveness of this method is proven using equations. Second, for unobservable effects such as traffic load, a k-means clustering method for steady state of traffic loads is proposed; using this method, which can divide the steady and nonsteady states of traffic loads and reduce the nonstationary effect of traffic loads on strain monitoring data, a steady-state baseline model is established. Finally, the effectiveness of the steady-state baseline model is verified using an actual bridge. The results show that the proposed baseline model can reduce the error caused by nonstationary effects, improve the modelling accuracy, and provide useful information for subsequent damage identification.

Temperature Effects Removal from Non-Stationary Bridge–Vehicle Interaction Signals for ML Damage Detection

“…Zhang et al [21] proposed a steady-state data baseline model for bridges by eliminating the non-stationary effects of the temperature by employing PCA directional projection. Strain data at the different points of the bridge were used.…”

“…Conversely, Huang et al [18] adopted PCA to remove wind and traffic effects, while temperature was eliminated using a canonically correlated method. Zhang et al [21] applied a machine learning approach to the second PC for damage detection without considering other PCs and environmental effects, while the present work employs K-means for detecting and localizing damage. Moreover, considering the contribution by Wang et al (2022) [22], where long-span bridges and static data only are considered, this paper focuses on standard and frequent bridges with short and medium spans and uses dynamic response.…”

Damage Detection of High-Speed Railway Box Girder Using Train-Induced Dynamic Responses

“…Thus, early damage detection of bridges has become an important and indispensable part of structural health monitoring (SHM) systems for high-speed railways, and the application of new methods or new materials in the field of SHM has been widely studied [3][4][5][6][7][8]. Extensive research efforts have been devoted to damage detection, and many effective methods have been proposed [9][10][11][12][13][14].…”