Improved approach for vibration‐based structural health monitoring of arch dams during seismic events and normal operation

Abstract

Summary Vibration‐based structural health monitoring for arch dams is influenced by various environmental effects, such as water depth and air temperature. These effects do not cause structural damage; therefore, their effects need to be minimized or removed from models. Moreover, arch dams are often located in earthquake‐prone areas; thus, an improved approach is proposed herein to adapt to the aforementioned situations. In this study, an arch dam in China was used for a case study. A long‐term continuous structural health monitoring system was proposed to operate under seismic and environmental excitation. An autoregressive exogenous model was applied for modal identification under seismic excitation. An automatic procedure based on stochastic subspace identification and density‐based spatial clustering of applications with noise was carried out for modal identification under environmental excitation. This study focused on the identification of the first three‐order modes. The seismic events were then analyzed. The natural frequencies before, during, and after seismic events were the same within a certain margin of error. This indicates that earthquakes did not cause major seismic damage to the arch dam. In addition, natural frequencies were continuously identified under environmental excitation. The relationship between natural frequency and water depth forms a quadratic curve, and the modal damping ratio increases with increasing water depth. Finally, this paper presents a random forest model to minimize environmental effects on natural frequencies. After minimizing the influence of non‐structural factors, the natural frequency exhibited a weak increasing trend over time.

Damage Identification in Cement-Based Structures: A Method Based on Modal Curvatures and Continuous Wavelet Transform

“…Sometimes static analyses have been exploited, for example with parameter identification to identify damages in frame structures [17] or stiffness separation method on truss structures [18]. Vibrational analysis (in particular, operational modal analysis coupled with proper signal processing techniques [19]) can depict the dynamical performance of a structure and reflects eventual changes due to disrupting events [20] (e.g., earthquakes [21]) or deterioration processes (e.g., contaminants penetration). In fact, in case of damage, the dynamic response of the structure is different with respect to the undamaged status [22].…”

A Robust Deep Learning-Based Damage Identification Approach for SHM Considering Missing Data

“…Civil infrastructures, including roads and bridges [1][2][3], buildings [4][5][6], dams [7][8][9], etc., are suffering from performance deterioration due to harsh environments, loadings, and even natural or man-made disasters. Structural health monitoring (SHM) is a critical technique that emerged in the past decades to detect and evaluate the condition of a structure in real-time [10][11][12].…”

A Smoothing EKF-UI-WDF Method for Simultaneous Identification of Structural Systems and Unknown Seismic Inputs without Direct Feedthrough

“…Seismic excitation information and structural state assessment are of great signifcance for the rapid evaluation of structural integrity after seismic excitation [1][2][3][4]. Teoretically, ground motion information can be obtained through seismic stations or sensors installed on the structure bases, but in practice, not all structures are equipped with sensors to record ground motions due to the cost of instrumentation.…”