System Identification of Large-Scale Bridges Using Target-Tracking Digital Image Correlation

Abstract

This paper characterizes the extensive research activities conducted in the Earthquake Engineering Laboratory of University of Nevada, Reno, in the field of dynamic monitoring and system identification of three 1/3-scale two-span bridges. The first part of the study briefly presents the verification of target-tracking Digital Image Correlation (DIC) results as compared to conventional sensors, e.g., string potentiometers and triaxial accelerometers from one of the three bridge tests. Structural system identification is presented in the second part for the other two bridges with a focus on determining structural model parameters based on the DIC measured response data. All bridges were tested under bidirectional earthquake loading using the multiple shake table array. However, the system identification used data collected from white noise runs before and after the seismic tests. A quasi-linear response of the system was assumed because of the low intensity white noise base excitations, and the modal parameters were estimated accordingly. Using the structural vibration data recorded by target-tracking DIC at various locations on the bridges, five system identification methods were applied to analyze the modal parameters of the tested bridges. The results were used to estimate the frequency, damping ratio, and mode shapes of the bridges in two states. The initial state is before seismic testing and the end state is the damaged state after the completion of the seismic tests. The results show that the applied methods provide a reasonable estimate of the natural frequency and damping ratio of the bridge systems in the original and damaged states.

Single-Camera-Based Bridge Structural Displacement Measurement with Traffic Counting

“…Frequency-domain analyses are usually employed on the extracted vibration signals to evaluate the structural health of bridges [ 11 , 12 ]. The frequency response from a bridge’s natural frequency and traffic load often resides at separate frequency bands [ 13 ]; thus, identifying the frequency response specific to the structure would be possible.…”

“…Statistics of abnormal modal frequencies can infer structural damages [ 14 ]. Although the natural frequencies may exhibit variance between multiple methods, measurement points, and ambient loading conditions [ 11 , 13 ], frequency-domain analysis gives reasonable estimates from the extracted vibration signal. As the structural damage is often localized within a particular member or joint, simultaneous measurement on multiple points on the bridge is also important to pinpoint the damage’s approximate location.…”

Implementation and Evaluation of Vision-Based Sensor Image Compression for Close-Range Photogrammetry and Structural Health Monitoring

“…Sensors 2020, 20, 6844 5 of 32 (4) natural feature point is the point that is found using the ellipse finding algorithm but their locations are also not within the scope of monitoring;…”

“…The advancements of non-contact and vision-based sensors in the field of structural health monitoring (SHM) as well as the development of optics and computer vision algorithms have led to a growing demand, among the civil and construction engineering communities, for long-term continuous and real-time vision-based SHM. Currently, monitoring using vision-based sensors incorporates an offline camera calibration and a closed-range photogrammetry process while using either artificial markers [1][2][3][4][5][6][7] or relying on the natural features of the structure [8][9][10]. Several previous works have shown the robust development and promising future of vision-based sensors deployment for SHM purposes [9,[11][12][13][14][15].…”

Sensor Data Interpretation in Bridge Monitoring—A Case Study

“…Conventionally, strain gauges and accelerometers are fixed on structures with wired connections to data acquisition systems. Now, camera based non-contact vision sensors have emerged as a promising alternative to conventional contact sensors for health monitoring (Brownjohn et al, 2017;Feng and Feng, 2018;Ngeljaratan and Moustafa, 2019). While sensor technology has progressed rapidly, methodologies for extracting useful information from data and incorporating them into the decision making process have not matured.…”