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Monitoring large structures using a vision‐based target‐tracking (TT) system while maintaining the full resolution of high‐speed cameras may limit the data size or the sampling rate selection. Also, similar to wireless sensors networks where data loss often occurs during data transmission, TT signals could possibly lose data due to overexposure. The overall goal of this paper is to demonstrate the validity of compressive sensing (CS) for TT time signal processing when faced with challenges such as data loss. The first part of the study is concerned with signal length where TT signals could be further compressed while original data length is already minimum. Next, CS is investigated for improving and recovering TT signals from the possibility of signal loss as well as enhancing sampling rate for system identification purposes. Two case studies were used to obtain the signals for CS processing. The first case included four field‐monitoring tests of a footbridge that were conducted using different sampling rates TT with limited data length to use full cameras resolution. The second case study was concerned with measuring the shifting of the modal properties of a large‐scale bridge model from white‐noise excitation after earthquake loading. The results show that with limited signal length, lower sampling rates, or data loss, CS techniques can successfully improve TT signals.

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.

Much research is still underway to achieve long-term and real-time monitoring using data from vision-based sensors. A major challenge is handling and processing enormous amount of data and images for either image storage, data transfer, or image analysis. To help address this challenge, this study explores and proposes image compression techniques using non-adaptive linear interpolation and wavelet transform algorithms. The effect and implication of image compression are investigated in the close-range photogrammetry as well as in realistic structural health monitoring applications. For this purpose, images and results from three different laboratory experiments and three different structures are utilized. The first experiment uses optical targets attached to a sliding bar that is displaced by a standard one-inch steel block. The effect of image compression in the photogrammetry is discussed and the monitoring accuracy is assessed by comparing the one-inch value with the measurement from the optical targets. The second application is a continuous static test of a small-scale rigid structure, and the last application is from a seismic shake table test of a full-scale 3-story building tested at E-Defense in Japan. These tests aimed at assessing the static and dynamic response measurement accuracy of vision-based sensors when images are highly compressed. The results show successful and promising application of image compression for photogrammetry and structural health monitoring. The study also identifies best methods and algorithms where effective compression ratios up to 20 times, with respect to original data size, can be applied and still maintain displacement measurement accuracy.

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