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Featured Application: Close-range photogrammetry and structural health monitoring of civil infrastructures in challenging lighting environments. This paper describes an alternative structural health monitoring (SHM) framework for low-light settings or dark environments using underexposed images from vision-based sensors based on the practical implementation of image enhancement algorithms. The proposed framework was validated by two experimental works monitored by two vision systems under ambient lights without assistance from additional lightings. The first experiment monitored six artificial templates attached to a sliding bar that was displaced by a standard one-inch steel block. The effect of image enhancement in the feature identification and bundle adjustment integrated into the close-range photogrammetry were evaluated. The second validation was from a seismic shake table test of a full-scale three-story building tested at E-Defense in Japan. Overall, this study demonstrated the efficiency and robustness of the proposed image enhancement framework in (i) modifying the original image characteristics so the feature identification algorithm is capable of accurately detecting, locating and registering the existing features on the object; (ii) integrating the identified features into the automatic bundle adjustment in the close-range photogrammetry process; and (iii) assessing the measurement of identified features in static and dynamic SHM, and in structural system identification, with high accuracy. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

• Target-tracking DIC seismic response monitoring from large-scale bridge shake table test. • Field monitoring and system identification of pedestrian bridge using target-tracking DIC. • 3D DIC displacement measurements are verified against conventional instrumentation. • DIC-based system ID is verified for two bridges against results from accelerometers. Our nation's infrastructure is aging and deteriorating which increases the need for condition assessment and structural health monitoring. For this purpose, there is a growing interest in using non-contact monitoring methods because of the challenges associated with deploying and installing conventional instrumentation. Target-tracking digital image correlation (DIC) is among these methods and is the focus of this paper. Several research activities have been conducted at the University of Nevada, Reno in the field of dynamic monitoring using DIC and presented here. The first part of the paper is concerned with a large-scale laboratory application where target-tracking DIC was utilized to monitor the response of a bridge structure tested under bidirectional earthquake shaking. The 3D dynamic response of the bridge, along with modal properties, i.e. natural frequency, damping ratio, and mode shapes, were measured or determined using both DIC and conventional instrumentation then compared for validation purposes. The second part of the paper presents results from field monitoring of an actual footbridge under pedestrian loading to determine the vibration frequencies of the bridge, and again compare it against results from conventional accelerometers for validation. From both applications, the DIC is demonstrated to successfully capture (1) peak and residual bridge deck rotation and deformation under different earthquake intensity levels and (2) modal properties for system identification. Practical DIC sampling rates were used to accurately monitor and capture the dynamic response of bridges, which shows a high potential for using DIC for larger structural health monitoring applications and future reconnaissance works. [ABSTRACT FROM AUTHOR]