Structural health monitoring techniques have been applied to several important structures and infrastructure facilities, such as buildings, bridges, and power plants. For buildings, accelerometers are commonly used for monitoring the accelerations induced by ambient vibration to analyze the structural natural frequencies for further system identification and damage detection. However, due to the relatively high cost of the accelerometers and data acquisition systems, accelerometer-based structural health monitoring systems are challenging to deploy in general buildings. This study proposed an image analysis-based building deformation monitoring method that integrates a small single-board computer, computer vision techniques, and a single-camera multiple degree-of-freedom algorithm. In contrast to other vision-based systems that use multiple expensive cameras, this method is designed for a single camera configuration to simplify the installation and maintenance procedures for practical applications. It is designed to monitor the inter-story drifts and torsional responses between the ceiling and floor of a story that is being monitored in a building, aiming to maximize the monitored structural responses. A series of 1:10 reduced scale static and dynamic structural experiments demonstrated that the proposed method and the device prototype are capable of analyzing images and structural responses with an accuracy of 0.07 and 0.3 mm from the results of the static and dynamic experiments, respectively. As digital imaging technology has been developing dramatically, the accuracy and the sampling rates of this method can be improved accordingly with the development of the required hardware, making this method practically feasible for an increasing number of applications for building structural monitoring.
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