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This paper introduces a new accelerating algorithm, efficient match slimmer (EMS), specifically designed to lighten computational loads of sophisticated template matching algorithms, enabling these algorithms to be effectively run on single‐board computers. Utilizing EMS in conjunction with a robust template matching algorithm, we have developed Raspberry Vision—a compact, cost‐effective, and real‐time vision‐based system. Its compactness and portability facilitate a practical measurement strategy that not only minimizes the camera‐to‐target distance but also simplifies the camera calibration process in bridge displacement monitoring, thereby enhancing measurement accuracy. The performance of the system is estimated on two operational suspension bridges. The results demonstrate that Raspberry Vision, equipped with the measurement strategy, can significantly improve the measurement accuracy in the long‐span bridge test and is also suitable for cross‐sea bridge measurements. [ABSTRACT FROM AUTHOR]

The empirical envelope (EE) method based on the amplitude-modulation and frequency-modulation (AMFM) decomposition is effective for identifying the modal parameters of time-varying and weakly nonlinear systems. However, the identification accuracy of the EE method is sensitive to noises which often exist in vibration measurements of real structures. In this study, an improved empirical envelope (IEE) method is proposed to achieve robust modal parameter identification from noisy measurements. Specifically, the idea of sliding window threshold denoising is introduced to reduce the error in the instantaneous envelope induced by abnormal extreme points, and a moving average filter is utilized to reduce the error in the instantaneous frequency induced by high-frequency noises. Two numerical examples and an experimental example of a full-bridge aeroelastic model are analyzed to validate the accuracy of the IEE method and highlight the superiority of the IEE method relative to the original EE method. It is concluded that the IEE method is robust to measurement noises (the considered signal-to-noise ratios include 5–90 dB) and that the IEE method is more accurate than the EE method. Hence, the IEE method serves as a promising alternative for modal parameter identification of time-varying and weakly nonlinear systems. [ABSTRACT FROM AUTHOR]