Vision-based modal parameter identification for bridges using a novel holographic visual sensor.

[Display omitted] • Vision-based modal parameter identification was assessed for bridge structural health monitoring. • HVS and identification algorithms were developed for structural damage detection. • The superiority of vision-based modal parameters in damage detection was demonstrated. • Improved sensor efficiency and reduced cost responses were demonstrated. Noncontact monitoring using holography can be quite useful in vision-based structural health monitoring owing to its advantages in terms of data visualization and real-time applications. Aimed at exploring the potential of vision-based sensors for structural health monitoring in more general applications, this paper has developed the holographic visual sensor (HVS) with a vision-based modal parameter identification method. This strategy is an extension of the current vision-based techniques and holography. Based on the newly introduced spatial and temporal sequence data measured by HVS, the vision-based modal parameter identification method was proposed and applied to structural health monitoring for the first time. Accordingly, the feature point set describing the geometric morphology of the structural holography and the time-state-space mathematical model of the structural dynamics system was constructed. In this way, the mechanical behavior characteristics could be extracted from serial transient holographic responses following excitation, and the structural modal parameters were then calculated and quantitatively analyzed. Laboratory experiments and field tests were employed for validation based on the comparison between the results determined by the proposed HVS and those determined by conventional, high-precision contact sensors. The developed HVS and identification algorithms were able to visually collect and identify modal parameters at a higher data resolution and provide smoother modal shapes. The proposed method can thus be used to complement and improve existing computer-vision-based measurement techniques and damage detection. [ABSTRACT FROM AUTHOR]