Structural Geometric Morphology Monitoring for Bridges Using Holographic Visual Sensor

Abstract

Full-field noncontact structural geometry morphology monitoring can be used to achieve a breakthrough in the fields of structural safety monitoring and digital twins owing to its advantages of economy, credibility, high frequency, and holography. Moreover, such type of monitoring can improve the precision and efficiency of the structural health monitoring technology and theory of large-scale structures. This study validated the performance of a proposed holographic visual sensor and algorithms in computer vision-based, full-field, noncontact displacement and vibration measurement. On the basis of the temporal and spatial characteristics of the measured series data, denoising, and the disturbance-rejection algorithm, the microscopy algorithm of subpixel motion and the extracting algorithm of motion information were respectively constructed for weak high-order displacement components and the holographic measurement of high-quality geometric morphology. Moreover, an intelligent perception method optimized for holographic-geometric and operational-modal shapes were used to extract morphological features from a series of holographic transient responses under excitation. Experimental results showed that the holographic visual sensor and the proposed algorithms can extract an accurate holographic displacement signal and factually and sensitively accomplish vibration measurement while accurately reflecting the actual change in structural properties under various damage/action conditions. The accuracy and efficiency of the system in the structural geometry monitoring for dense full-field displacement measurement and smooth operational modal shape photogrammetry were confirmed in the experiments. The proposed method could serve as a foundation for further research on digital twins for large-scale structures, structural condition assessment, and intelligent damage identification methods.