# Novel Method for Bridge Structural Full-Field Displacement Monitoring and Damage Identification
###### Abstract  
Currently, measurement points in bridge structural health monitoring are limited. Consequently, structural damage identification is challenging due to sparse monitoring data. Hence, a structural full-field displacement monitoring and damage identification method under natural texture conditions is proposed in this work. Firstly, the feature points of a structure were extracted via image scale-invariant feature transform. Then, the mathematical model was analyzed respecting the relative position change of the feature points before and after deformation, and a calculation theory was proposed for the structure's full-field displacement vector (FFDV). Next, a test beam was constructed to obtain the FFDV calculation results for the beam under different damage conditions. Validation results showed that the maximum length error of the FFDV was 0.48 mm, while the maximum angle error was 0.82\({}^{\circ}\). The FFDV monitoring results for the test beam showed that the rotation angle of the displacement vector at the damage location presented abnormal characteristics. Additionally, a damage identification index was proposed for the rotation-angle change rate. Based on the validation test, the index was proven to be sensitive to the damage location. Finally, a structural damage identification program was proposed based on the FFDV monitoring results. The obtained results will help to expand structural health monitoring data and fundamentally solve damage identification issues arising from sparse monitoring data. This study is the first to implement structural full-field displacement monitoring under natural texture conditions. The proposed method exhibits outstanding economic benefits, efficiency, and visualization advantages compared with the conventional single-point monitoring method. b 2023 13 1756 10.3390/app13031756 10.390/app13031756 10.  
device and a close-range photogrammetric target to measure subsidence in local areas of a structure. The technology uses various image-processing algorithms to achieve real-time automatic measurements of structural subsidence. [PERSON] et al. [4] proposed a full-field optical flow method based on deep learning to measure structural displacement. This method offers a higher level of accuracy than the classical optical flow algorithm, and the measurements are consistent with displacement sensor measurements. [PERSON] et al. [5] proposed a bridge-vibration measurement method combining unmanned aerial vehicles (UAVs) and digital image correlation technology. The DIC method was used to analyze a bridge video captured by a UAV, to monitor the displacement of a measuring point, by which the frequency and vibration mode were extracted from the time-variant displacement of the target point. The experimental results proved the feasibility of UAVs in bridge-vibration measurements. Compared with the method of Ngeljaratan [2], the later method offers a 35% increase in monitoring efficiency, a 26% decrease in monitoring costs, and a 7% increase in monitoring accuracy. [PERSON] et al. [6] proposed a cable force estimation method based on UAV and computer vision technology. The test results indicated that the cable force calculated using this technique was consistent with the value measured using an accelerometer. [PERSON] et al. [7] proposed two remote-detection methods for long-span bridges based on computer vision: in the first method, structural deformation is obtained by detecting the movement of a bridge target light source via a camera under a bridge; the second method is used for bridges that cross rivers or deep canyons and involves the use of cameras on a bridge deck to monitor deformations. The two methods are applicable in safety assessments of various bridges and large engineering structures. [PERSON] et al. [8] used a fiber Bragg grating (FBG) differential settlement measurement (DSM) system to monitor the vertical displacement of a prestressed concrete box girder bridge that had been damaged in an earthquake for more than two years.