# Novel Method for Bridge Structural Full-Field Displacement Monitoring and Damage Identification
###### Abstract
Its trajectory was automatically calculated via a 3D model, and typical bridge damage was identified using the machine learning method. [PERSON] et al. [25] proposed a crack-shaped intelligent extraction model based on a support vector machine. They used UAVs to obtain images of a bridge structure, which were then used to realize the intelligent recognition of bridge-crack width. [PERSON] et al. [26] designed a bridge monitoring scheme using a UAV equipped with a high-definition pan-tilt-zoom camera. The scheme can efficiently acquire images of bridge cables based on their structural characteristics and the distribution form of bridge cables, extract useful information via image processing, and comprehensively evaluate the health status of bridge cables based on relevant specifications. [PERSON] et al. [27] designed an automatic bridge-crack detection system using a real-time image-processing method. The system, which is assembled on a UAV, can realize real-time data acquisition and processing. Additionally, it can effectively detect bridge cracks with higher precision and speed compared with other detection methods. [PERSON] et al. [28] developed a method that uses a climbing robot to inspect damage in steel bridges. This method applies a steel-surface mosaic image and 3D reconstruction technology to demonstrate the apparent state of the structure. [PERSON] et al. [29] developed a visual inspection system comprising a climbing robot, an image-processing platform, and four fixed cameras. Based on the scale-invariant feature transformation (SIFT) algorithm, the system realizes bridge-surface continuous image sequence automatic stitching and the intelligent recognition of bridge defects using image-processing technology. [PERSON] et al. [30] proposed a vision-based bolt-looseness detection method using the Hough transform and a support vector machine. Subsequently, a structural damage location method was proposed based on a density peak fast clustering algorithm. The aforementioned structural health monitoring methods are computer-vision-based and thus require the arrangement of target points on a structure [31]. The monitoring object is the same as that in conventional monitoring, which targets local structural measurement points [32]. Hence, structural damage identification difficulties arising from sparse monitoring data are not well solved [33]. The feature-extraction method can be used to obtain natural texture feature points on a bridge surface. In this study, the mathematical model was analyzed regarding the relative position change of the feature points on a bridge before and after deformation. Then, a displacement field calculation theory was proposed for the feature points. In addition, a full-field displacement monitoring method was established for the structure under natural texture conditions. Finally, a damage identification method was proposed combined with full-field displacement monitoring. The results can be used to monitor the deformation of the bridge structure in a non-contact manner, which improves the completeness of bridge structural monitoring data, and visualizes structural damage. Compared with conventional single-point monitoring, the proposed method offers outstanding economic, efficiency, and visualization advantages. In fact, the proposed method can automatically yield images of a bridge, provide structural full-field displacement analysis results in real time, and automatically output the damage location. The results presented herein can be applied extensively to small- and medium-span bridges.