# Novel Method for Bridge Structural Full-Field Displacement Monitoring and Damage Identification
###### Abstract
This system is based on the hydrostatic liquid leveling of the connected vessels, buoyancy, force equilibrium, and photo-elasticity of FBGs [9; 10; 11]. The results showed that the FBG-DSM liquid-level system is promising for inexpensive long-term monitoring of bridges over long distances as it does not require specific environmental conditions, in situ visibility, or intensive labor. For structural damage identification based on computer vision, [PERSON] et al. [12] proposed a digital image crack-recognition method based on neural networks and image processing. This method requires a high-resolution camera to capture images of welded joints in large-span steel bridges. By training with a dataset constructed using crack pixels, accurate crack recognition was achieved. Moreover, the method is highly robust against noise. [PERSON] et al. [13] proposed a fine crack segmentation network (FCS-Net), which combines ResNet-50 with a full convolutional network. A test was performed using steel beam images with complex backgrounds, and the results showed that the MIoU of the FCS-Net was 0.7408, which is better than those of benchmark algorithms, such as LinkNet, DeepLab V3, and CrackSegNet. [PERSON] et al. [14] proposed a crack-recognition method that combines an R-FCN and Haar AdaBoost, which is suitable for UAV-based image recognition. This method relies on UAV-based computer vision and uses feature learning to recognize bridge cracks. [PERSON] et al. [15] proposed a bridge safety state assessment method based on a UAV, which combines visual image sensing technology with UAVs for crack image acquisition. As a result, it promotes the development of UAV detection and bridge-surface crack detection. [PERSON] et al. [16] proposed an automatic identification method for bridge-surface cracks based on two-dimensional amplitude and phase estimation (2D-APES) and mobile computer vision. As suggested by the name, 2D amplitude and phase estimation is performed to obtain a high-precision 2D spectral estimation of the crack image. Subsequently, low-frequency information is filtered to enhance the crack information to realize automatic crack identification. [PERSON] et al. [17] proposed a steel structural surface corrosion detection technology that combines image roughness and color information. [PERSON] [18] and [PERSON] [19] proposed a new method for identifying the corrosion of steel structures. This method combines the fully convolutional neural network U-Net with a newly developed image semantic segmentation model to achieve pixel-level corrosion defect identification of steel bridges. [PERSON] [20] proposed a semantic segmentation deep learning method and an efficient image marking tool that allows the rapid preparation of large training datasets and serves as a foundation for the effective detection, segmentation, and evaluation of corrosion status in images. [PERSON] et al. [21] investigated the automatic detection of concrete bridge cracks. Firstly, an image was filtered with increasing structural elements to smoothen the image and remove noise. Subsequently, the edges of the bridge cracks were accurately extracted using a multiscale morphological edge detector. [PERSON] et al. [22] developed a robot image-acquisition system based on computer vision. The system uses a vision-based remote-control robot to obtain images. It identifies the sizes of the cracks obtained using digital image-processing software, resulting in a rapid, safe, reliable, and low-cost detection process. [PERSON] et al. [23] proposed a vision-based inspection technique that automatically processes and analyzes acquired images. The images used in this technique are captured without controlling the angles and positions of cameras, and a preliminary calibration is not required. Using images from many different angles and prior knowledge regarding typical appearances, the proposed technique can successfully detect cracks near bolts. [PERSON] et al. [24] used a UAV equipped with a camera to acquire high-definition image data for bridge structures.