Crack Detection And Structural Damage Assessment Based On Computer Vision And Data-Driven Methods

Structural surface cracks are important indicators for accessing structural damage and safety,thus the crack detection has become an important part of structural health monitoring.This paper uses computer vision technology to identify and extract the crack pixels from the image of structure surfaces,and then calculates the crack features including width and length based on the binary image of crack.Moreover,various machine learning algorithms are applied to make a direct connection between the crack features and the damage state of the actual structure.The main contributions of the paper are as follows.(1)A large crack database is established to train the neural network for crack identification and crack extraction.Two crack identification and extraction models are proposed,namely two-step method and one-step method.In addition,two field tests were carried out to verify the effectiveness of the two-step method and one-step method proposed in this chapter.(2)Considering that it is difficult to accurately detect and extract cracks in the original image due to factors such as long shooting distance,jitter during shooting,and unsatisfactory lighting conditions in the shooting environment,optimized super-resolution reconstruction models with magnifications of 2x,4x,and 8x are proposed based on the classic SRGAN.The new models are proved to be better than bicubic linear interpolation algorithm and SRGAN model.(3)A method based on image local feature point detection and matching is proposed to realize image stitching.The RANSAC algorithm is also used to reduce the mismatching situation.In addition,the crack features are systematically summarized and divided into single level and overall level.And the definition and calculation method of each feature is respectively given.Finally,the whole process of crack identification,extraction,image stitching and crack features calculation is verified by the loading test of the actual reinforced concrete beam.(4)A complete set of crack labelling and crack matching algorithm based on shape context is proposed,which can combine the crack information from different detections.And the shape context of the skeleton line can be utilized to realize crack matching at pixel level,so that the development of each crack pixel can be accurately monitored.And through the verification of the actual experiment,this algorithm shows the effectiveness of the method.(5)16 groups of reinforced concrete beam specimens were designed,and the design variables included height-span ratio,stirrup ratio,reinforcement ratio,concrete strength,cover thickness and longitudinal bar diameter.The specimens are loaded in stages by means of three-point loading,and the surface crack developments during the test are recorded by multiple cameras to establish a database.Based on the database,machine learning algorithms including random forest,Adaboost and BP neural network are respectively proposed to establish the connection between crack features and structural damage status,so that the damage status of structures can be accessed according to crack features.