Application Of Deep Learning Algorithm In Identification Of Concrete Material Composition And Component Surface Cracks

Due to its high compressive strength,good plasticity and other advantages,concrete has become the most widely materials in civil engineering.Therefore,It is significance to study the performance of concrete materials for the development of civil engineering.Meanwhile,the application of artificial intelligence in civil engineering is more and more important.As an important part of artificial intelligence,visual analysis has been widely concerned in the analysis of concrete materials or structures because of its efficient adaptability and economy.In this paper,we will use deep learning technology to identify the short fibers in concrete materials on three scales of micron,millimeter and centimeter,and analyze the crack width,length and coordinates of reinforced concrete components,to realize the multi-scale application of deep learning technology in concrete structures and materials.Firstly,based on the micro scale,the image database of fiber reinforced concrete materials is established using nano-CT scanning,and the deep learning technology is used to build the rapid recognition model of chopped fiber,which can realize the rapid recognition of micro chopped fiber in fiber reinforced concrete,to help the establishment of fiber reinforced concrete mechanical model in meso-scale;Secondly,based on the macro scale,the image database of the existing reinforced concrete components is established,and the deep learning technology is used to quickly identify and segment the cracks on the surface of the components;Finally,crack parameters are extracted to establish a rapid identification model of reinforced concrete surface cracks.The specific research contents and conclusions are as follows:(1)In the research of the application of deep learning in the recognition of chopped fiber,the collected CT scan images of fiber are marked by the marking software,and then the data set is amplified by the methods of rotation,magnification and mirror image to establish the chopped fiber data set for deep learning training.Then,the deep learning network model of semantic segmentation is built through the comparison of different super parameters,and then the network is trained by using the fiber data set.In the experimental analysis,three different evaluation indexes are selected to evaluate the accuracy of the model,and other classical segmentation network models are compared,which proves the feasibility of the model in the identification of chopped fiber from both qualitative and quantitative aspects.(2)In the application of deep learning in the research of concrete surface crack,the crack data set is established by collecting the crack photos of concrete component surface in the laboratory,and then the rapid identification model of reinforced concrete existing component surface crack is established based on deep learning network.(3)After the deep learning network is used to get the crack segmentation graph,the crack connection algorithm proposed in this paper is used to denoise and connect the noise and fracture points on the segmentation graph,and then the octagon search and skeleton thinning algorithm are used to obtain the crack width,length and coordinates.Finally,the above segmentation model and crack parameter extraction algorithm are applied to flexural beam components,and the pixel size of crack parameters is converted into actual size by using target method.