Defect Identification And Analysis Of Rockfill Concrete Based On Deep Learning Metho

Due to the influence of factors such as construction environment,pouring method,and stone pile structure,bubbles that are not discharged during the stone pile concrete pouring process are squeezed and deformed,forming internal defects of different sizes and shapes,which ultimately affect the mechanical properties of stone pile concrete materials and bring unpredictable risks.Conducting research on defects in stone pile concrete,classifying,accurately identifying,characterizing the structure,and studying the mechanical properties of defects can help explore the characteristics of stone pile concrete under construction environments and provide reference for achieving digital twins,more realistic numerical simulation research,and engineering practice.Based on the construction site poured large volume stone pile concrete specimens,this paper takes internal defects in stone pile concrete as the research object,and uses the latest advances in image recognition and numerical simulation techniques to construct a U-net deep learning model to segment stone pile concrete defects pixel by pixel.By using this program to obtain defect information,the position,shape,and size characteristics are explored,and the defect distribution model,distribution parameters,and characterization methods are established to reveal the failure mode,damage mechanism,and fracture evolution process of the defect model.1)a specially made 10 cm cardboard is used to extract defects from large volume specimens at the engineering scale.According to the principle of defect formation and the degree of contact between the defect and the bond surface,the defects are classified into four categories: internal pores of self-compacting concrete,internal filling of self-compacting concrete,bond surface pores,and bond surface filling.A U-net deep learning network is constructed to build a classifier,which includes a main feature extraction network,feature enhancement network,and prediction network.By using image enhancement based on different lighting,angles,and defect shapes to expand samples,the model is parameterized and modified through backpropagation and specific evaluation indicators.The prediction accuracy of the model can reach 97%.2)based on the independently developed stone pile concrete defect recognition program,various types of defect contours and location information containing physical features are obtained.The shape basically conforms to the elliptical fitting,and the roundness decreases as the defect area increases.On the position,there are more defects on the bond surface with larger areas,while the rest of the region is roughly the same.The sizes,numbers,porosity,and major and minor axes of the fitted ellipse of the defects all conform to the logarithmic normal distribution.3)the impacts caused by various types of defects are different.Among them,filling unconsolidated defects can easily lead to the generation of cracks and become the beginning of the fracture process.When pore defects are connected in a straight line or deviation is small,it is easy to form overall penetration,which leads to premature loss of bearing capacity.The distribution position of the bond surface defect is prone to form a weak surface,which guides the direction of crack propagation.