Method for Concrete Structure Analysis by Microscopy of Hardened Cement Paste and Crack Segmentation Using a Convolutional Neural Network

Abstract

In recent years, the trend of applying intelligent technologies at all stages of construction has become increasingly popular. Particular attention is paid to computer vision methods for detecting various aspects in monitoring the structural state of materials, products and structures. This paper considers the solution of a scientific problem in the area of construction flaw detection using the computer vision method. The convolutional neural network (CNN) U-Net to segment violations of the microstructure of the hardened cement paste that occurred after the application of the load is shown. The developed algorithm makes it possible to segment cracks and calculate their areas, which is necessary for the subsequent evaluation of the state of concrete by a process engineer. The proposed intelligent models, which are based on the U-Net CNN, allow segmentation of areas containing a defect with an accuracy level required for the researcher of 60%. It has been established that model 1 is able to detect both significant damage and small cracks. At the same time, model 2 demonstrates slightly better indicators of segmentation quality. The relationship between the formulation, the proportion of defects in the form of cracks in the microstructure of hardened cement paste samples and their compressive strength has been established. The use of crack segmentation in the microstructure of a hardened cement paste using a convolutional neural network makes it possible to automate the process of crack detection and calculation of their proportion in the studied samples of cement composites and can be used to assess the state of concrete.