Analysis of Geometric Characteristics of Cracks and Delamination in Aerated Concrete Products Using Convolutional Neural Networks

Abstract

Currently, artificial intelligence (AI) technologies are becoming a strategic vector for the development of companies in the construction sector. The introduction of “smart solutions” at all stages of the life cycle of building materials, products and structures is observed everywhere. Among the variety of applications of AI methods, a special place is occupied by the development of the theory and technology of creating artificial systems that process information from images obtained during construction monitoring of the structural state of objects. This paper discusses the process of developing an innovative method for analyzing the presence of cracks that arose after applying a load and delamination as a result of the technological process, followed by estimating the length of cracks and delamination using convolutional neural networks (CNN) when assessing the condition of aerated concrete products. The application of four models of convolutional neural networks in solving a problem in the field of construction flaw detection using computer vision is shown; the models are based on the U-Net and LinkNet architecture. These solutions are able to detect changes in the structure of the material, which may indicate the presence of a defect. The developed intelligent models make it possible to segment cracks and delamination and calculate their lengths using the author’s SCALE technique. It was found that the best segmentation quality was shown by a model based on the LinkNet architecture with static augmentation: precision = 0.73, recall = 0.80, F1 = 0.73 and IoU = 0.84. The use of the considered algorithms for segmentation and analysis of cracks and delamination in aerated concrete products using various convolutional neural network architectures makes it possible to improve the quality management process in the production of building materials, products and structures.

Keywords

• computer vision
• convolutional neural network
• segmentation
• aerated concrete
• cracks