IDENTIFICATION OF CRACKS IN VERTICAL ELEMENTS OF BUILDING STRUCTURES USING CONVOLUTIONAL NEURAL NETWORKS

Problem statement. The relevant issue of modern Civil Engineering is the development of new methods for identifying defects in building structures, which would save human resources and reduce the dependence of survey results on subjective human factors. The purpose of the research is to develop artificial neural networks for the identification and classification of cracks in vertical elements of building structures (e. g., concrete and reinforced concrete walls). Methodology. The cloud tool Teachable Machine is used to develop a neural network with a pre-defined internal architecture. The libraries of TensorFlow software platform allow us to develop a convolutional neural network with a tunable architecture. The program code is written in Python. The training was performed using the cloud environment Colab. Scientific novelty. New models of artificial neural networks for the identification of defects in building structures are developed. The rational magnitudes of the training parameters and the topology of the convolutional neural network are determined allowing to achieve the highest accuracy and the lowest losses of the model. Practical value. The developed models of neural networks and the results obtained with their help ensures a high efficiency of the artificial intelligence to solve problems of the health monitoring of building structures. Unlike traditional approaches, the proposed models allow real-time automatic diagnostics by analyzing photographic images, images from computer, smartphone or quadcopter webcams. The latter makes it possible to inspect buildings without the physical presence of humans at the site, which is especially important for working in dangerous places, such as tall buildings, partially destroyed buildings, mined areas, etc. Conclusions. The proposed methods can be further extended for the monitoring and classification of a wide range of defects in building structures.