The use of artificial intelligence to detect defects in building structures

Abstract

Monitoring the technical condition of structures is the most important task aimed at improving the reliability and safety of buildings and structures. During the survey, a set of tasks arises to assess visible defects and damages, the solution of which requires the experience and attention of structural survey specialists. Often the omission of visible defects is the most common mistake when examining the engineering and technical condition of a building. Technical vision, as a method of classifying objects in images, can significantly improve the efficiency of visual inspection and reduce the number of errors on the object. In this paper, an algorithm for detecting damage to reinforced concrete structures based on a convolutional neural network model created in the Python programming language is investigated. The neural network was trained and tested on real defects of a monolithic reinforced concrete building. According to the results of the work, the high efficiency of artificial intelligence in determining defects and damages in the framework of the survey of the engineering and technical condition of monolithic reinforced concrete structures of a building under construction was revealed. Automation of works on visual inspection of building structures is a promising direction for the development of artificial intelligence.