Damage detection method for reinforced concrete structures based on technical vision

Abstract

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