Identification of the stress-strain state and damage of metal structures of building constructions with nanoparticle coatings using the electromagnetic-acoustic method

Introduction. Approximately 20% of accidents in buildings and structures are associated with the destruction of metal structures. Nanocoatings applied to metal structures significantly improve their operational properties, but at the same time make it difficult to use traditional non-destructive testing methods that require access to the surface of the base metal. The quality of the nanocoating is controlled during its formation. To prevent the destruction of metal structures, it is necessary to identify structural heterogeneities of the base metal in micro- and sub-micron sizes, which arise in areas of increased mechanical stress concentration and transform into macrodefects leading to structural failure. Methods and tools are required to non-destructively identify the stress-strain state and damage of the base metal structure through the layer of nanocoating. Electromagnetic-acoustical (EMA) transformation, is considered promising for solving this problem. Existing EMA tools do not fully utilize the potential of EMA conversion, have insufficient sensitivity and the lack of informativity. Methods and materials. For experimental research, we selected widely used in construction structures steels. Research was conducted on the interrelationship of structural alterations in standard samples of metals and the parameters of the electro-magnetic (EMA) transducer under static and cyclic loads. Results. Load diagrams of metal samples were obtained, with accompanying photographs of microstructure in control points, along with their frequency models derived through spectral analysis of the signal from EMA transducer. Discussion. Changes in the stress-strain state of the metal and accumulation of damage in its structure result in a complex interrelated set of changes in mechanical, acoustic, and electro-physical properties, all of which are reflected in the changes in the parameters of the frequency model. Conclusion. Based on the results of the conducted research, the use of a frequency model has been proposed as an integral parameter for identifying the stress-strain state and damage of metal in the equipment. The use of an artificial neural network for analyzing frequency model parameters simplifies the process of identifying the stress-strain state and damage of metal structures and increases its reliability.