Numerical simulation of the defect&apos;s behavior in building structure elements during ultrasonic diagnostics

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Abstract

One of the important scientific problems of modern construction is the detection of cracks in elements of building structures. Among a significant number of methods, ultrasound diagnostics is popular due to automation and speed of processing results. However, the accuracy of ultrasonic methods is in the range of 40%, which does not meet modern requirements. The article considers the problem of ultrasonic wave propagation in a brick and conducts a numerical analysis of the behavior of defects in the form of a crack. The results were obtained in the form of fields of displacements, deformations and stresses in the structure as a function of time. The results obtained make it possible to identify the characteristic features of the reaction of defects to dynamic impact and obtain numerical dependences of the response of the structure in comparison with samples without defects.

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Artificial Neural Networks in Classification of Steel Grades Based on Non-Destructive Tests

Alexey Beskopylny, Alexandr Lyapin, Hubert Anysz … (2020)

Assessment of the mechanical properties of structural steels characterizing their strength and deformation parameters is an essential problem in the monitoring of structures that have been in operation for quite a long time. The properties of steel can change under the influence of loads, deformations, or temperatures. There is a problem of express determination of the steel grade used in structures—often met in the practice of civil engineering or machinery manufacturing. The article proposes the use of artificial neural networks for the classification and clustering of steel according to strength characteristics. The experimental studies of the mechanical characteristics of various steel grades were carried out, and a special device was developed for conducting tests by shock indentation of a conical indenter. A technique based on a neural network was built. The developed algorithm allows with average accuracy—over 95%—to attribute the results to the corresponding steel grade.