APPLICATION OF ARTIFICIAL INTELLIGENCE METHODS TO PREDICT THE ULTIMATE STATIC LOAD OF A BEAM MADE OF HOMOGENEOUS MATERIAL ACCORDING TO THE VON MISES CRITERION BASED ON THE DATA OF STRUCTURAL STRENGTH ANALYSIS
DOI:
https://doi.org/10.20998/2079-0023.2025.01.05Keywords:
neural networks, supervised learning, linear regression, static structural strength analysis, finite element method, numerical experimentAbstract
The subject of the study is static structural analysis in mechanics. The aim of the work is to create and train an artificial intelligence model in the form of neural networks to predict the ultimate load on a structural element such as a beam made of a homogeneous material. The strength state of this structural element is determined by equivalent stresses according to the von Mises criterion. The initial and variable parameters are the geometric dimensions and power loads acting on the body. Achieving the goal makes it possible to calculate the strength of a structural element faster in terms of computation and with acceptable error values compared to classical methods of mechanics using numerical methods, in particular the finite element method. To achieve this goal, the following tasks are solved: conducting numerical experiments to analyze the strength state under static loading of a beam structural element using the finite element method; determining the key parameters of the body; preparing and aggregating data for the model; designing and training the model. Numerical experiments were carried out with predefined types of fixings and loads on the beam. There were 3 variations of data preparation and, accordingly, models to ensure the representativeness of predictions by neural networks. All numerical experiments were conducted in computeraided design systems. All numerical experiments were conducted in computeraided design systems. The design of the models was based on the principle of a minimal but sufficient number of hidden network layers and neurons in them. The model was trained on the principle of learning with a teacher, where a certain number of geometric properties and the pressure resisted by the body were selected as input parameters, and the maximum equivalent stress according to the von Mises criterion corresponding to these parameters was selected as an output parameter. These stress values are obtained as a result of analyzes in the computeraided design system. Prediction of the same values for other parameters of the object of study using neural networks is based on a linear regression algorithm and a certain number of input parameters. The models were optimized using the adaptive moment estimation algorithm. The model prediction error was calculated using the mean square error. The result of the study is the creation and training of artificial intelligence models and verification of their ability to predict the maximum equivalent stresses according to the von Mises criterion based on the geometric and force characteristics of a structural element with relative accuracy to a similar calculation in computeraided design systems. The analysis of the obtained results made it possible to prove the possibility of a reliable prediction of the desired maximum values of equivalent stresses characterizing the strength state of the considered structural element at different ratios of geometric and force parameters, without performing strength analysis by traditional methods. This expands the possibilities of finding rational design options.
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