On the Application of Neural Networks Trained with FEM Data for the Identification of Stiffness Parameters of Improved Mechanical Beam Joints

Abstract

Even though beam-type elements are widely adopted in the industry due to their low computational cost and potential time savings when modeling, they present a significant shortcoming given by their own formulation, which makes them incapable of accounting for local joint topology, which has a notable influence on the behavior of these structures. In this scenario, solutions that can mitigate this drawback while still providing improved results with simple models are of special interest. Many research works have focused on joint-specific approaches, as reflected in the literature. This paper introduces a novel generally improved beam model. This model uniquely features 4 nodes, 12 elastic elements, and 1 beam, contrasting starkly with the conventional beam elements that consist of merely 2 nodes and 1 element. This innovative model enhances the adaptability of modeled structures at the joint level. Crucially, it necessitates a methodology for the precise estimation of the elastic elements at the joint level. This article explores the capabilities of artificial neural networks for predicting the stiffness values derived from the calculated displacements at specific points within a complete structure. This research provides a complete analysis of the proposed methodology showing the significant limitations encountered for ANN when predicting finite element methodology (FEM)-derived values. The results and findings obtained in the article serve as a valuable reference paving the way for future studies involving finite element models and artificial neural networks.