Deep Neural Network Direct Stiffness Method: Fundamental Steps into Beam Design

Abstract

AbstractThis paper presents a novel approach for performing beam element analysis that considers the nonlinear deformation behavior of various RHS and SHS sections made of mild to high‐strength steel: the Deep Neural Network Direct Stiffness method (DNN‐DSM), which uses Deep Neural Networks (DNN), a subset of machine learning algorithms and more general artificial intelligence approaches, to predict the nonlinear stiffness matrix terms in a beam element formulation for implementation in the direct stiffness matrix (DSM). These predictions are made using trained DNN models drawn from an extensive pool of geometric‐material nonlinear simulations with additional imperfections (GMNIA) using shell‐based models. Initial implementations of this method are able to predict the nonlinear load‐displacement and moment‐rotation behavior of various cross sections with high accuracy. This combines the precision of shell analysis with the computational efficiency of beam element analysis. Previous publications have already demonstrated the suitability and advantages of this method, albeit on a small scale for local buckling prediction. This work goes beyond previous studies and focuses on finite beam element design. This includes a review of the modeling approaches of DNN‐DSM using experimental and numerical results from literature.