Elasto-Plastic Optimization of Steel Trusses Under Geometric Nonlinearity and Imperfections via a Neural-Network-Assisted Genetic Algorithm

Elasto-plastic optimization of steel trusses accounting for geometric nonlinearity and initial imperfections poses a significant computational challenge, as each candidate configuration requires expensive nonlinear structural analysis. To address this, the present paper proposes a neural-network-assisted genetic algorithm (NNAGA)-based design framework, in which a deep neural network (DNN) model is progressively trained on data accumulated during the genetic algorithm (GA) search. A penalty-based objective function is constructed to minimize structural weight while enforcing constraints on plastic deformation, load-bearing capacity, and global stability, where the elasto-plastic response is characterized by complementary plastic work, and initial imperfections are introduced through scaled buckling mode shapes. The structural performance of each candidate is evaluated by geometrically and materially nonlinear finite element analysis with imperfections (GMNIA), coupled with linear buckling analysis (LBA). The framework is assessed using four established benchmark structures, namely a 10 bar, a 25 bar, and a 37 bar truss, as well as a double-layer space truss (DLST), and compared with a conventional GA under the same number of finite element evaluations. The NNAGA yields statistically significantly better designs in all four examples, with average fitness improvements of 42%, 21%, 77%, and 42%, respectively, reaching the solution quality of the best GA results using only 20–50% of the evaluation budget. A parametric study further supports the robustness of the penalty formulation and the algorithmic parameters, indicating additional achievable performance reserves.