Global structural optimization considering expected consequences of failure and using ANN surrogates
The literature is filled with structural optimization articles which claim to minimize costs but which disregard the costs of failure. Due to uncertainties, minimum cost can only be achieved by considering expected consequences of failure. This article discusses challenges in solving real structural optimization problems, taking into account expected consequences of failure. The solution developed herein combines non-linear FE analysis (by positional FEM), structural reliability analysis, Artificial Neural Networks (used as surrogates for objective function) and a hybrid Particle Swarm Optimization algorithm, which efficiently solves for the global optimum. Optimization of a steel-frame transmission line tower is the application example.
structural optimization reliability analysis artificial neural networks particle swarm optimization optimum design risk optimization