Optimization Approaches for the Numerical Design of Structures Under Consideration of Polymorphic Uncertain Data

In this paper, optimization approaches for the numerical design of structures are presented. The uncertainty of structural parameters is considered by means of random variables and interval design parameters within the optimization. A new space subdividing technique is introduced to substitute time-consuming failure probability constraint evaluations. In order to solve optimization problems with polymorphic uncertain parameters, surrogate objectives are formulated and solved by means of a particle swarm optimization (PSO) approach in combination with Monte Carlo simulation and interval analysis. Two computational schemes are presented and verified by a benchmark example. Finally, an application is shown, where the reinforcement layout of a reinforced concrete bridge structure is optimized by minimizing the crack widths at the reinforcement layers in order to improve the durability of the structure. A nonlinear finite element (FE) model is used to compute the uncertain crack patterns and the load bearing capacity. The stochastic objective function and the failure probability constraint are approximated by neural network based surrogate models.