Topology optimization of structures with interval random parameters

The aim of this paper is to present a robust topology optimization methodology for structures with imprecise probability uncertainty. In this paper, the imprecise probability uncertainties are treated with an interval random model, in which the probability variables are used to model the uncertain parameters and some distribution parameters of probability variables are expressed as interval variables instead of a precise value. In the presented methodology, the deterministic topology optimization techniques and the hybrid stochastic interval perturbation method (HSIPM) are combined to obtain robust topology designs for structures with interval random parameters. The exploitation of HSIPM transforms the problem of topology optimization with interval random parameters into an augmented deterministic topology optimization problem. This provides a computationally cheap alternative to Monte Carlo-based optimization algorithms. Several numerical examples are presented to demonstrate the effectiveness of the proposed method.