A new sensitivity-based robust optimization of structures under bounded type uncertainty

This paper deals with robust design optimization (RDO) of structures when uncertainty information about system parameters is limited. For such parameters, neither the statistical moments, nor the probability distribution is available. Rather, based on practical considerations, only bounds of variations of such parameters under uncertainty can be set, which is in-turn used to model them as uncertain-but-bounded (UBB) type. In recent years, convex programming (CP) approaches are used to solve RDO problem with UBB parameters. However, these approaches generally do not consider sensitivity information while formulating an RDO problem. But, it is well accepted that sensitivity is the key focus of an RDO as by definition a robust design should be least sensitive to uncertainty effects. Thus, a new sensitivity-importance based RDO formulation is proposed in this paper, where a new sensitivity index is defined and minimized along with the usual cost function subjected to a dispersion-related constraint. Then, the proposed RDO is solved through usual CP framework. The improvement by the proposed approach is demonstrated taking four application examples. The results indicate that the proposed approach yields more robust solutions compare to the conventional approaches.