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This paper presents an effective sequence interval and correlation inverse strategy for the uncertain inverse problem, aiming to identify the uncertainties and non-probabilistic correlations of the structural parameters simultaneously. First, an ellipsoidal convex model is adopted to quantify the uncertainty boundary of the measured responses with limited samples. Then, the uncertain inverse problem based on the ellipsoidal convex model is decoupled into an interval inverse problem and a correlation inverse problem. For the interval inverse problem, a subinterval decomposition analysis method constrained by the ellipsoidal convex model is developed to evaluate the intervals of the structural responses with a low computational cost. For the correlation inverse problem, the correlation propagation equations are derived to calculate the non-probabilistic correlation coefficient matrix of the structural responses. After that, by using optimization algorithms to circularly reduce the errors of the intervals and the correlation coefficients between the measured responses and calculated structural responses, the intervals and the non-probabilistic correlation coefficient matrix of the structural parameters are identified effectively, and an ellipsoidal convex model of the structural parameters can be established eventually. Two numerical examples and one experimental example are investigated to verify the effectiveness and accuracy of the proposed sequence interval and correlation inverse strategy. [ABSTRACT FROM AUTHOR]

In this paper, a new non-probabilistic reliability-based topology optimization (NRBTO) method is proposed to account for interval uncertainties considering parametric correlations. Firstly, a reliability index is defined based on a newly developed multidimensional parallelepiped (MP) convex model, and the reliability-based topology optimization problem is formulated to optimize the topology of the structure, to minimize material volume under displacement constraints. Secondly, an efficient decoupling scheme is applied to transform the double-loop NRBTO into a sequential optimization process, using the sequential optimization & reliability assessment (SORA) method associated with the performance measurement approach (PMA). Thirdly, the adjoint variable method is used to obtain the sensitivity information for both uncertain and design variables, and a gradient-based algorithm is employed to solve the optimization problem. Finally, typical numerical examples are used to demonstrate the effectiveness of the proposed topology optimization method. [ABSTRACT FROM AUTHOR]