Adaptive second order step length algorithm for inverse reliability analysis

Abstract

In order to obtain reliable operating conditions, uncertainties must be taken into account in the mathematical modeling of systems and processes. An uncertainty is measured by the probability of failure of the process, which can be tackled by inverse reliability analysis. This analysis guarantees the achievement of the probabilistic constraints at a specified level. Here, we propose a new methodology for obtaining optimum operating conditions, considering certain probabilistic constraints. The method uses a second order approximation for the calculation of an adaptive step length, in a technique based on steepest descent method, to optimize the performance function. The efficiency of the proposed technique is evaluated in some benchmark problems and in an engineering problem, showing that it outperforms other recent methodologies in convergence capability and stability, while being robust in the choice of control parameters.