An improved first order reliability method based on modified Armijo rule and interpolation-based backtracking scheme

Abstract

Hasofer-Lind and Rackwtiz-Fiessler (HLRF) method is an efficient iterative algorithm for locating the most probable failure point and calculating the first order reliability index in structural reliability analysis. However, this method may encounter numerical instability problems for high nonlinear limit state function (LSF). In this paper, an improved HLRF-based first order reliability method is developed based on a modified Armijo line search rule and an interpolation-based step size backtracking scheme to improve the robustness and efficiency of the original HLRF method. Compared with other improved HLRF-based methods, the proposed method can not only guarantee the global convergence but also adaptively estimate some sensitive algorithm parameters, such as initial step size, step-size reduction coefficient, using the current known iterative information. Ten selected examples with high nonlinear LSFs are used to compare the robustness and efficiency of the proposed method with the original HLRF method and the improved HLRF (iHLRF) method. Results indicate that the proposed method is not only more computationally efficient but also less sensitive to the remaining user-defined algorithm parameters than the iHLRF method.

Improved Hybrid Response Surface Method Based on Double Weighted Regression and Vector Projection

“…For the rst-order reliability method, the problem of robustness and e ciency has always been the core problem of the method. In order to improve its robustness and eciency, researchers have put forward their own ideas [4][5][6][7][8]. However, in general, the drawbacks of these methods have not been e ectively resolved.…”