Locating design point in structural reliability analysis by introduction of a control parameter and moving limited regions

Abstract In reliability analysis, computation of reliability index and finding design point is still a challenge. In this paper a new efficient reliability algorithm to locate design point is proposed. The proposed algorithm takes benefit from two significant means in its efficient search for the design point. One means is an updating rule by which the candidate of design point is updated and moved towards real design point. The criteria of updating in this rule are designed such that the candidate moves on an effective general path towards real design point. The other means is the introduction of a control parameter by which the search process at each iteration is limited to a relatively small region. This parameter controls the candidate of design point on its defined general path and does not let it leave the path. These two means have made the proposed algorithm very reliable in finding design point. Through numerical examples the accuracy and efficiency of the proposed algorithm is shown.

[1]  A. Naess,et al.  System reliability analysis by enhanced Monte Carlo simulation , 2009 .

[2]  G. I. Schueller,et al.  Efficient Monte Carlo simulation procedures in structural uncertainty and reliability analysis - recent advances , 2009 .

[3]  David K. E. Green,et al.  Efficient Markov Chain Monte Carlo for combined Subset Simulation and nonlinear finite element analysis , 2017 .

[4]  B. Sudret,et al.  Metamodel-based importance sampling for structural reliability analysis , 2011, 1105.0562.

[5]  Arthur W. Lees,et al.  Efficient robust design via Monte Carlo sample reweighting , 2007 .

[6]  S. Adhikari,et al.  Reliability analysis of uncertain dynamical systems using correlated function expansion , 2011 .

[7]  Qiang Yu,et al.  Monte Carlo simulation of grain growth of single-phase systems with anisotropic boundary energies , 2009 .

[8]  Mohsen Ali Shayanfar,et al.  A new adaptive importance sampling Monte Carlo method for structural reliability , 2013 .

[9]  Ping Yi,et al.  Non-Gradient–Based Algorithm for Structural Reliability Analysis , 2014 .

[10]  L. C. Matioli,et al.  HLRF–BFGS optimization algorithm for structural reliability , 2015 .

[11]  R. Rackwitz Reliability analysis—a review and some perspectives , 2001 .

[12]  A. Kiureghian,et al.  Optimization algorithms for structural reliability , 1991 .

[13]  F. Grooteman Adaptive radial-based importance sampling method for structural reliability , 2008 .

[14]  Chan Ghee Koh,et al.  First-Order Reliability Method for Structural Reliability Analysis in the Presence of Random and Interval Variables , 2015 .

[15]  Mohsen Ali Shayanfar,et al.  An efficient reliability algorithm for locating design point using the combination of importance sampling concepts and response surface method , 2017, Commun. Nonlinear Sci. Numer. Simul..

[16]  Dixiong Yang Chaos control for numerical instability of first order reliability method , 2010 .

[17]  H. Dai,et al.  Low-discrepancy sampling for structural reliability sensitivity analysis , 2011 .

[18]  Yi Gao,et al.  Unified reliability analysis by active learning Kriging model combining with Random‐set based Monte Carlo simulation method , 2016 .

[19]  Sondipon Adhikari,et al.  Sensitivity based reduced approaches for structural reliability analysis , 2010 .

[20]  Sondipon Adhikari,et al.  Reliability Analysis Using Parabolic Failure Surface Approximation , 2004 .

[21]  R. Grandhi,et al.  Safety index calculation using intervening variables for structural reliability analysis , 1996 .

[22]  Bijan Kumar Roy,et al.  Reliability-based-design-optimization of base isolated buildings considering stochastic system parameters subjected to random earthquakes , 2013 .

[23]  A. M. Hasofer,et al.  Exact and Invariant Second-Moment Code Format , 1974 .

[24]  A. Kareem,et al.  Efficacy of Averaging Interval for Nonstationary Winds , 2014 .

[25]  Ikjin Lee,et al.  Post optimization for accurate and efficient reliability‐based design optimization using second‐order reliability method based on importance sampling and its stochastic sensitivity analysis , 2016 .

[26]  Jinxin Gong,et al.  A robust iterative algorithm for structural reliability analysis , 2011 .