On the effects of non-linear elements in the reliability-based optimal design of stochastic dynamical systems

Abstract The aim of the present paper is to study the effects of non-linear devices on the reliability-based optimal design of structural systems subject to stochastic excitation. One-dimensional hysteretic devices are used for modelling the non-linear system behavior while non-stationary filtered white noise processes are utilized to represent the stochastic excitation. The reliability-based optimization problem is formulated as the minimization of the expected cost of the structure for a specified failure probability. Failure is assumed to occur when any one of the output states of interest exceeds in magnitude some specified threshold level within a given time duration. Failure probabilities are approximated locally in terms of the design variables during the optimization process in a parallel computing environment. The approximations are based on a local interpolation scheme and on an efficient simulation technique. Specifically, a subset simulation scheme is adopted and integrated into the proposed optimization process. The local approximations are then used to define a series of explicit approximate optimization problems. A sensitivity analysis is performed at the final design in order to evaluate its robustness with respect to design and system parameters. Numerical examples are presented in order to illustrate the effects of hysteretic devices on the design of two structural systems subject to earthquake excitation. The obtained results indicate that the non-linear devices have a significant effect on the reliability and global performance of the structural systems.

[1]  J. -F. M. Barthelemy,et al.  Approximation concepts for optimum structural design — a review , 1993 .

[2]  James L. Beck,et al.  Reliability Estimation for Dynamical Systems Subject to Stochastic Excitation using Subset Simulation with Splitting , 2005 .

[3]  C. D. Boor,et al.  Computational aspects of polynomial interpolation in several variables , 1992 .

[4]  Ronald L. Wasserstein,et al.  Monte Carlo: Concepts, Algorithms, and Applications , 1997 .

[5]  G. Fadel,et al.  Automatic evaluation of move-limits in structural optimization , 1993 .

[6]  Jack Dongarra,et al.  PVM: Parallel virtual machine: a users' guide and tutorial for networked parallel computing , 1995 .

[7]  Gene H. Golub,et al.  Matrix computations , 1983 .

[8]  Rüdiger Rackwitz,et al.  Optimal design under time-variant reliability constraints , 2000 .

[9]  Hideomi Ohtsubo,et al.  Reliability-Based Structural Optimization , 1991 .

[10]  J. Beck,et al.  Estimation of Small Failure Probabilities in High Dimensions by Subset Simulation , 2001 .

[11]  Peter Kall,et al.  Stochastic Programming Methods and Technical Applications , 1998 .

[12]  Kurt Marti Robust optimal design: A stochastic optimization problem , 2002 .

[13]  Hector A. Jensen,et al.  Structural optimization of linear dynamical systems under stochastic excitation: a moving reliability database approach , 2005 .

[14]  Christina Bloebaum,et al.  Variable move limit strategy for efficient optimization , 1991 .

[15]  John E. Dennis,et al.  Numerical methods for unconstrained optimization and nonlinear equations , 1983, Prentice Hall series in computational mathematics.

[16]  T. T. Soong,et al.  Random Vibration of Mechanical and Structural Systems , 1992 .

[17]  John Dalsgaard Sørensen,et al.  Reliability-Based Optimization in Structural Engineering , 1994 .

[18]  R. Tapia,et al.  Nonparametric Function Estimation, Modeling, and Simulation , 1987 .

[19]  Jianye Ching,et al.  Local estimation of failure probability function and its confidence interval with maximum entropy principle , 2007 .

[20]  K. Bathe Finite Element Procedures , 1995 .

[21]  E. Polak,et al.  Reliability-based optimal design using sample average approximations , 2004 .

[22]  A. J. Booker,et al.  A rigorous framework for optimization of expensive functions by surrogates , 1998 .

[23]  Manolis Papadrakakis,et al.  Reliability-based structural optimization using neural networks and Monte Carlo simulation , 2002 .

[24]  G. I. Schuëller,et al.  Some Basic Principles of Reliability-Based Optimization (RBO) of Structures and Mechanical Components , 1998 .

[25]  Helmut J. Pradlwarter,et al.  On the dynamic stochastic response of FE models , 2004 .

[26]  J. Douglas Faires,et al.  Numerical Analysis , 1981 .

[27]  Hojjat Adeli,et al.  Advances in Design Optimization , 1994 .

[28]  K. Schittkowski NLPQL: A fortran subroutine solving constrained nonlinear programming problems , 1986 .

[29]  H. Saunders Book Reviews : NUMERICAL METHODS IN FINITE ELEMENT ANALYSIS K.-J. Bathe and E.L. Wilson Prentice-Hall, Inc, Englewood Cliffs, NJ , 1978 .

[30]  Bruce R. Ellingwood,et al.  Formulation of load factors based on optimum reliability , 1991 .

[31]  N. M. Alexandrov,et al.  A trust-region framework for managing the use of approximation models in optimization , 1997 .

[32]  James L. Beck,et al.  Hybrid Subset Simulation method for reliability estimation of dynamical systems subject to stochastic excitation , 2005 .

[33]  Gerhart I. Schuëller,et al.  Reliability-Based Optimization of structural systems , 1997, Math. Methods Oper. Res..

[34]  Virginia Torczon,et al.  Using approximations to accelerate engineering design optimization , 1998 .