Identification of critical samples of stochastic processes towards feasible structural reliability applications

Identification of critical samples of stochastic processes towards feasible structural reliability applications

WoS Article

This paper contributes to the structural reliability problem by presenting a novel approach that enables for identification of stochastic oscillatory processes as a critical input for given mechanical models. The proposed method is based on a graphical representation of such processes utilizing state of the art image processing and pattern recognition techniques, leading to a set of finite rules that consistently identifies those realizations of stochastic processes that would lead to a critical response of a given mechanical model. To examine the validity of the suggested method, large sets of realizations of artificial non-stationary processes were generated from known models, several criteria for critical response were formulated and the results were statistically evaluated. The promising results suggest important applications that would dramatically decrease computational costs e.g. in the field of probabilistic seismic design. Further examination may lead to a formulation of a new class of importance sampling techniques.

This paper contributes to the structural reliability problem by presenting a novel approach that enables for identification of stochastic oscillatory processes as a critical input for given mechanical models. The proposed method is based on a graphical representation of such processes utilizing state of the art image processing and pattern recognition techniques, leading to a set of finite rules that consistently identifies those realizations of stochastic processes that would lead to a critical response of a given mechanical model. To examine the validity of the suggested method, large sets of realizations of artificial non-stationary processes were generated from known models, several criteria for critical response were formulated and the results were statistically evaluated. The promising results suggest important applications that would dramatically decrease computational costs e.g. in the field of probabilistic seismic design. Further examination may lead to a formulation of a new class of importance sampling techniques.

Critical excitation; Identification problem; Image processing; Importance sampling; Pattern recognition; Reliability analysis; Stochastic process

Critical excitation; Identification problem; Image processing; Importance sampling; Pattern recognition; Reliability analysis; Stochastic process

PODROUŽEK, J.; BUCHER, C.; DEODATIS, G.

2016

08.01.2015

Elsevier Ltd.

Holandsko

0167-4730

STRUCTURAL SAFETY

47

2

Kingdom of the Netherlands

39

47

9