Metamodel-based importance sampling for structural reliability analysis

05/03/2011
by   V. Dubourg, et al.
0

Structural reliability methods aim at computing the probability of failure of systems with respect to some prescribed performance functions. In modern engineering such functions usually resort to running an expensive-to-evaluate computational model (e.g. a finite element model). In this respect simulation methods, which may require 10^3-6 runs cannot be used directly. Surrogate models such as quadratic response surfaces, polynomial chaos expansions or kriging (which are built from a limited number of runs of the original model) are then introduced as a substitute of the original model to cope with the computational cost. In practice it is almost impossible to quantify the error made by this substitution though. In this paper we propose to use a kriging surrogate of the performance function as a means to build a quasi-optimal importance sampling density. The probability of failure is eventually obtained as the product of an augmented probability computed by substituting the meta-model for the original performance function and a correction term which ensures that there is no bias in the estimation even if the meta-model is not fully accurate. The approach is applied to analytical and finite element reliability problems and proves efficient up to 100 random variables.

READ FULL TEXT

page 7

page 15

page 19

research
04/18/2011

Metamodel-based importance sampling for the simulation of rare events

In the field of structural reliability, the Monte-Carlo estimator is con...
research
04/19/2011

Reliability-based design optimization using kriging surrogates and subset simulation

The aim of the present paper is to develop a strategy for solving reliab...
research
02/13/2015

Polynomial-Chaos-based Kriging

Computer simulation has become the standard tool in many engineering fie...
research
10/22/2018

Surrogate modeling based on resampled polynomial chaos expansions

In surrogate modeling, polynomial chaos expansion (PCE) is popularly uti...
research
08/04/2022

Reliability analysis of discrete-state performance functions via adaptive sequential sampling with detection of failure surfaces

The paper presents a new efficient and robust method for rare event prob...
research
11/30/2022

Learning non-stationary and discontinuous functions using clustering, classification and Gaussian process modelling

Surrogate models have shown to be an extremely efficient aid in solving ...
research
07/28/2023

Seeking the Yield Barrier: High-Dimensional SRAM Evaluation Through Optimal Manifold

Being able to efficiently obtain an accurate estimate of the failure pro...

Please sign up or login with your details

Forgot password? Click here to reset