RELIABILITY OPTIMIZATION OF STRUCTURES USING PROBABILISTIC SUFFICIENCY FACTOR AND CORRECTION RESPONSE SURFACE

Reliability based design optimization for low failure probability often requires millions of function analyses. Response surface approximation of the response functions (ARS: Analysis Response Surface) is often used to reduce the cost of failure probability calculations. Failure probabilities obtained from numerical sampling schemes are noisy and not suited for gradient-based optimization. To overcome this, response surfaces have been fitted to the failure probability of the designs (DRS: Design Response Surface) as a function of the design variables and used in optimization. Two shortcomings of the approach are (i) the ARS fitting is extremely expensive for a large number of variables, especially for the high accuracy required to obtain very accurate reliability estimates and (ii) DRS introduces fitting errors that affect the tails of the distributions which are significant for the low failure probabilities. This paper investigates an approach to obtain high accuracy reliability estimates using probabilistic sufficiency factor and correction response surface. The method is demonstrated using a thin walled box beam structure designed for minimum weight with failure probability constraints. The design is subjected to buckling, strength and displacement constraints. Two methods to correct low fidelity analyses are investigated. For the example problem, the local corrections applied to the analysis models used in probabilistic sufficiency factor calculations was found to be more effective than applying correction response surface to the