CHAPTER 6
 PROBABILISTIC SUFFICIENCY FACTOR APPROACH FOR RELIABILITY-
 BASED DESIGN OPTIMIZATION

 A probabilistic sufficiency factor approach is proposed that combines safety factor

and probability of failure for use in reliability-based design optimization. The

probabilistic sufficiency factor approach represents a factor of safety relative to a target

probability of failure. It provides a measure of safety that can be used more readily than

probability of failure or safety index by designers to estimate the required weight increase

to reach a target safety level. The probabilistic sufficiency factor can be calculated from

the results of Monte Carlo simulation with little extra computation. The chapter presents

the use of probabilistic sufficiency factor with a design response surface approximation,

which fits it as function of design variables. It is shown that the design response surface

approximation for the probabilistic sufficiency factor is more accurate than that for the

probability of failure or for the safety index. The probabilistic sufficiency factor does not

suffer like probability of failure or safety index from accuracy problems in regions of low

probability of failure when calculated by Monte Carlo simulation. The use of

probabilistic sufficiency factor accelerates the convergence of reliability-based design

optimization

 Introduction

 Recently, there has been interest in using alternative measures of safety in

reliability-based design optimization. These measures are based on margin of safety or

safety factors that are commonly used as measures of safety in deterministic design.