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.