values of the random variables. Better prediction can be achieved by using a MPP based
safety factor.
When the target probability failure is very low, the RBDO using double loop is
computationally prohibitive. RBDO using coarse MCS and multi-fidelity technique can
also be computationally very expensive due to multiple DRS construction and
corrections. Converting RBDO to deterministic optimization becomes computationally
attractive. The beam design with strength constraint with a target probability of failure of
0.0000135 is shown in Table 7-4. It is seen that the method provides an acceptable design
within one design iteration. The total computational cost is two deterministic
optimizations, each followed by a reliability analysis.
Table 7-4. Design history of RBDO based on sequential deterministic optimization with
probabilistic sufficiency factor under strength constraint for target probability
of failure of 0.000013 5
Probabilistic Minimize obj ective function F while P > 3 or 0.0013 5 > pof
sufficiency .Objective Pof/ Safety factor from MCS of
factorOptimafunction Fwt 10O samples
Initial
w=1.9574,
design '7.6630 0.5001 1 55/0.63 52549
1 t- 3.9149
(s = 1.0)
s2= S1
w=2.2770,
0.6352549= '10.3698 0.0000130/1.0006877
t-4.5541
1.5741712
Reliability-Based Design Optimization Using Coarse MCS with Probabilistic
Sufficiency Factor
The beam design with a target probability of failure of 0.0000135 was repeated
here, so that the previous response surface and the optimum in Table 7-2 is used here as
initial design. The design process and ranges of DRS are shown in Table 7-5 and 7-6. It is
seen that the target reliability is achieved in two design iterations of low fidelity DRS