Using Monte Carlo and Directional Sampling combined with an Adaptive Response Surface for system reliability evaluation L. Schueremans, D. Van Gemert luc.schueremans@bwk.kuleuven.ac.be,
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Using Monte Carlo and Directional Sampling combined with an Adaptive Response Surface
for system reliability evaluation
L. Schueremans, D. Van Gemertluc.schueremans@bwk.kuleuven.ac.be,
dionys.VanGemert@bwk.kuleuven.ac.be
Department of Civil Engineering
KULeuven, Belgium
Praha
Euro-Sibram, June 24 tot 26, 2002, Czech Republic
IntroductionFramework:
– Ph. D. “Probabilistic evaluation of structural unreinforced masonry”,
– Ongoing Research: “Use of Splines and Neural Networks in structural reliability - new issues in the applicability of probabilistic techniques for
construction technology”.
Target: – obtain an accurate value for the global pf, accounting for the exact PDF of
the random variables;
– minimize the number of LSFE, which is of increased importance for complex structures;
– remain workable for a large number of random variables (n). In practice, the number of LSFE should remain proportional with the number of random variables (n).
IntroductionLevel II and Level III methods:
Leve l D efin i tion
III L e v e l I I I m e t h o d s s u c h a s M o n t e C a r l o ( M C ) s a m p l i n g a n d N u m e r i c a lI n t e g r a ti o n ( N I) a r e co n s i d e r e d m o s t a c c u r a t e . T h e y c o m p u t e t h e e x a c tp r o b a b i l i t y o f f a i l u r e o f t h e w h o l e s t r u c t u r a l s y s t e m , o r o f s t r u c t u r a le l e m e n ts , u s i n g t h e e x a c t p r o b a b i li t y d e n s i t y f u n c ti o n o f a ll r a n d o m v a r i a b l e s .
II Leve l II me thods such a s F O R M and S O R M compu te the p robab il i ty o f fa i l ure bym eans o f a n idea l iza t ion o f the l im it s ta te func tion w here the p robab il i ty de ns i tyfunc tions o f a l l ra ndo m var iab les a re app roxi m ated by equiva lent no rmald is tr ibution func tions .
I Leve l I m e thods ve r ify w he the r o r no t the re l iab il i ty o f the s truc ture is suffic ien tins tead o f co m puting the p robab il i ty o f fa i l ure e xp lic i tly. In p rac tice this is o ftenca rr ied out by means o f pa r tia l sa fe ty fac to rs .
T a b l e 1 : Leve ls fo r the ca lc ula tion o f s truc tura l sa fe ty va lues (E C 1 , 1994 ; JC S S , 1982 )
p P g f df
g
X x xX
X
00
. . .
Introduction - reliability methodsR e l i a b i l i t y m e t h o d s - L e v e l ( I , I I , I I I ) - D i r e c t / I n d i re c t ( D , I D )
In tegra tionm e thods
A na lytica l o r N um erica l Integra tion (A I/N I, III, D)
D irec tiona l Integra tion (D I, III, D )
S a m plingm e thods
( Im por tance S a m pling) M onte C ar lo ( IS M C , III, D )
( Im por tance ) D irec tiona l S ampli ng ( ID S , III, D )
F O R M /S O R Mm e thods
F irs t O rde r S econd M om en t re l iab i l i ty m e thod (F O S M , II, D )
F irs t O rde r and S econd O rder R e liab i l i ty M ethod ( F O R M /S O R M , II, D )i n co m bi na tion w i th a sys tem ana lys is ( F O R M /S O R M -S A , III, D )
C o m binedm e thods us i ngA dap tiveR esponseS urfacetechn iques
D irec tiona l A dap tive R esponse surface S ampling ( D A R S , III, D-ID )
M onte C ar lo A dap tive R esponse surface S ampli ng (M C A R S , III, D -ID )
F O R M w ith an A dap tive Response S urface (F O R M A R S , II, D-ID ) i ncombina tion w ith a sys te m ana lys is (F O R M A R S -S A , III, D -ID)
T a b l e 2 : O verv iew o f re l iab i l i ty me thods fo r a leve l III re l iab i l i ty ana lys is
#LSFE~9n
#LSFE~3/pf VI
#LSFE~cte.n
Methods for System Reliability using an Adaptive Response Surface
Real structure: high degree of mechanical complexity, numerical algorithms, non-linear FEM
Response Surface: low order polynomial, Splines, Neural Network,...
Reliability analysis
Optimal scheme: DARS or MCARS+VI
DARS: Matlab 6.1 [Schueremans, 2001], Diana 7.1 [Waarts,2000]
MCARS+VI: Matlab 6.1 [Schueremans, 2001]
u1
u2
g1(u1,u2)<0
g2(u1,u2)<0
g3(u1,u2)<0
g4(u1,u2)<0
unsafe
unsafe
unsafe
unsafe
safeg3>0 g1>0
g4>0g2>0
Component reliability:
pf,g1= 0.0161, g1=2.14pf,g2= 0.0161, g2=2.14pf,g3= 0.0062, g3=2.50pf,g4= 0.0062, g4=2.50
System reliability:
pf= 0.0446=1.70
DARS-Directional Adaptive Response surface Sampling
u1u2 g1(u1,u2)<0
g2(u1,u2)<0
g3(u1,u2)<0
g4(u1,u2)<0
unsafe
unsafeunsafe
unsafe
u2
u1
fU1,U2(u1,u2)
fU1,U2(u1,u2)
g1(u1,u2)<0
unsafesafe
g u u
g u u u uu u
g u u u uu u
g u u u u
g u u u u
1 2
1 1 2 1 22 1 2
2 1 2 1 22 1 2
3 1 2 1 2
4 1 2 2 1
2 0 0 12
2 0 0 12
2 5 22 5 2
, m in
, . .
, . .
, ., .
DARS -Directional Adaptive Response surface Sampling
Step 1: - Evaluate the LSF forthe origin in the u-space;- Search the roots ofthe limit state functionfor the principaldirections in the u-space (n=2):- [1,0];[0,1];[-1,0];[0,-1]With the root-findingalgorithm, this requiresapproximately 3 to 4LSFE
u1
u2
[0,0] =
min
[0,0]
[-0,0]
[0,-0]
N=5=2n+1, #LSFE=21
min = 3.5, =2.85
DARS -Directional Adaptive Response surface Sampling
Step 2: Fit a response surfacethrough these data inthe x-space and theresulting outcome Y,using a least squaresalgorithm.
u1
u2
gRS,1= 1.65-0.13u1
2
-0.13u22
gRS,1 = 0
min = 3.5
add = 3.0
DARS -Directional Adaptive Response surface Sampling
Step 3: iter. procedure required accuracyPerform DS on theResponse Surface:
If i,RS < min+
add
Calculatepi(LSF)=2(i,LSF,n)Update the responsesurface with new data
ElseCalculatepi(RS)= 2(i,RS,n)
u1
u2
gRS,2= 0.92+0.046u1
-0.023u2-0.074u1u2
-0.097u12-0.084u2
2
gRS,2 = 0
min = 2.05
add = 3.0
DARS -Directional Adaptive Response surface Sampling
u1
u2
gRS,3 = 0
pf N = 14
add=3
min=2
p
L S F E
N
f
0 0 2 8
1 9 1
5 1
1 4
2 0 5
.
.
#
.m in
Step 3:
Monte Carlo Variance Increase on the Response Surface (vi).
Sampling function: h=n-0.4
IF |gRS(v i)|<|g,add|
calculate gLSF(vi)
update RS
update g,add
Else
.
u
gLSFRS
RS
add
g,add
g,add
i
gRS,i
gLSF,i
g,i
MCARS+VI Monte Carlo Adaptive Response surface Sampling+Variance Increase
p I g
f
hi LSF i vv
vu
v
0
p I g
f
hi RS i vv
vu
v
0
DARS and MCARS+VI
The number of direct LSFE remains proportional to the number of random variables (n),
There is no preference for a certain failure mode. All contributing failure modes are accounted for, resulting in a safety value that includes the system behavior, thus on level III.
Safety of masonry Arch
R ando mvariab les
P robab il i tydens i tyfunc tion
Mean va lue µ
S tandarddev ia tion σ
C oeffic ient o fva r ia tion V [% ]
x 1 = r 0 [m ] N or m al 2 .5 0 .02 0 .8
x 2 = t [m ] N or m al 0 .16 0 .02 12
x 3= dr [m ] N or m al 0 0 .02 /
x 4 = F [N] Lognormal 750 150 20
T a b le : R andom var iab les and the ir pa ra m ete rs
Safety of masonry Arch
To evaluate the stability of the arch, the thrust line method is used (Heyman, 1982), which is a Limit Analysis. Following assumptions are made:– blocs are infinitely resistant,
– joints resist infinitely to compression
– joints do not resist to traction
– joints resist infinitely to shear
An external program Calipous is used for the Limit State Function Evaluations [Smars, 2000]
Safety of masonry ArchFailure modes - limit states - limit analysis based on thrust lines
g g
S
m in
X
X
1
1
S X 1 S X 1
g X 1 g X 1
Safety of masonry Archprocedure pf #LSFE (time) Accuracyreliability analysis – initial random values: d~N(0.16,(0.02)²)DARSMCARS+VI
1.261.25
0.110.11
43 (17 min)23 (12 min)
()=0.15()=0.15
reliability analysis – increased accuracy on thickness: d~N(0.16,(0.005)²)DARSMCARS+VI
3.553.46
1.9 10-4
2.7 10-434 (13 min)56 (21 min)
V()=0.05V()=0.05
reliability analysis – increased mean value for thickness: d~N(0.21,(0.02)²)DARSMCARS+VI
3.693.67
10-4
1.2 10-445 (17 min)23 (12 min)
V()=0.05V()=0.05
Table 2: Outcome of reliability analysis for masonry arch – initial parameters and update
Safety of masonry Arch
Figure: DARS-outcome - reliability vernus number of samples N
Increased thickness: t: =0.21 m; =0.02 mN=192: =3.72, pf=1.0 10-4
Increased accuracy: t: =0.16 m; =0.005 mN=273: =3.44, pf=2.9 10-4
Initial survey: t: =0.16 m; =0.02 mN=371: =1.26, pf=1.0 10-1
Number of Samples N
95% Confidence interval
T=3.7
Conclusions• Focus was on the use of combined reliability methods to
obtain an accurate estimate of the global failre probability of a complete structure, within an minimum number of LSFE.
• A level III method is presented and illustrated: (DARS/MCARS+VI
• Ongoing research: Splines and Neural Network instead of low order polynomial for Adaptive Response Surface (ARS).
• Acknowlegment: IWT-VL (Institute for the encouragement of Innovation by Science and Technology in Flanders).
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