Environmental Engineering Reference
In-Depth Information
where δ Y1Y2 is the Pearson correlation between (Y 1 , Y 2 ); δ 12 is the Pearson correlation between
(X 1 , X 2 ); and f ( x 1 , x 2 ; δ 12 ) is the bivariate standard normal PDF defined in Equation 1.37 :
T
1
x
x
1
δ
x
x
(
)
(
) =
1
12
1
1 2
f
xx
,
;
δ
exp.
05
2
π
×−
1
δ
(1.109)
1212
δ
1
2
12
2
and μ Y i and σ Y i are the mean and standard deviation of Yi, i , respectively:
(
) × ()
2
()
× ()
()
1
1
µ
=
F
Φ
x
ϕ
x
d
x
σ
=
F
Φ
x
µ
ϕ
xx
d
(1.110)
Y
i
Y
i
Y
i
i
i
−∞
ϕ( x ) is the univariate standard normal PDF ( Equation 1.3 ) .
This relation between δ Y1Y2 and δ 12 is plotted on the left plot in Figure 1.32 . When evalu-
ating the CDF transform F
Φ x the parameters ( a X1 , b X1 , a Y1 , b Y1 ) in Table 1.15 are
used. The same approach is followed for evaluating F
1 ()
[
],
1
Φ x It is clear that δ Y1Y2 and δ 12
are not identical. Moreover, even though δ 12 spans the full range −1.0-1.0, δ Y1Y2 only spans
a nonfull range of −0.91-0.98. This gets even worse if we reduce a X1 and a X2 in Table 1.15
by a factor of 2 ( a X1 = 1.491/2 = 0.754; a X2 = 2.506/2 = 1.253). By doing so, the standard
deviations of (Y 1 , Y 2 ) will increase. The resulting relation between δ Y1Y2 and δ 12 is plotted on
the right plot in Figure 1.32 . It is seen that the lower bound for δ Y1Y2 is now only −0.4! The
inability of the CDF transform approach to reproduce strong negative correlations among
physical variables is arguably the most critical weakness of this approach.
2 ()
[
].
2
1.7 real eXaMPle
In this section, the construction of multivariate probability distributions of soil param-
eters will be demonstrated using the Clay/10/7490 database compiled by Ching and Phoon
(a)
1
(b)
1
a X1 and a X2
reduced by factor of 2
0.5
0.5
0
0
-0.5
-0.5
-1
-1
-1
-0.5
0
δ 12
0.5
1
-1
-0.5
0
δ 12
0.5
1
Figure 1.32 Relation between δ Y1Y2 and δ 12 (a) for parameters given in Table 1.15 ; and (b) a X1 and a X2 reduced
by a factor of 2. The dashed lines are the 1:1 lines.
 
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