Environmental Engineering Reference
In-Depth Information
Table 6.6
Example #2: Two-layer soil layer mass—parameters of the model
Parameter
Notation
Type of PDF
Mean Value
Coefficient of Variation
Upper layer soil thickness
t
1
Deterministic
8 m
-
Lower layer soil thickness
t
2
Deterministic
22 m
-
Upper layer Young's modulus
E
1
Lognormal
50 MPa
20%
Lower layer Young's modulus
E
2
Lognormal
100 MPa
20%
Upper layer Poisson ratio
Uniform
0.3
15%
ν
1
Lower layer Poisson ratio
Uniform
0.3
15%
ν
2
Load #1
P
1
Gamma
0.2 MPa
20%
Load #2
P
2
Weibull
0.4 MPa
20%
Table 6.7
Settlement of a foundation on a two-layer soil mass—reliability results
Threshold
FORM
SORM
Importance Sampling
u
adm
(cm)
P
f ,FORM
P
f ,SORM
P
f , IS
β
β
β
12
1.02
1.09
1.07
1.54
⋅
10
−
1
1.38
⋅
10
−
1
1.42
⋅
10
−
1
[CoV
=
1.27%]
15
1.64
⋅
10
−
2
2.13
1.36
⋅
10
−
2
2.21
1.43
⋅
10
−
2
[CoV
=
1.67%]
2.19
20
1.54
⋅
10
−
4
3.61
1.17
⋅
10
−
4
3.68
1.23
⋅
10
−
4
[CoV
=
2.23%]
3.67
21
3.86
3.94
3.91
5.57
⋅
10
−
5
4.14
⋅
10
−
5
4.53
⋅
10
−
5
[CoV
=
2.27%]
The serviceability of this foundation on a layered soil mass vis-à-vis an admissible settle-
ment is studied. The limit state function is defined by
g
(
X
) =
u
adm
−
u
A
=
u
adm
−
M
FE
(
E
1
,
E
2
, ν
1
, ν
2
,
P
1
,
P
2
),
(6.82)
in which the admissible settlement is chosen to be 12, 15, 20, and 21 cm. First, FORM and
SORM are applied, together with IS at the design point using
n
MCS
= 10
4
samples. The latter
Then PC expansions of the maximal settlement M
PC
are computed using different EDs
of increasing size, namely
n
= 100, 200, 500, and 1000 using the UQLab platform (Marelli
and Sudret, 2014). The obtained expansion is then substituted for in the limit state function
Table 6.8
Settlement of a foundation on a two-layer soil mass—reliability results from PC expansion
(MCS with
n
MCS
=
10
7
)
Threshold
n
=
100
n
=
200
n
=
500
n
=
1000
u
adm
(cm)
P
f
P
f
P
f
P
f
β
β
β
β
12
1.44
⋅
10
−
1
[CoV
=
0.08%]
1.06
1.45
⋅
10
−
1
[CoV
=
0.08%]
1.06
1.44
⋅
10
−
01
[CoV
=
0.08%]
1.06
1.44
⋅
10
−
1
[CoV
=
0.08%]
1.06
15
1.33
⋅
10
−
2
[CoV
=
0.27%]
2.22
1.44
⋅
10
−
2
[CoV
=
0.26%]
2.19
1.45
⋅
10
−
02
[CoV
=
0.26%]
2.18
1.45
⋅
10
−
2
[CoV
=
0.26%]
2.18
20
3.85
3.69
3.66
3.66
5.84
⋅
10
−
5
[CoV
=
4.14%]
1.13
⋅
10
−
4
[CoV
=
2.97%]
1.26
⋅
10
−
04
[CoV
=
2.82%]
1.24
⋅
10
−
4
[CoV
=
2.84%]
21
4.15
3.97
3.90
3.91
1.66
⋅
10
−
5
[CoV
=
7.76%]
3.64
⋅
10
−
5
[CoV
=
5.24%]
4.72
⋅
10
−
05
[CoV
=
4.60%]
4.56
⋅
10
−
5
[CoV
=
4.68%]
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