Civil Engineering Reference
In-Depth Information
a
max
M
100
100
Yes
No
Yes
No
80
80
60
60
40
40
20
20
0
0
5.9 6.1 6.3 6.5 6.7 6.9 7.1 7.3 7.5 7.7
0.12
0.23
0.33
0.43
0.53
g
s
vo
s¢
vo
100
100
Yes
Yes
No
No
80
80
60
60
40
40
20
20
0
0
25
75
120
180
220
280
10 30 50 70 90
130
170
210
kPa
kPa
q
c
D
50
100
100
Yes
Yes
No
No
80
80
60
60
40
40
20
20
0
0
2.5
7.5
12
18
22
28
0.025
0.12
0.22
0.32
0.43
MPa
mm
7.3
Histogram of six variables.
the engineering signifi cance of each parameter and correlation in the model
development part.
To generate a BBN for liquefaction assessment, the local score metrics
are considered for structure learning. The process of learning a network
structure is implemented by maximizing the quality measure of the network
given the training dataset. The quality measure, i.e. metric, can be based on
a Bayesian approach, minimum description length, information and other
criteria (Bouckaert
et al.
2011). The score of the whole network can be
decomposed as the sum (or product) of the score of the individual nodes.
This will allows for local scoring and local search methods. Four searching
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