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III. FUZZY-NEURAL NETWORK MODEL
A fuzzy-neural network is established, in accordance
with the requirements of a feasible safety assessment.
The five layer network is illustrated as Fig1. The first
layer is the Input layer, of which each node signifies one
variable. The nodes amount to 7 on the Input layer. The
second layer with altogether 35 nodes is the Obfuscation
layer for the purpose of obfuscating the input variable.
BP Inclusion layer is known as the third layer serves as
the reflection between the input variable and the output
variable. The 71 nodes are derived from the 35 nodes of
the second layer in the light of Kolmogorov Complexity.
The fourth layer is the Output layer, where the
obfuscated results come forth. The fifth is the
Defuzzification layer, where more precise coming out
can be expected by applying the defuzzification
principles. The five layers mentioned is characterized
with its capacity of self-adjustment.
IV. AN ASSESSMENT INSTANCE
The safety assessment of Bridge A is rendered in the
light of the above network as referred to. Bridge A has a
length of 1187.489 m, with a clearspan of 960 m and a
width of 30 m. As a double- tower suspended bridge, an
assessment is to be carried out to this five-year-old.
A Sample learning
A well-distributed value is vital to a relatively
thorough assessment. Twenty arrays of theoretical
input samples by the auto computing will generate an
output scored by the experts. A hierarchy will manifest
the assessment where A, B, C, D, E different degrees is
to be brought forth through a weight multiplied by a
centesimal criteria.
The input-output is as TabI goes, of which the
weight vector is ω = [ 0108, 01105, 01175, 01114,
01144, 01174, 01208 ]
B Network Training
The ten arrays is input in the neural-fuzzy network,
where a satisfied result is generated with a discrepancy
Ε < 0.00001. Two hundred and forty four learning
processes confirm the parameter value. Thus is
established the Fuzzy-neural network to the safety
assessment of the large-span suspended bridge. In order
to justify the feasibility, randomly generated five arrays
of input samples yield the results in comparison with the
experts’ scoring in TabII.
TABLE I.
Network training sample data
Shape of
the
stiffened
girder
(F 4 )
Shape of
main
cable
(F 6 )
Tension
of the
anchored
cable
(F 7 )
Sample
Exterior
examination
(F 1 )
Tower
displacement
(F 2 )
Tower
stress
(F 3 )
Cable
stress
(F 5 )
1 100 0.05 0.7 0.65 0.01 0.75 0 A
Safety
assessments
Result
2 100 0.02 0.6 0.65 0 0.8 0.01 A
3 95 0.1 0.7 0.7 0.02 0.75 0.01 A
4 90 0.1 0.65 0.8 0.025 0.85 0.02 A
5 85 0.1 0.85 0.9 0.03 1 0.02 B
6 88 0.12 0.9 0.82 0.028 0.95 0.03 B
7 85 0.15 0.75 0.95 0.04 1.05 0.035 B
8 81 0.22 0.95 1.05 0.055 1.12 0.048 B
9 72 0.28 0.9 1.1 0.065 1.13 0.055 C
10 75 0.3 1.1 1.15 0.07 1.1 0.052 C
TABLE.II
Assessments results of verifying sample
Sample F1 F2 F3 F4 F5 F6 F7 Result Export
11 81 .0.2 0.95 1.3 0.02 1.05 0.028 B [0.02,0.97,0.02,0,0]
12 83 0.09 0.81 0.75 0.033 0.89 0.022 B [0.07,0.71,0,0,0.1]
13 91 0.06 0.77 0.81 0.022 0.79 0.021 A [0.95,0.03,0,0,0]
14 70 0.35 1.15 1.12 0.075 1.35 0.074 D [0,0,0,0.97,0]
15 35 0.85 1.55 1.61 0.131 1.59 0.122 E [0,0,0,0,1]
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