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Proceedings of the Second International Symposium on Networking and Network Security (ISNNS ’10)
Jinggangshan, P. R. China, 2-4, April. 2010, pp. 043-046
Research on the Safety Assessment of Bridges
Based on Fuzzy-Neural Network
Bo Wang 1 , Xuzheng Liu 2 , and Chao Luo 3
1 School of Information Science And Medium, JingGangShan University,Ji’an 343009, China
E-mail:woboxp@126.com
2 School of Civil Engineering and Architecture, East China Jiaotong University, Nanchang 330013,China
Email:urbwolf@126.com
3 Morden Education Technology Center, Jinggangshan UniverSity, Ji’an 343009, China
Email:luochao6668@163.com
Abstract--Fuzzy theory is integrated with Artificial Neural
Network to create a bridge safety assessment model,
through which the Fuzzy-Neural Network is improved in
the light of sample data simulation. First, determine
network layers interms of the seven critiria for bridge
safety assessment. Then enter sample data at the input layer;
study sample at the fuzzy reasoning layer by BP calculation
method; obtain professional experience and ways of
thinking about bridage safety assessment via the
network. Finally, compare the assessment results from the
network with those from professionals. The comparison
proves the artificial fuzzy-neural network's feasibility and
efficiency in assessing bridge safety.
Index Terms --fuzzy-neural network; bridge safety
assessment; BP network; suspended bridge
I. INTRODUCTION
Artificial Neural Network, also known as Neural
Network, is widely applied to pattern recognization,
automatic control, image processing and language
identification. Neural Network, integrated with fuzzy
theory, is greatly enhanced to a better processing of
information, information both precise and fuzzy; thus is
the fuzzy system escalated to be known as adaptive fuzzy
system. Efforts have been made to employ neural
network to the bridge safety assessment in [1][2][3][4][5].
BP neural network is utilized to detect the structural
damage by Wu[6][7] and the others[8][9][10].
Kaminski[11] has made a research into the examination
of girder steel in the light of neural network. Kaminski’s
neural network has been verified through the tests of
absolute frequency[12][13][14], relative frequency and
the synthesized frequency to guarantee the solid
feedback on the identification of the damage[15][16][17].
A comparative inadequacy can be seen when the
application of fuzzy-neural network to the bridge safety
assessment is put in the concerned domain world wide.
The paper is therefore dedicated to a specific reliability
bridge assessment resolutions based on the fuzzy-neural
network and employed to Bridge A.
II.
SAFETY ASSESSMENT MODEL
A. The Criteria for the Model
A set of criteria has to be established before the
This article is funded by Education Department,province
Jiangxi,GJJ09143.
© 2010 ACADEMY PUBLISHER
AP-PROC-CS-10CN006
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assessment can be carried out to a specific subject. As
main cable sustains the most load of a suspended
bridge, its strained condition and its lineshape is of
significance to the entire bridge safety. The bridge
tower is at always left at the bending moment and
shaft force, which makes another inconvenient
criterion the deviation of the tower-top and the
capacity of the tower to the tension. The other
influential criteria are the lineshape of the stiff girder,
the internal force and the exterior examination of the
lift lock. Now we come to criteria covers seven
significant aspects to a feasible safety assessment
model: exterior examination (F 1 ), bridge tower
deviation (F 2 ), bridge tower capacity to tension (F 3 ),
lineshape of the stiff girder (F 4 ), internal force of the
lift lock (F 5 ), lineshape of main cable (F 6 ) and the
tension of the anchor cable (F 7 )
x 1
x 2
x i
μ 11
μ 12
μ 1k
μ 21
μ 22
μ 2k
μ i1
μ i2
μ ik
Safety Assessment
F 1 F 2 F 3 F 4 F 5 F 6 F 7
Figure 1.
Figure 2.
Safety assessment model
BP hide ylayer
Fuzzification Fuzzy reasoning Disfuzzification
Fuzzy-neural network
A
B
C
D
E