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Frontiers of Environmental Science & Engineering

ISSN 2095-2201

ISSN 2095-221X(Online)

CN 10-1013/X

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Front. Environ. Sci. Eng.    2014, Vol. 8 Issue (5) : 767-775    https://doi.org/10.1007/s11783-014-0632-6
RESEARCH ARTICLE
Seismic vulnerability assessment of water supply network in Tianjin, China
Yanxi CHEN1(), Zhiguang NIU1(), Jiaqi BAI2, Yufei WANG3
1. Department of Environmental Engineering, College of Environmental Science & Engineering, Tianjin University, Tianjin 300072, China
2. China Aerospace Construction Group Co. Ltd, Beijing 100071, China
3. China Beijing Environment Exchange, Beijing 100033, China
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Abstract

The water supply network (WSN) system is a critical element of civil infrastructure systems. Its complexity of operation and high number of components mean that all parts of the system cannot be simply assessed. Earthquakes are the most serious natural hazard to a WSN, and seismic risk assessment is essential to identify its vulnerability to different stages of damage and ensure the system safety. In this paper, using a WSN located in the airport area of Tianjin in northern China as a case study, a quantitative vulnerability assessment method was used to assess the damage that the water supply pipelines would suffer in an earthquake, and the finite element software ABAQUS and fuzzy mathematic theory were adopted to construct the assessment method. ABAQUS was applied to simulate the seismic damage to pipe segments and components of the WSN. Membership functions based on fuzzy theory were established to calculate the membership of the components in the system. However, to consider the vulnerability of the whole system, fuzzy cluster analysis was used to distinguish the importance of pipe segments and components. Finally, the vulnerability was quantified by these functions. The proposed methodology aims to assess the performance of WSNs based on pipe vulnerabilities that are simulated and calculated by the model and the mathematical method based on data of damage. In this study, a whole seismic vulnerability assessment method for a WSN was built, and these analyses are expected to provide necessary information for a mitigation plan in an earthquake disaster.
Keywords water supply network      seismic vulnerability assessment      finite element      fuzzy mathematics     
Corresponding Author(s): Zhiguang NIU   
Issue Date: 20 June 2014
 Cite this article:   
Yanxi CHEN,Zhiguang NIU,Jiaqi BAI, et al. Seismic vulnerability assessment of water supply network in Tianjin, China[J]. Front. Environ. Sci. Eng., 2014, 8(5): 767-775.
 URL:  
https://academic.hep.com.cn/fese/EN/10.1007/s11783-014-0632-6
https://academic.hep.com.cn/fese/EN/Y2014/V8/I5/767
Fig.1  Network map of water supply pipelines
pipe diameter/mm frequency β
rad/s Hz
200 167 27 0.00060
300 249 40 0.00042
400 323 51 0.00031
600 453 72 0.00022
800 560 89 0.00018
Tab.1  First-order natural frequency and stiffness damping coefficient of different pipe diameters of a section of ductile cast iron pipe
Fig.2  Status of the pipe wall when the largest tensile stress occurs
Fig.3  Status of the pipe joint when largest tensile stress occurs
damage status mise stress /MPa function
no damage 3.5 μ˜1(t)={ 10≤ t<3.5 6−t 6−3.53.5≤t<60others
critical damage 6 μ˜2(t)={ t −3.5 6−3.53.5≤t<6 9− t9−66≤t <90others
completely damaged 9 μ˜3(t)={ 0t <6t− 69−66≤t<91t≥9
Tab.2  Seismic damage criterion and the membership functions of the joint of pipe segment
block total water consumption average water consumption water weight value total distance average distance distance weight value comprehensive weight value
1 864 144 0.0311 2600 433 0.5303 0.2807
2 6282 3141 0.6777 1900 950 0.2418 0.4597
3 9475 1053 0.2272 24400 2711 0.0847 0.1560
4 6446 143 0.0309 107300 2384 0.0963 0.0636
5 308 154 0.0332 9800 4900 0.0469 0.0401
sum 23375 4635 136200 11378 1.0000
Tab.3  Weights of the blocks
block cell
1 1, 2, 37, 38, 41, 42
2 34, 36
3 3, 6, 7, 11, 13, 16, 33, 39, 43
4 8, 9, 10, 12, 14, 15, 17−32, 35, 40, 44−64
5 4,5
Tab.4  Cells in each block
block ∑μ comprehensive weight amount of components vulnerability
1 2.7568 0.2807 21 0.0368
2 1.8768 0.4597 8 0.1079
3 3.3436 0.1560 32 0.0163
4 6.9654 0.0636 143 0.0031
5 0.4160 0.0401 6 0.0028
Tab.5  Component vulnerability
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