water supply risk
TRANSCRIPT
Taeho Choi
Jayong Koo
University of Seoul
2
3
Year
2007 2008 2009 2010 2011
Reve
nue w
ate
r ra
tio(%
)
80.5
81.0
81.5
82.0
82.5
83.0
83.5
84.0
Num
ber
of
civi
l appe
al
4.0e+5
6.0e+5
8.0e+5
1.0e+6
1.2e+6
1.4e+6
1.6e+6
Revenue water ratio
Number of civil appeal
• Continuous efforts have been made for optimal management of water pipe network aimed at
securing water resource, maintaining management soundness and ensuring water supply safety.
As a result, the current revenue water ratio of Korea shows 83.5%, showing a sharp upward
trend.
• Despite this quantitative effect, however, the number of civil complaints rose rapidly to
1,397,942 cases in 2011 from 549,432 cases after 2007 when the statistics on water service
complaints started to be collected, making the request for quality control also increase.
4
• This result definitely indicates that no matter how well the provider-focused water
management may be performed, it means little unless it satisfies the service level required by
the consumers. Accordingly, in order to improve the consumer satisfaction in water supply,
consumers' situation should be considered in water management.
• For this, the indicator of water management for the consumers is required like the revenue
water ratio which is the indicator of water management for the provider.
• Thus, this chapter developed the assessment indicators and models that can consider the
consumers' position, rather than the provider-focused management by introducing the concept
of water supply risk in water management, and also developed the optimal design model of a
newly-installed water distribution network and the reconstruction model of the existing water
distribution network on the basis of water supply risk assessment model.
5
6
In this study, Semi-PDA among PDA used in case of an accident situation of water pipe
network, and it can EPANET toolkit.
And, it were used water supply risk assessment and optimal design for water pipe network.
Hydraulic analysis of water pipe network is divided into two kind of DDA (Demand-Driven
Analysis) and PDA (Pressure-Driven Analysis). (Mays, 2003)
7
Yes
No
8
• Any problems by structuring the ISM is a method of analysis.
• In this study, the ISM method makes the structured pipe network. And when pipe was burst, it is useful to estimate
demand shortage by cut-off gate valve.
• Multiple regression model use to estimate leakage work time(LDT+RWT) by pipe, and logistic regression model use to
estimate pipe burst probability.
9
Terms Definition
Water supply risk
(m3/year)
Stochastic demand shortage quantity due to pipe burst, the product of pipe burst
probability and impact by pipe burst.
Pipe burst probability
(cases/m*year) The number by the possibility of pipe burst under unit pipe length and unit time.
Impact by pipe burst
(m3)
The product of demand shortage quantity by pipe burst and leakage work
time(LDT+RWT)
Leakage duration time
(LDT, day) The time from pipe burst to cut-off valve
Repair work time
(RWT, day) The time from cut-off valve to recovery
<Pipe burst> <Cut-off gate valve> <Recovery>
<Leakage duration time(LDT)> <Repair work time(RWT)>
10
D12
E20 E19
B5
C7
C8
D13
A1
A2
B5
C11
E17
B3
B6
E18
C10
C9
E15
D14
E16
Target area : K District, S City Block : 20 pipe : 2185 Connection : 10,772 Pump : 13 Reservoir : 1 Total pipe length : 136,585.5m Total demand : 32,417.92m3/day
11
Yes
No
12
• Logistic regression analysis was used to calculated pipe burst probability for Block A2, and using 6 independent variables and 1 dependent variable.
Variable Type Explanation
Probability Binary Dependent variable: 1, if pipe failure occurred, 0, otherwise
Diameter Category Independent variable: 1.<80mm, 2.80mm, 3.100-150mm, 4.200-250mm, 5.300-350mm,
6.400-500mm, 7.>=600mm
Material Category Independent variable: 1.STS, 2.DCIP, 3.SP, 4.PVC, 5.PE, 6.CIP
Age Category Independent variable: 1.<=5year, 2.6~10year, 3.11-15year, 4.16-20year,
5.21-25year, 6.26-30year, 7.>=31year
Depth Category Independent variable: 1.<1.0m, 2.1.0-1.5m, 3.>1.5m
Road Category Independent variable: 1.footway, 2.unpaved 3.2 lane road, 4.>= 4 lane road
Area
characteristic Category Independent variable: 1.not busy, 2.residential, 3.mixed(residential+commercial)
STS: Stainless Steep Pipe, DCIP: Ductile Cast Iron Pipe, SP: Coated and Wrapped Steel Pipe, PVC: Polyvinyl Chloride Pipe,
PE: Polyethylene Pipe, CIP: Cast Iron Pipe
1
1
1
pi totalpi pi
p total
n
pj
j
p
n
total j
j
n
total j
j
p Lprob Ln
Av Ln
p
Avn
L L
Ln Ln
1
1
1
pi totalpi pi
p total
n
pj
j
p
n
total j
j
n
total j
j
p Lprob Ln
Av Ln
p
Avn
L L
Ln Ln
1
1
1
pi totalpi pi
p total
n
pj
j
p
n
total j
j
n
total j
j
p Lprob Ln
Av Ln
p
Avn
L L
Ln Ln
1
1
1
pi totalpi pi
p total
n
pj
j
p
n
total j
j
n
total j
j
p Lprob Ln
Av Ln
p
Avn
L L
Ln Ln
piprob
pippAv
totalL
totalLn piLn
: Burst probability of pipe i (cases/year)
: Burst probability of pipe i used logistic regression analysis
: Average burst probability of all pipe used logistic regression analysis
: Total burst number of all pipe used logistic regression analysis (cases/year)
: Total pipe length of all pipe used logistic regression analysis (cases/year) : Pipe length of pipe i (m)
13
• Estimated leakage by pipe input to the corresponding node, after that, calculate the change of all node demand
r
d dq C H
dq Hr
dC
r
L L
LL r
d L
d L
q C H
qC
H
q q q
C C C
LqLC
1
1, 1,
_ ( _ )n n
st i i r
pi j j d a
j j i j j i
impact before demand after demand C H
1st
piimpact
_ i
jbefore demand
_ i
jafter demand
aH
r
L L
LL r
d L
d L
q C H
qC
H
q q q
C C C
r
L L
LL r
d L
d L
q C H
qC
H
q q q
C C C
r
L L
LL r
d L
d L
q C H
qC
H
q q q
C C C
: Base demand of node (㎥/day) : Node pressure before pipe burst (m)
: Emitter exponent (this study : 0.5) : Emitter coefficient before pipe burst
: Estimated leakage after pipe burst (㎥/day) : Emitter coefficient after pipe burst
: Demand shortage of LDT when burst pipe i th(㎥/day)
: Actual demand of node j before burst pipe i th(㎥/day)
: Actual demand of node j after burst pipe i th(㎥/day)
: Pressure of arrival node after burst pipe i th(m)
14
<Representation of gate valve location>
N1
N2 N3
N4
N5
P1
P2
P3 P4
P5
P6
V1
V2
V3
V4
V5
V6
N6
P7 V7
P1 P2 P3 P4 P5 P6 P7
N1 1
N2 1 1 1 1
N3 1 1
N4 1 1
N5 1 1 1 1
N6 1
P1 P2 P3 P4 P5 P6 P7
N1
N2 1 1
N3 1
N4 1
N5 1 1 1
N6
<Sample network>
<Node-pipe matrix A> <Node-pipe matrix B>
• Matrix A : location of all installation possible valve.
• Matrix B : location of currently installed valve.
15
<Segment algorithm> <Node-pipe matrix C>
P1 P2 P3 P4 P5 P6 P7
N1 1
N2 2
(V1) 2
(V2) 1 1
N3 2
(V3) 1
N4 1 2
(V4)
N5 2
(V5) 1
2 (V6)
2 (V7)
N6 1
Pipe Segment Valve Node
P1 U1={P1} V1 N1
P2 U2={P2} V2, V3 -
P3 U3={P3, P6} V1, V2, V4, V6
N2, N4
P4 U4={P4} V3, V5 N3
P5 U5={P5} V4, V5, V6, V7
N5
P6 U3={P3, P6} V1, V2, V4, V6
N2, N4
P7 U6={P7} V7 N6
U1 U2 U3 U4 U5 U6
U1 1
U2 -1 1
U3 -1 1 1
U4 -1 1
U5 -1 -1 1
U6 -1
<Segment table>
<Adjacency matrix among segment>
• Matrix A + Matrix B = Matrix C
• Segment table make using segment algorithm, and it is adjacency
matrix among segment.
• Assignment segment demand estimate using the ISM.
16
<Estimation of assignment segment demand>
<Adjacency matrix A> <Reachability matrix R> <Matrix B (A+I)>
U1 U2 U3 U4 U5 U6
U1 1
U2 1
U3 1 1
U4 1
U5 1
U6
U1 U2 U3 U4 U5 U6
U1 1 1
U2 1 1
U3 1 1 1
U4 1 1
U5 1 1
U6 1
U1 U2 U3 U4 U5 U6
U1 1 1 1 1 1 1
U2 1 1 1 1
U3 1 1 1 1
U4 1 1 1
U5 1 1
U6 1
(A+I) = B
If Bk = Bk+1 ,Bk=R
※ I = unit matrix
※ Boolean operation
0+0=0, 1+0=1, 0+1=1, 1+1=1, 0x0=0, 1x0=0, 0x1=0, 1x1=0
※ Apply to ISM(Interpretive Structural Modeling)
17
<Estimation of assignment segment demand>
U1 Level 1
Level 2
Level 3
Level 4
d1
U3 d3
U2
d2
U4
d4
Level 5 U5
d5
U6
d4 Level 6
( )
( ) ,
i
i i i
j D
k
k i i i
E d e j
e i E P for k P
iE : Assignment segment demand of segment
included pipe i (m3/day)
id : Demand of segment i (m3/day)
( )ie j : Substep segment of segment I that depend
on demand (m3/day)
iD : Substep segment lower than segment i
: Demand shortage of RWT when burst pipe i
(m3/day)
iE : Assignment segment demand included
pipe i (m3/day)
2nd
pi iimpact E
2nd
piimpact
18
1 1 2 2( ) ( )st st st st
pi pi pi piRisk prob impact T impact T
piRisk : pipe water supply risk of pipe i (m3/day)
piprob : Burst probability of pipe i (cases/year)
1st
piimpact : Demand shortage of LDT when burst pipe i (m3/day)
: Demand shortage of RWT when burst pipe i (m3/day)
1stT : Duration time of LDT(day)
2nd
piimpact
2ndT : Duration time of RWT(day)
1
n
pi
ib
Risk
Riskn
bRisk : Block water supply risk(m3/day)
n : Pipe number within block
19
Valve
location
Cost
Risk
Pressure,
velocity
Rank, Crowding
distance
No Yes Yes
Gate valve
plan cost
No
Diameter
Cost
Risk
Number of
generation
Pressure,
velocity
Gate valve
plan cost
Number of
generation
20
Diameter(mm) Gene Diameter(mm) Gene
80 1 250 5
100 2 300 6
150 3 350 7
200 4 400 8
Diameter(mm) Cost
(won/m) Diameter(mm)
Cost
(won/m)
80 148,200 250 235,300
100 156,700 300 264,800
150 183,500 350 293,200
200 205,600 400 314,900
Source: Water resources engineering corporation(201
3)
Chromosome Pipe 1 Pipe 2 Pipe 3 ... Pipe n Objective 1 Objective 2
Population 1 Dai(1,1) Dai(1,2) Dai(1,3) ... Dai(1,n) Cost(1,1) Risk(1,2)
Population 2 Dai(2,1) Dai(2,2) Dai(2,3) ... Dai(2,n) Cost(2,1) Risk(2,2)
... ... ... ... ... ... ... ...
Population m Dai(m,1) Dai(m,2) Dai(m,3) ... Dai(m,n) Cost(m,1) Risk(m,2)
<Gene representation of design variables> <Total laying cost for each pipe diameter>
• Design variable : pipe diameter, Objective variables : block water supply risk, pipe laying cost
21
Chromosome Pipe 1 Pipe 2 ... Pipe i Objective 1 Objective 2
Population 1
Node 1 Val(1,1) Val(1,2) ... Val(1,i)
Cost(1,1) Risk(1,2) Node 2 Val(2,1) Val(2,2) ... Val(2,i)
... ... ... ... ...
Node j Val(j,1) Val(j,2) ... Val(j,i)
...
Chromosome Pipe 1 Pipe 2 ... Pipe i Objective 1 Objective 2
Population n
Node 1 Val(1,1) Val(1,2) ... Val(1,i)
Cost(n,1) Risk(n,2) Node 2 Val(2,1) Val(2,2) ... Val(2,i)
... ... ... ... ...
Node j Val(j,1) Val(j,2) ... Val(j,i)
Diameter(mm) Cost(won) Diameter(mm) Cost(won)
80 1,782,121 250 3,863,459
100 1,926,134 300 5,489,096
150 2,499,089 350 7,079,579
200 2,938,469 400 9,292,996
Source: 2013 Korea Price Information, Hwashin engin
eering corporation(2013)
<Total laying cost for each valve diameter> • Design variable : gate valve location,
Objective variables : block water supply
risk, valve laying cost
22
<Screen capture of optimal design model for water distribution network>
<Screen capture of water supply risk assessment model>
23
• Here, water supply risks by pipe are values considering all pipe burst probability, pipe length, the impacts of
LDT and RWT, and Pipe No. 1 of A2 Block, a water supply risk of 0.372㎥/year means that pipe burst may
cause a probability of a demand shortage of 0.372㎥ once a year.
• Thus, based on this finding, the priority of management of pipe in A2 Block can be determined, and order
can be determined like Pipe No. 10 with the highest water supply risk, followed by No. 13, 8, 5, 43, 7 and 51.
<Map of water supply risk for Block A2> <Water supply risk by pipe for Block A2>
24
• Supposing leakage hole scale by pipe was 1㎠, if a leakage took place, the block water supply risk of A2
Block was calculated at 1.507㎥/year.
• The meaning of this value is an average value of water supply risks by pipe in the relevant block, which
means that a burst accident occurs in each pipe about once a year and at the time and it is expected that
a probable demand shortage of 1.507㎥ would take place.
Leakage hole scale(cm2)
0.0 0.5 1.0 1.5 2.0
Blo
ck w
ate
r su
pp
ly r
isk(
m3/y
ea
r)
0
1
2
3
4
Block water supply risk of LDT
Block water supply risk of RWT
Total block water supply risk
• Here, if the leakage quantity of a pipe
increases, the block water supply risk of LDT
rapidly will increase while that during the
RWT will not change.
• Judging from these results, as the leakage
quantity of each pipe increases, the block
water supply risk increases and especially it
was found that it had an impact on the
block water supply risk of LDT.
<Change of block water supply risk by leakage hole scale for Block A2>
25
Number of gate valve
55 60 65 70 75
Blo
ck w
ater
sup
ply
risk(
m3 /y
ear)
0.4
0.6
0.8
1.0
1.2
1.4
1.6
Block water supply risk of LDT
Block water supply risk of RWT
Total block water supply risk
• This is a finding of calculation of block water supply risks by the number of gate valve installation by installing an additional gate valve one by one when currently 54 gate valves have been installed in A2 Block, 1 gate valves was added at a random spot one by one. However, the leakage hole was sized 1㎠.
• Here, it turned out that as the number of gate valve installation increased, block water supply risk decreased, and especially, it was found that the block water supply risk of RWT decreased.
• This is judged that this was because the increase of the number of the gate valves dispersed the impact of RWT by pipe burst.
• In contrast, since block water supply risk of LDT had nothing to do with whether there was a gate valve, there was no difference by the number of gate valve.
<Change of block water supply risk by gate valve number for Block A2>
26
• To estimate block water supply risk of LDT, the leakage hole was sized 1㎠, and since the target pipe was assumed
to be a new pipe, the burst probability of all pipe was supposed to be 0.2 cases/km/year.
• Also, LDT was set up equally to 4.2 hr for all pipes.
• The constraint conditions were as follows: the maximum velocity of flow of pipe was set to 3.0m/s; the minimum
velocity of flow to 0.1m/s; the minimum water pressure of the joint to 30m; and genetic parameters to carry out
NSGAⅡ were as follows: population 200, generation 100, crossover rate 0.9 and mutation rate 0.1 to run the model.
Generation
number
Population
number
Block water supply risk
in LDT
(㎥/year)
Pipe laying
cost(Won)
50
50 0.106 478,371,771
100 0.096 458,169,472
150 0.081 469,150,323
200 0.066 454,132,935
250 0.046 444,007,790
100
50 0.080 480,075,559
100 0.056 458,838,079
150 0.055 449,959,524
200 0.040 438,195,672
250 0.043 459,099,951
150
50 0.062 479,778,926
100 0.059 457,564,716
150 0.048 450,667,170
200 0.046 452,933,388
250 0.040 439,628,753
Cross
over
rate
Mutation
rate
Block water
supply risk in LDT
(㎥/year)
Pipe laying
cost(Won)
0.95 0.05 0.056 453,801,702
0.90 0.10 0.040 438,195,672
0.85 0.15 0.060 450,995,978
0.80 0.20 0.058 457,415,621
<Result of optimal diameter design model by generation and population number>
<Result of optimal diameter design model by cross over and mutation rate>
27
• In the pipe network designed with the optimal diameter, 54 out of 66 nodes that met the nodal pressure of 30m in the constraint conditions while the number of the pipe that met the minimum velocity of flow of 0.1m/s was 36 out of 84.
• In the optimal pipe diameter design, all nodes and pipes did not meet the constraint conditions of the minimum nodal pressure and the minimum velocity of pipe.
• This is because the node demand of the existing pipe network was much lower than the diameter and there were 3 pipes that met the velocity of pipe of over 0.1m/s in the existing pipe network.
• In other words, the pipe network prepared with the optimal diameter design model can be judged to have been optimally designed under the present condition of the node demand.
Node number
0 20 40 60
Pre
ssure
of
node(m
)
20
25
30
35
40
45
<Pressure by node for optimal diameter design network>
Pipe number
0 20 40 60 80
Ve
locity in
pip
e(m
/s)
0.0
0.2
0.4
0.6
0.8
1.0
<Velocity by pipe for optimal diameter design network>
28
• In the existing pipe network, pipes with a diameter of 400mm were distributed, but after the running of the optimal diameter design model, one with a diameter of 250mm was designed as the largest pipe, and those with a diameter of 80mm were mostly included.
• This is judged that the existing pipe network was designed excessively by an uncertain forecast of water demands.
• Consequently, the total laying costs of pipes turned out to decrease from 1,219,373,257 won to 458,838,079 won by 62.4%.
Pipe
No.
Diameter(mm) Pipe
No.
Diameter(mm) Pipe
No.
Diameter(mm)
Exi. Opt. Exi. Opt. Exi. Opt.
1 400 250 15 400 80 29 150 80
2 100 80 16 400 80 30 150 80
3 400 80 17 400 80 31 150 80
4 400 80 18 200 80 32 150 80
5 400 80 19 200 80 33 150 80
6 400 80 20 200 80 34 150 80
7 400 200 21 200 80 35 150 80
8 400 80 22 200 80 36 150 80
9 400 150 23 200 80 37 150 80
10 400 80 24 200 80 38 150 80
11 400 150 25 150 80 39 150 80
12 400 80 26 150 80 40 150 80
13 400 150 27 150 80 41 150 80
14 400 80 28 150 80 42 150 80
Pipe
No.
Diameter(mm) Pipe
No.
Diameter(mm) Pipe
No.
Diameter(mm)
Exi. Opt. Exi. Opt. Exi. Opt.
43 150 80 57 150 80 71 150 80
44 150 80 58 150 80 72 150 80
45 150 80 59 150 80 73 150 80
46 150 80 60 150 80 74 100 80
47 150 80 61 150 80 75 100 80
48 150 80 62 150 80 76 100 80
49 150 80 63 150 80 77 100 80
50 150 80 64 150 80 78 100 80
51 150 80 65 150 100 79 100 80
52 150 80 66 150 80 80 100 80
53 150 80 67 150 80 81 100 80
54 150 80 68 150 80 82 100 80
55 150 80 69 150 80 83 80 80
56 150 80 70 150 80 84 400 80
<Comparison between existing and optimal diameter design network>
29
Leakage hole
scale(㎠)
Block water supply
risk of LDT(㎥/year) Pipe laying cost(won)
0.1 0.001 438,214,072
0.2 0.004 440,991,625
0.3 0.006 461,616,752
0.4 0.011 438,101,990
0.5 0.015 447,898,078
0.6 0.021 461,764,000
0.7 0.036 489,288,945
0.8 0.037 448,576,553
0.9 0.042 454,268,038
1.0 0.052 457,972,400
1.1 0.061 442,660,037
1.2 0.091 460,301,802
1.3 0.094 438,092,667
1.4 0.100 465,592,948
1.5 0.108 455,304,698
1.6 0.123 450,633,562
1.7 0.143 451,625,225
1.8 0.148 451,014,828
1.9 0.169 458,729,346
2.0 0.179 444,620,417
• According to the leakage hole scale, since the leakage
hole scale and block water supply risk of LDT reflected
not linearly proportional relationship but the impact of
leakage, it was found that the exponential impact
changed.
• In other words, it is judged that using this result, the
leakage reduction targets by each pipe can be
quantified for the reduction of the block water supply
risk, which can be used for the efficient management of
water supply risks in the future.
<Block water supply risk of LDT by leakage hole scale>
<Results of optimal diameter design model by leakage hole scale
30
Node
pressure(m)
Block water supply risk
of LDT(㎥/year) Pipe laying cost(won)
15 0.051 444,582,071
20 0.049 454,169,440
25 0.052 448,154,511
30 0.051 470,493,134
35 0.054 450,014,186
40 0.058 469,156,543
• It is found that as nodal pressure, a constraint condition increases, the block water supply risk of LDT
increases linearly.
• In other words, it is judged that using this result, in designing a new water distribution network
diameter, the operating water pressure will be determined in advance and the water supply risk of
water distribution network will be minimized.
<Block water supply risk of LDT by node pressure>
<Results of optimal diameter design model by node pressure>
31
• Since objective functions take only the location of the gate valve as design variable, they were set to the block
water supply of RWT risk and gate valve laying cost, and the constraint condition was set to meet 105,000,000 won,
the gate valve laying budget.
• The genetic parameters to run NSGAⅡ were set up as follows: population 200, generation 100, crossover rate 0.9
and mutation rate 0.1 to run the model.
Generation
number
Population
number
Block water supply risk
of RWT(㎥/year)
Gate valve laying
cost(Won)
50
50 0.263 103,837,919
100 0.262 103,505,471
150 0.258 103,505,471
200 0.246 103,505,471
250 0.240 103,505,471
100
50 0.243 104,697,901
100 0.224 103,505,471
150 0.219 103,505,471
200 0.215 103,505,471
250 0.212 103,505,471
150
50 0.227 103,505,471
100 0.214 104,697,901
150 0.215 103,505,471
200 0.213 103,505,471
250 0.216 103,505,471
Cross
over
rate
Mutation
rate
Block water
supply risk of
RWT(㎥/year)
Gate valve laying
cost(Won)
0.95 0.05 0.223 103,505,471
0.90 0.10 0.215 103,505,471
0.85 0.15 0.235 103,505,471
0.80 0.20 0.238 103,505,471
<Results of gate valve optimal location design model by generation and population number>
<Results of gate valve optimal location design model by cross over and mutation rate>
32
• The number of the optimally designed pipes was 56, and the total gate valve laying costs were estimated at 104,747,986 won, which decreased by 40.7% from 176,673,247, the gate valve laying costs of the existing pipe network design method.
• This is because in the diameter optimization, the diameter of a part of the target pipe was reduced and the gate valve laying cost decreased, and in the meantime, in designing the optimal diameter, with the extension of a part of the diameter, an additional cost incurred, but finally, the cost was reduced, so it is judged that the effectiveness of the model developed in this study was proven enough.
Gate
valve
No.
Location Gate
valve
No.
Location
Pipe No. Node No. Pipe No. Node
No.
1 19 8 15 9 5
2 27 15 16 13 5
3 56 21 17 9 7
4 72 47 18 11 7
5 1 1 19 21 8
6 3 16 20 20 8
7 33 25 21 15 9
8 79 40 22 61 23
9 28 41 23 39 26
10 75 34 24 41 27
11 30 19 25 64 29
12 82 47 26 58 30
13 12 4 27 59 35
14 16 4 28 63 35
Gate
valve
No.
Location Gate
valve
No.
Location
Pipe No. Node No. Pipe No. Node No.
29 57 22 43 24 11
30 23 23 44 29 12
31 16 9 45 73 32
32 40 24 46 36 33
33 49 24 47 74 33
34 62 24 48 60 34
35 24 10 49 2 17
36 58 26 50 35 17
37 68 26 51 76 35
38 18 11 52 67 36
39 67 27 53 77 36
40 69 27 54 34 37
41 36 28 55 77 37
42 66 28 56 55 43
33
Gate valve planing
cost(won)
Block water supply risk of RWT(㎥
/year)
220,000,000 0.114
200,000,000 0.123
180,000,000 0.127
160,000,000 0.155
140,000,000 0.187
120,000,000 0.215
100,000,000 0.244
• In running the gate valve optimal location design model, it turned out that making the gate valve
laying budget, a constraint condition lowered the block water supply risk of RWT.
• This is because the number of the available laying of the gate valve will increase if the laying budge of
the gate valve increases and this causes the water supply risk of RWT by pipe.
<Result of gate valve optimal location design model by plan cost>
<Variation analysis of block water supply risk of RWT by gate valve plan cost>
34
Pipe number
0 20 40 60 80
Wate
r su
pply
ris
k(m
3/y
ear)
0
2
4
6
8
10
Existing network
Optimal network
• To assess the appropriateness of the optimal design model developed in this study, a comparative
analysis was carried out on the pipe network designed with the optimal pipe network design method
and the existing pipe network design method.
• In addition, for both pipe network design methods, the pipe burst probability by pipe was assumed to
be 0.2 cases/km/year for the comparison.
• As a result, it turned out that the block water supply risk with the existing pipe network design method
was 0.306㎥/year while that with the optimal pipe network design method decreased by 10.1% (0.275㎥
/year) and the cost of construction with the existing pipe network design method was 1396 million won
while that with the optimal pipe network design method was reduced by 59.7% (563 million won)
35
• Although the pipe network designed with the optimal pipe network design method had much less cost
of construction than that designed with the existing pipe network design method, in that it might
reduce the block water supply risk, the validity of the optimal design model of the water distribution
network developed in this study could be verified.
• In addition, besides the cost of construction, the reduction effect of the block water supply risk with the
optimal design shows that cutting off the water can be prevented for 8.8 persons a year when
converted to Lpcd of 296L in the target area of this study.
<Map of water supply risk for existing and optimal design network>
36
• This study developed a water supply risk assessment model for water distribution network,
calculating the pipe burst probability and the impact of pipe burst through the application of
the developed model to the targeted area(Block A2 in S city ) and the water supply risk by
pipes using the multiplication of these two values.
• The impact of pipe burst was separately calculated for the leakage duration time and the repair
work time when water service is cut off or water service is reduced.
• As a result, water supply risk for the block in the study area was calculated at 1.507㎥/year.
• And, this research developed a design model to minimize a water supply risk of a newly-
installed water distribution network with the minimum design cost using NSGAⅡ.
• The developed optimal design model decides on the diameter and the location of gate valve
and it is also a model using a multipurpose genetic algorithm with the pipe and gate valve
installation cost and the water supply risk as the objective functions and with the flow velocity
in a pipe, the water pressure at a panel point and the budget for a gate valve installation as
the restriction conditions.
37
• As a result, the design cost was calculated at 0.56 billion Won which was reduced by 69.7%
compared with the existing pipe network.
• In the case of water supply risk for the block, it appeared as 0.275㎥/year which was reduced by
10.1% compared with the existing pipe network.
• The amount of water supply risk reduction caused by the application of the optimal design model
for a newly-installed water distribution network is equivalent to the amount used by 8.8 persons
in a day if it is calculated based on 296L of Lpcd(liters per capita per day) in the study area.
• This means that the water supply cutoff can be prevented for one day for 8.8 persons.
• As shown by this study result, the proposed water supply risk assessment model could
concurrently consider the positions of both water provider and water consumer. Besides, it was
ascertained that the model could be realized to be suitable for the research purpose from the
viewpoint that it can get the best effect also in cost/effect perspective.
• And, it is considered that the optimal design model for a newly-installed water distribution
network developed by this study can concurrently solve the objective functions of the
minimization of the design cost and the water supply risk for the block, and it could be
ascertained that the model developed by this study could produce the outcome suitable for the
purpose.
