Risk assessment of sewer condition using artificial intelligence tools

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RISK ASSESSMENT OF SEWER CONDITION USING ARTIFICIAL INTELLIGENCE TOOLS
Application to the SANEST sewer system
Vitor Sousa
IST, UTL
José Pedro Matos
IST, UTL
Nuno Marques Almeida
IST, UTL
José Saldanha Matos
IST, UTL
http://www.toledoblade.com/Police-Fire/2013/07/06/Sewer-repairs-start-after-intersection-collapse-Copy.html
SPN7 2013
Sheffield, 28-30 August
OUTLINE
1. Introduction
2. Sewer condition modelling
3. SANEST sewer system
4. Data collection
5. Model design
6. Artificial Neural Networks
7. Support Vector Machines
8. Discriminant analysis
9. Conclusions
SPN7 2013
Sheffield, 28-30 August
1. INTRODUCTION
Wastewater drainage systems asset management strategies

Reactive

Proactive:
 prevention-based (or based on age);
 inspection-based (or based on condition);
 prediction-based (or based on reliability);
The concept of risk has also been used in managing wastewater drainage assets, either:

Indirectly – by indentifying critical sewers (managed proactively) and non-critical sewers
(managed reactively)

Directly – through the development of multicriteria tools accounting also for the
consequences of the sewers failures (MARESS - Reyna 1993; RERAUVIS - RERAU 1998;
CARE-S - CARE-S 2005)
SPN7 2013
Sheffield, 28-30 August
2. SEWER CONDITION MODELLING
CATEGORY
CLASS
Function-based Deterministic
Stochastic
Data-based
TYPE
Linear regression
REFERENCES
Chughtay and Zayed (2007a, 2007b, 2008)
Non-linear regression
Newton and Vanier (2006); Wirahadikusumah et al. (2001)
Survival function
Hörold and Baur (1999); Baur and Herz (2002); Baur et al.
(2004); Ana (2009)
Ordinal regression
Yang (1999); Davies et al. (2001b); Ariaratnam et al. (2001);
Pohls (2001); Ana (2009)
Markov chains
Wirahadikusumah et al. (2001); Micevski et al. (2002);
Coombes et al. (2002); Baik et al. (2006); Koo and Ariaratnam
(2006); Newton and Vanier (2006); Tran (2007); Le Gat (2008)
Semi-Markov chains
Kleiner (2001); Dirksen and Clemens (2008); Ana (2009)
Discriminant analysis
Artificial inteligence Artificial Neural Networks – ANNs
Tran (2007); Ana (2009)
Najafi and Kulandaivel (2005); Tran et al. (2006); Tran (2007);
Ana (2009); Khan et al. (2010)
Fuzzy Set
Yan and Vairavamoorthy (2003); Kleiner et al. (2004a, 2004b,
2006)
Case Based Reasoning – CBR
Support Vector Machines – SVMs
Fenner et al. (2007)
Mashford et al. (2011)
Genetic programing Evolutionary Polynomial Regression – EPR
SPN7 2013
Savic et al. (2006); Ugarelli et al. (2008); Savic et al. (2009)
Sheffield, 28-30 August
3. SANEST SEWER SYSTEM
http://www.sanest.pt/artigo.aspx?sid=e73adb75-e84d-46ae-b578-50a5ee934cc2&cntx=d00N%2Fz8yc6LPuMNx72xjzkHnWQg%2Bm23akSu576zxbEk%3D
SPN7 2013
Sheffield, 28-30 August
4. DATA COLLECTION
Material / Diameter
VC (1)
200
250
300
350
400
PC (2)
315
500
PVC (3)
200
250
315
400
500
630
700
800
HDPE (4)
360
400
450
500
600
C-PP (5)
315
400
500
630
C-PVC (6)
350
400
Total
SPN7 2013
Sewers [nº]
Total length [m]
Average age [years]
Average depth [m]
Average slope [%]
Average length [m]
134
7
15
38
69
1
53
1
52
348
3
59
38
112
73
27
30
6
122
38
4
4
66
10
60
26
4
29
1
28
7
21
745
4370.50
186.13
389.41
1232.85
2484.68
42.23
1408.70
51.26
1357.44
12682.20
80.44
2291.46
957.03
4347.90
2868.81
1132.64
915.38
88.54
4102.04
1206.47
111.03
217.33
2154.48
412.73
1771.99
908.06
122.89
713.70
27.34
1033.74
165.00
868.74
25369.17
54.55
45.00
58.13
49.74
58.17
39.00
29.85
30.00
29.85
11.53
8.00
10.37
12.39
11.59
12.26
10.37
12.00
12.00
9.84
10.00
9.75
9.00
9.92
9.00
9.65
9.96
12.00
9.03
10.00
4.42
6.20
4.00
19.92
2.52
2.68
2.41
1.98
2.82
2.31
2.47
2.73
2.47
2.88
2.19
2.34
2.46
2.98
3.03
3.12
3.47
3.47
3.53
3.70
3.31
2.07
3.76
2.08
3.02
4.42
3.23
1.72
3.40
3.87
2.83
4.12
2.94
2.14
1.32
1.09
2.95
1.83
1.11
2.08
2.09
2.08
1.72
7.22
4.14
0.90
1.75
0.87
0.81
0.53
0.34
1.23
0.96
1.68
1.26
1.50
0.27
1.51
2.83
0.26
0.46
2.71
1.24
2.71
0.89
1.71
32.62
26.59
25.96
32.44
36.01
42.23
26.58
51.26
26.10
36.44
26.81
38.84
25.19
38.82
39.30
41.95
30.51
14.76
33.62
31.75
27.76
54.33
32.64
41.27
29.53
34.93
30.72
24.61
27.34
39.76
33.00
41.37
34.14
Sheffield, 28-30 August
5. MODEL DESIGN
The sewer operational and structural condition classes were determined from the CCTV
inspection results using the WRc (2001) rating protocol.
Two alternative approaches were used to reduce number of condition classes used as outputs:

ALT A – the sewers were classified into three categories representing reaches that are in
good condition and are expected to endure a long period before the next inspection (category
0 – sewers in condition 1 and 2), sewers that require a shorter period of time until the next
inspection (category 1 – sewers in condition 3) and sewers that are failing and should be
intervened in the short term (category 2 –sewers in condition 4 and 5)

ALT B – the sewers were divided into those that require intervention (category 2 – sewers in
condition 4 and 5) and those which do not require intervention (category 1 – sewers in
condition 1, 2 and 3).
SPN7 2013
Sheffield, 28-30 August
6. ARTIFICIAL NEURAL NETWORKS
ANNs
Classification
Case
Operational
– ALT A
Structural –
ALT A
Operational
– ALT B
Structural –
ALT B
Correlation
Number of neurons
Train
Algorithm
Error
Function
Train
Test
BFGS
CE
61.80
66.67
15
3
BFGS
SOS
68.52
71.85
29
3
BFGS
CE
80.00
82.96
19
2
BFGS
SOS
75.74
82.22
18
2
Activation function
Hidden Layer Output Layer Hidden Layer Output Layer
Hiperbolic
Tangent
Hiperbolic
Tangent
Sigmoid
Logistic
Sigmoid
Logistic
Softmax
Sigmoid
Logistic
Softmax
Sigmoid
Logistic
For the classification case of the sewers' structural condition according to ALT B, the
corresponding ANN presented was used to evaluate the effect of the initial weights of the neuron
connections. Randomly varying the initial weights of the neuron connections in 100 ANNs resulted
in correlations ranging from 67% to 79%, for the train data (average=73%), and from 72% to 84%,
for the test data (average=76%).
SPN7 2013
Sheffield, 28-30 August
6. ARTIFICIAL NEURAL NETWORKS
ALT A
OBSERVED
Category
PREDICTED (Operational)
0
1
2
Correct /
Incorrect
PREDICTED (Structural)
0
1
2
Correct /
Incorrect
0
7
2
3
58.3% /
41.7%
5
1
0
83.3% /
16.7%
1
11
49
4
76.6% /
23.4%
7
55
11
75.3% /
24.7%
2
12
13
34
57.6% /
42.4%
5
14
37
66.1% /
33.9%
Correct /
Incorrect
23.3% /
76.7%
76.6% /
23.4%
82.9% /
17.1%
66.7% /
33.3%
29.4% /
70.6%
78.6% /
21.4%
77.1% /
22.9%
71.9% /
28.1%
ALT B
OBSERVED
Category
PREDICTED (Operational)
1
2
Correct /
Incorrect
PREDICTED (Structural)
1
2
Correct /
Incorrect
1
85
14
85.9% / 14.1%
75
12
86.2% / 13.8%
2
9
27
75.0% / 25.0%
12
35
75.0% / 25.0%
Correct /
Incorrect
90.4% / 9.6%
65.9% / 34.1%
83.0% / 17.0%
86.2% / 18.8%
75.0% / 25.0%
82.2% / 17.8%
SPN7 2013
Sheffield, 28-30 August
7. SUPPORT VECTOR MACHINES
ALT A
OBSERVED
PREDICTED (Operational)
Correct /
Incorrect
Category
0
1
2
0
17
0
17
50% / 50%
1
70
64
6
2
48
16
32
Correct /
Incorrect
12.6% /
87.4%
80.0% /
20.0%
58.2% /
41.8%
45.7% /
54.3%
33.3% /
66.7%
41.9% /
58.1%
PREDICTED (Structural)
0
1
2
14
6
10
17
37
10
12
32.6% /
67.4%
0
86.0% /
14.0%
29
59.2% /
40.8%
Correct /
Incorrect
46.7% /
53.3%
57.8% /
42.2%
70.7% /
29.3%
59.3% /
40.7%
ALT B
OBSERVED
Category
PREDICTED (Operational)
1
2
Correct /
Incorrect
PREDICTED (Structural)
1
2
Correct /
Incorrect
1
83
11
88.3% / 11.7%
80
7
92.0% / 8.0%
2
18
23
56.1% / 43.9%
32
16
33.3% / 66.7%
Correct /
Incorrect
82.2% / 17.8%
67.6% / 32.4%
78.5% / 21.5%
71.4% / 28.6%
69.6% / 30.4%
71.1% / 28.9%
SPN7 2013
Sheffield, 28-30 August
8. DISCRIMINANT ANALYSIS
ALT A
OBSERVED
PREDICTED (Operational)
Category
0
1
2
0
12
6
12
1
15
37
12
2
12
0
29
Correct /
Incorrect
30.8% /
69.2%
86.0% /
14.0%
54.7% /
45.3%
Correct /
Incorrect
40.0% /
60.0%
57.8% /
42.2%
70.7% /
29.3%
57.8% /
42.2%
PREDICTED (Structural)
0
1
2
4
11
2
0
56
14
0
27
21
100.0% /
0.0%
59.6% /
40.4%
56.8% /
43.2%
Correct /
Incorrect
23.5% /
76.5%
80.0% /
20.0%
43.8% /
56.3%
60.0% /
40.0%
ALT B
OBSERVED
Category
PREDICTED (Operational)
1
2
Correct /
Incorrect
PREDICTED (Structural)
1
2
Correct /
Incorrect
1
84
10
89.4% / 10.6%
79
8
90.8% / 9.2%
2
17
24
58.5% / 41.5%
30
18
37.5% / 62.5%
Correct /
Incorrect
83.2% / 16.8%
70.6% / 29.4%
80.0% / 20.0%
72.5% / 72.5%
69.2% / 30.8%
71.9% / 28.1%
SPN7 2013
Sheffield, 28-30 August
9. CONCLUSIONS
The different methods yielded very similar overall result.
Since the main goal of modelling the condition of sewers is to identify the sewer reaches that may
need intervention, the ANNs’ results provided better results given the approach adopted.
However, contrarily to the SVMs and discriminant analysis, the ANNs’ results depend significantly
in various factors.
The increase of the number of classes resulted in a decrease in the models accuracy.
SPN7 2013
Sheffield, 28-30 August
REFERENCES
Ana, E. V. (2009). Sewer asset management - sewer structural deterioration modeling and multicriteria decision making in
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Ariaratnam, T. S.; Assaly, E. A.; Yuqing, Y. (2001). Assessment of infrastructure inspection needs using logistic models.
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Baur, R.; Herz, R. (2002). Selective inspection planning with ageing forecast for sewer types. Water Science and Technology,
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SPN7 2013
Sheffield, 28-30 August
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SPN7 2013
Sheffield, 28-30 August
REFERENCES
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SPN7 2013
Sheffield, 28-30 August
RISK ASSESSMENT OF SEWER CONDITION USING ARTIFICIAL INTELLIGENCE TOOLS
Application to the SANEST sewer system
Vitor Sousa
IST, UTL
José Pedro Matos
IST, UTL
Nuno Marques Almeida
IST, UTL
José Saldanha Matos
IST, UTL
http://www.toledoblade.com/Police-Fire/2013/07/06/Sewer-repairs-start-after-intersection-collapse-Copy.html
SPN7 2013
Sheffield, 28-30 August

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