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
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
TYPE
Linear regression
Non-linear regression
Survival function
Ordinal regression
Markov chains
REFERENCES
Chughtay and Zayed (2007a, 2007b, 2008)
Newton and Vanier (2006); Wirahadikusumah et al. (2001)
Hörold and Baur (1999); Baur and Herz (2002); Baur et al.
(2004); Ana (2009)
Yang (1999); Davies et al. (2001b); Ariaratnam et al. (2001);
Pohls (2001); Ana (2009)
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)
Data-based
Semi-Markov chains
Discriminant analysis
Artificial inteligence Artificial Neural Networks – ANNs
Kleiner (2001); Dirksen and Clemens (2008); Ana (2009)
Tran (2007); Ana (2009)
Najafi and Kulandaivel (2005); Tran et al. (2006); Tran (2007);
Ana (2009); Khan et al. (2010)
Fuzzy Set
Case Based Reasoning – CBR
Support Vector Machines – SVMs
Yan and Vairavamoorthy (2003); Kleiner et al. (2004a, 2004b,
2006)
Fenner et al. (2007)
Mashford et al. (2011)
Genetic programing Evolutionary Polynomial Regression – EPR Savic et al. (2006); Ugarelli et al. (2008); Savic et al. (2009)
SPN7 2013 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
250
315
400
500
630
700
800
HDPE (4)
360
400
450
500
600
VC (1)
200
250
300
350
400
PC (2)
315
500
PVC (3)
200
C-PP (5)
315
400
500
630
C-PVC (6)
350
400
Total
SPN7 2013
Sewers [nº]
6
122
38
4
4
66
10
59
38
112
73
27
30
69
1
53
1
52
348
3
134
7
15
38
29
1
28
60
26
4
7
21
745
Total length [m] Average age [years] Average depth [m] Average slope [%] Average length [m]
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
4370.50
186.13
389.41
1232.85
2484.68
42.23
1408.70
51.26
1357.44
12682.20
80.44
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
14.76
33.62
31.75
27.76
54.33
32.64
41.27
38.84
25.19
38.82
39.30
41.95
30.51
32.62
26.59
25.96
32.44
36.01
42.23
26.58
51.26
26.10
36.44
26.81
29.53
34.93
30.72
24.61
27.34
39.76
33.00
41.37
34.14
0.34
1.23
0.96
1.68
1.26
1.50
0.27
4.14
0.90
1.75
0.87
0.81
0.53
2.14
1.32
1.09
2.95
1.83
1.11
2.08
2.09
2.08
1.72
7.22
1.51
2.83
0.26
0.46
2.71
1.24
2.71
0.89
1.71
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
Train
Algorithm
Operational
– ALT A
Structural –
ALT A
Operational
– ALT B
Structural –
ALT B
BFGS
BFGS
BFGS
BFGS
Error
Function
CE
SOS
CE
SOS
Train
Correlation
Test
61.80
66.67
68.52
80.00
75.74
71.85
82.96
82.22
Number of neurons Activation function
Hidden Layer Output Layer Hidden Layer Output Layer
15
29
19
18
3
3
2
2
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
0
1
2
Correct /
Incorrect
ALT B
OBSERVED
Category
1
2
Correct /
Incorrect
0
PREDICTED (Operational)
1 2
7 2 3
11 49 4
12
23.3% /
76.7%
13
76.6% /
23.4%
34
82.9% /
17.1%
Correct /
Incorrect
58.3% /
41.7%
76.6% /
23.4%
57.6% /
42.4%
66.7% /
33.3%
0
PREDICTED (Structural)
1 2
5 1 0
7 55 11
5
29.4% /
70.6%
14
78.6% /
21.4%
37
77.1% /
22.9%
Correct /
Incorrect
83.3% /
16.7%
75.3% /
24.7%
66.1% /
33.9%
71.9% /
28.1%
PREDICTED (Operational)
1 2
85 14
Correct /
Incorrect
85.9% / 14.1%
PREDICTED (Structural)
1 2
75 12
Correct /
Incorrect
86.2% / 13.8%
9 27 75.0% / 25.0% 12 35 75.0% / 25.0%
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
Category
0
1
2
Correct /
Incorrect
ALT B
OBSERVED
Category
1
2
Correct /
Incorrect
0
PREDICTED (Operational)
1 2
17 0 17
70 64 6
48
12.6% /
87.4%
16
80.0% /
20.0%
32
58.2% /
41.8%
Correct /
Incorrect
50% / 50%
45.7% /
54.3%
33.3% /
66.7%
41.9% /
58.1%
0
PREDICTED (Structural)
1 2
14
17
12
32.6% /
67.4%
6
37
0
86.0% /
14.0%
10
10
29
59.2% /
40.8%
Correct /
Incorrect
46.7% /
53.3%
57.8% /
42.2%
70.7% /
29.3%
59.3% /
40.7%
PREDICTED (Operational)
1 2
83 11
Correct /
Incorrect
88.3% / 11.7%
PREDICTED (Structural)
1 2
80 7
Correct /
Incorrect
92.0% / 8.0%
18 23 56.1% / 43.9% 32 16 33.3% / 66.7%
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
Category
0
1
2
Correct /
Incorrect
ALT B
OBSERVED
Category
1
2
Correct /
Incorrect
0
PREDICTED (Operational)
1 2
12 6 12
15 37 12
12
30.8% /
69.2%
0
86.0% /
14.0%
29
54.7% /
45.3%
Correct /
Incorrect
40.0% /
60.0%
57.8% /
42.2%
70.7% /
29.3%
57.8% /
42.2%
0
PREDICTED (Structural)
1 2
4 11 2
0 56 14
0
100.0% /
0.0%
27
59.6% /
40.4%
21
56.8% /
43.2%
Correct /
Incorrect
23.5% /
76.5%
80.0% /
20.0%
43.8% /
56.3%
60.0% /
40.0%
PREDICTED (Operational)
1 2
84 10
Correct /
Incorrect
89.4% / 10.6%
PREDICTED (Structural)
1 2
79 8
Correct /
Incorrect
90.8% / 9.2%
17 24 58.5% / 41.5% 30 18 37.5% / 62.5%
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 sewer rehabilitation projects prioritization. PhD Thesis, Faculty of Engineering, Vrije Universiteit Brussel, Brussels, Belgium.
Ariaratnam, T. S.; Assaly, E. A.; Yuqing, Y. (2001). Assessment of infrastructure inspection needs using logistic models.
Journal of Infrastructure Systems, 7(4):66-72.
Baik, H. S.; Jeong, H. S.; Abraham, D. M. (2006). Estimating transition probabilities in markov chain-based deterioration models for management of wastewater systems. Journal of Water Resources Planning and Management, 132(1):15-24.
Baur, R.; Herz, R. (2002). Selective inspection planning with ageing forecast for sewer types. Water Science and Technology,
46(6-7):379-387.
Baur, R.; Zielichowski-Haber, W.; Kropp, I. (2004). Statistical analysis of inspection data for the asset management of sewer networks. In Proceedings 19th EJSW on Process Data and Integrated Urban Water Modeling, Lyon, France.
Chughtai, F; Zayed, T. (2007a). Structural condition models for sewer pipeline. Pipelines 2007: Advances and Experiences with Trenchless Pipeline Projects, 8–11 July, Boston, USA.
Chughtai, F; Zayed, T. (2007b). Sewer pipeline operational condition prediction using multiple regression. Pipelines 2007:
Advances and Experiences with Trenchless Pipeline Projects, 8–11 July, Boston, USA.
Chughtai, F; Zayed, T. (2008). Infrastructure condition prediction models for sustainable sewer pipelines. Journal of
Performance of Constructed Facilities, 22(5):333-341.
Davies, J.; Clarke, B.; Whiter, J.; Cunningham, R. (2001). The structural condition of rigid sewer pipes: a statistical investigation. Urban Water, 3:277-286.
Dirksen, J.; Clemens, F. H. L. R. (2008). Probabilistic modeling of sewer deterioration using inspection data. Water Science &
Technology, 57(10):1635-1641.
Fenner, R. A.; McFarland, G.; Thorne, O. (2007). Case-based reasoning approach for managing sewerage assets.
Proceedings of the Institution of Civil Engineers, Water Management, 160(WM1):15–24.
SPN7 2013 Sheffield, 28-30 August

REFERENCES
Hörold, S.; Baur, R. (1999). Modeling sewer deterioration for selective inspection planning – case study Dresden. In
Proceedings 13th EJSW on Service Life Management Strategies of Water Mains and Sewers, 8-12 September, Switzerland.
Khan, Z.; Zayed, T.; Moselhi, O. (2010). Structural condition assessment of sewer pipelines. Journal of Performance of
Constructed Facilities, 24(2):170-179.
Kleiner, Y. (2001). Scheduling inspection and renewal of large infrastructure assets. Journal of Infrastructure Systems,
7(4):136-143.
Kleiner, Y.; Rajani, B.; Sadiq, R. (2004a). Modeling failure risk in buried pipes using fuzzy Markov deterioration process”, 4th
International Conference on Decision Making in Urban and Civil Engineering, 28-30 October, Porto, Portugal, pp. 1-11.
Kleiner, Y.; Sadiq, R.; Rajani, B. (2004b). Modeling failure risk in buried pipes using fuzzy Markov deterioration process.
Pipelines 2004, Conference Proceedings, ASCE, San Diego, California, USA, pp. 7-16.
Kleiner, Y.; Sadiq, R.; Rajani, B. B. (2006). Modelling the deterioration of buried infrastructure as a fuzzy Markov process.
Journal of Water Supply Research and Technology: Aqua, 55(2):67-80.
Koo, D.-H.; Ariaratnam, S. T. (2006). Innovative method for assessment of underground sewer pipe condition. Automation in
Construction, 15:479-488.
Le Gat, Y. (2008). Modelling the deterioration process of drainage pipelines. Urban Water, 5(2):97-106.
Mashford, J.; Marlow, D.; Tran, T.; May, R. (2011). Prediction of Sewer Condition Grade Using Support Vector Machines.
Journal of Computing in Civil Engineering, 25(4):283-290.
Micevski, T.; Kuczera, G.; Coombes, P. (2002). Markov model for storm water pipe deterioration. Journal of Infrastructure
Systems, 8(2):49–56.
multi-objective data mining. Journal of Hydroinformatics, 11(3–4):211-224.
Najafi, M.; Kulandaivel, G. (2005). Pipeline condition prediction using neural network models. Pipelines 2005, ASCE, Reston,
VA, USA, pp. 767–775.
SPN7 2013 Sheffield, 28-30 August

REFERENCES
Pohls, O. (2001). The analysis of tree root blockages in sewer lines & their prevention methods. MSc. Thesis, Institute of Land and Food Resources, University of Melbourne, Melbourne, Australia.
Savic, D. A.; Giustolisi, O.; Laucelli, D. (2009). Asset deterioration analysis using multi-utility data and
Savic, D.; Giustolisi, O.; Berardi, L.; Shepherd, W.; Djordjevic, S.; Saul, A. (2006). Modelling sewer failure by evolutionary computing. Proceedings of the Institution of Civil Engineers, Water Management, 159(WM2):111-118.
Tran, D. H.; Ng, A. W. M.; Perera, B. J. C.; Davis, P. (2006). Application of probabilistic neural networks in modeling structural deterioration of stormwater pipes. Urban Water Journal, 3(3):175–184.
Tran, H. (2007) Investigation of deterioration models for stormwater pipe systems. PhD Thesis, Victoria University, School of
Architectural, Civil and Mechanical Engineering Faculty of Health, Engineering and Science, Victoria, Australia.
Ugarelli, R.; Kristensin, S. M.; Røstum, J.; Sægrov, S.; Di Frederico; V. (2008). Statistical analysis and definition of blockagesprediction formulae for the wastewater network of Oslo by evolutionary computing. 11th International Conference in Urban
Drainage, Edinburgh, Scotland, UK.
Wirahadikusumah, R.; Abraham, D.; Iseley, T. (2001). Challenging issues in modeling deterioration of combined sewers.
Journal of Infrastructure Systems, 7(2):77-84.
Yan, J.; Vairavamoorthy, K. (2003). Fuzzy approach for pipe condition assessment. Proc., New Pipeline Technologies,
Security, and Safety, ASCE, Reston, Va., pp. 466–476.
Yang, Y. (1999). Statistical models for assessing sewer infrastructure inspection requirements. MSc. Thesis, Department of
Civil and Environmental Engineering, University of Alberta, Edmonton, Alberta, Canada.
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