SPN7 2013 Sheffield, 28-30 August

RISK ASSESSMENT OF SEWER CONDITION USING ARTIFICIAL INTELLIGENCE TOOLS Application to the SANEST sewer system

Vitor SousaIST, UTL

José Pedro MatosIST, UTL

Nuno Marques AlmeidaIST, UTL

José Saldanha MatosIST, 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 TYPE REFERENCES

Function-based Deterministic Linear regression Chughtay and Zayed (2007a, 2007b, 2008)

Non-linear regression Newton and Vanier (2006); Wirahadikusumah et al. (2001)

Stochastic 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 Tran (2007); Ana (2009)Data-based Artificial inteligence Artificial Neural Networks – ANNs 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 Fenner et al. (2007)Support Vector Machines – SVMs 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

Material / Diameter Sewers [nº] Total length [m] Average age [years] Average depth [m] Average slope [%] Average length [m]

VC (1) 134 4370.50 54.55 2.52 2.14 32.62200 7 186.13 45.00 2.68 1.32 26.59250 15 389.41 58.13 2.41 1.09 25.96300 38 1232.85 49.74 1.98 2.95 32.44350 69 2484.68 58.17 2.82 1.83 36.01400 1 42.23 39.00 2.31 1.11 42.23

PC (2) 53 1408.70 29.85 2.47 2.08 26.58315 1 51.26 30.00 2.73 2.09 51.26500 52 1357.44 29.85 2.47 2.08 26.10

PVC (3) 348 12682.20 11.53 2.88 1.72 36.44200 3 80.44 8.00 2.19 7.22 26.81250 59 2291.46 10.37 2.34 4.14 38.84315 38 957.03 12.39 2.46 0.90 25.19400 112 4347.90 11.59 2.98 1.75 38.82500 73 2868.81 12.26 3.03 0.87 39.30630 27 1132.64 10.37 3.12 0.81 41.95700 30 915.38 12.00 3.47 0.53 30.51800 6 88.54 12.00 3.47 0.34 14.76

HDPE (4) 122 4102.04 9.84 3.53 1.23 33.62360 38 1206.47 10.00 3.70 0.96 31.75400 4 111.03 9.75 3.31 1.68 27.76450 4 217.33 9.00 2.07 1.26 54.33500 66 2154.48 9.92 3.76 1.50 32.64600 10 412.73 9.00 2.08 0.27 41.27

C-PP (5) 60 1771.99 9.65 3.02 1.51 29.53315 26 908.06 9.96 4.42 2.83 34.93400 4 122.89 12.00 3.23 0.26 30.72500 29 713.70 9.03 1.72 0.46 24.61630 1 27.34 10.00 3.40 2.71 27.34

C-PVC (6) 28 1033.74 4.42 3.87 1.24 39.76350 7 165.00 6.20 2.83 2.71 33.00400 21 868.74 4.00 4.12 0.89 41.37

Total 745 25369.17 19.92 2.94 1.71 34.14

4. DATA COLLECTION

SPN7 2013 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

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%).

Classification Case

Train Algorithm

Error Function

Correlation Number of neurons Activation function

Train Test Hidden Layer Output Layer Hidden Layer Output Layer

Operational – ALT A BFGS CE 61.80 66.67 15 3 Hiperbolic

Tangent Softmax

Structural – ALT A BFGS SOS 68.52 71.85 29 3 Hiperbolic

TangentSigmoid Logistic

Operational – ALT B BFGS CE 80.00 82.96 19 2 Sigmoid

Logistic Softmax

Structural – ALT B BFGS SOS 75.74 82.22 18 2 Sigmoid

LogisticSigmoid Logistic

SPN7 2013 Sheffield, 28-30 August

6. ARTIFICIAL NEURAL NETWORKS

ALT A

ALT B

OBSERVED PREDICTED (Operational) Correct / Incorrect

PREDICTED (Structural) Correct / IncorrectCategory 0 1 2 0 1 2

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%

OBSERVED PREDICTED (Operational) Correct / Incorrect

PREDICTED (Structural) Correct / IncorrectCategory 1 2 1 2

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

ALT B

OBSERVED PREDICTED (Operational) Correct / Incorrect

PREDICTED (Structural) Correct / Incorrect

Category 0 1 2 0 1 2

0 17 0 17 50% / 50%14 6 10

46.7% / 53.3%

1 70 64 6 45.7% / 54.3% 17 37 10

57.8% / 42.2%

2 48 16 32 33.3% / 66.7% 12 0 29

70.7% / 29.3%

Correct / Incorrect

12.6% / 87.4%

80.0% / 20.0%

58.2% / 41.8%

41.9% / 58.1% 32.6% /

67.4%86.0% / 14.0%

59.2% / 40.8%

59.3% / 40.7%

OBSERVED PREDICTED (Operational) Correct / Incorrect

PREDICTED (Structural) Correct / IncorrectCategory 1 2 1 2

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

ALT B

OBSERVED PREDICTED (Operational) Correct / Incorrect

PREDICTED (Structural) Correct / Incorrect

Category 0 1 2 0 1 2

0 12 6 12 40.0% / 60.0% 4 11 2 23.5% /

76.5%

1 15 37 12 57.8% / 42.2% 0 56 14 80.0% /

20.0%

2 12 0 29 70.7% / 29.3% 0 27 21 43.8% /

56.3%Correct / Incorrect

30.8% / 69.2%

86.0% / 14.0%

54.7% / 45.3%

57.8% / 42.2%

100.0% / 0.0%

59.6% / 40.4%

56.8% / 43.2%

60.0% / 40.0%

OBSERVED PREDICTED (Operational) Correct / Incorrect

PREDICTED (Structural) Correct / IncorrectCategory 1 2 1 2

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 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.

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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 blockages-prediction 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 SousaIST, UTL

José Pedro MatosIST, UTL

Nuno Marques AlmeidaIST, UTL

José Saldanha MatosIST, UTL

http://www.toledoblade.com/Police-Fire/2013/07/06/Sewer-repairs-start-after-intersection-collapse-Copy.html