Caso de Estudo - University of Sheffield

Caso de Estudo - University of Sheffield

RISK ASSESSMENT OF SEWER CONDITION USING ARTIFICIAL INTELLIGENCE TOOLS Application to the SANEST sewer system Vitor Sousa IST, UTL Jos Pedro Matos IST, UTL Nuno Marques Almeida IST, UTL Jos 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 CARES 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 Hrold 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 [n] 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 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, 811 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, 811 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):1524. SPN7 2013 Sheffield, 28-30 August REFERENCES Hrold, 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):4956. multi-objective data mining. Journal of Hydroinformatics, 11(34):211-224. Najafi, M.; Kulandaivel, G. (2005). Pipeline condition prediction using neural network models. Pipelines 2005, ASCE, Reston, VA, USA, pp. 767775. 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):175184. 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.; Rstum, J.; Sgrov, 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. 466476. 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 Jos Pedro Matos IST, UTL Nuno Marques Almeida IST, UTL Jos 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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