Study (Prediction) of Main Pipes Break Rates in Water Distribution Systems Using Intelligent and Regression Methods

Document Type : Research Paper

Authors

1 Assoc. Prof., Center of Excellence for Eng. and Management of Infrastructures, School of Civil Eng. Tehran University, Tehran, Iran

2 Instructor, Dept. of Civil Eng., Taft Branch, Islamic Azad University

3 Assist. Prof. of Civil Eng., Zabol University, Zabol

Abstract

Optimum operation of water distribution networks is one of the priorities of sustainable development of water resources, considering the issues of increasing efficiency and decreasing the water losses. One of the key subjects in optimum operational management of water distribution systems is preparing rehabilitation and replacement schemes, prediction of pipes break rate and evaluation of their reliability. Several approaches have been presented in recent years regarding prediction of pipe failure rates which each one requires especial data sets. Deterministic models based on age and deterministic multi variables and stochastic group modeling are examples of the solutions which relate pipe break rates to parameters like age, material and diameters. In this paper besides the mentioned parameters, more factors such as pipe depth and hydraulic pressures are considered as well. Then using multi variable regression method, intelligent approaches (Artificial neural network and neuro fuzzy models) and Evolutionary polynomial Regression method (EPR) pipe burst rate are predicted. To evaluate the results of different approaches, a case study is carried out in a part ofMashhadwater distribution network. The results show the capability and advantages of ANN and EPR methods to predict pipe break rates, in comparison with neuro fuzzy and multi-variable regression methods.
 

Keywords

References

1- Elahi Panah, N. A. (1998). “Subsequent water distribution networks in the country until 1400.” J. of Water and Environment, 28, 4-15. (In Persian)
2- Tabesh, M., and Abedini, A.A. (2005). “Analysis of pipe failure in water distribution network.” J. of Iran Water Resources Research, 1(1), 78-79. (In Persian)
3- Tabesh, M., Jafari, H., and Delavari, M. R. (2005). “Management model of accidents and breaks in water distribution system using GIS.” J. of Waterand Wastewater, 70, 2-15. (In Persian)
4- Newport, R. (1981). “Factors influencing the occurrence of bursts in iron water mains.” Water Supply and Management, 3, 274-278.
5- Walski, T. M., and Pelliccia, A. (1982). “Economic analysis of water main breaks.” J. of AWWA, 74, 140-147.
6- Shamir, U., and Howard, C.D.D. (1979). “An analytical approach to scheduling pipe replacement.” J. of AWWA., 71, 248-258.
7- Kettler, A.J., and Goulter, L.C. (1985). “An analysis of pipe breakage in urban water distribution networks.” Can. J. Civ. Eng., 12(2), 286-293.
8- Su, Y.C., Mays, L.W., Duan, N., and Lansey, K.E. (1987), “Reliability based optimization model for water distribution systems.” J. Hydr. Eng., 114(12), 1539-1556.
9- Sacluti, F. R. (1999). “Modeling water distribution pipe failures using artificial neural networks.” M.Sc. Thesis, Department of Civil and Envir.Eng.,University of Alberta,Canada.
10-Tabesh, M., Aghaei, A., and Abrishami, J. (2008). “Investigation of the effects of influential parameters on pipe burst in water distribution systems using evolutionary polynomial regression method.” J. of Faculty of Engineering (Special issue on: Civil Engineering), 42(6), 691-703. (In Persian)
11- Tabesh, M., Soltani, J., Farmani, R., and Savic, D. (2009). “Assessing pipe failure rate and mechanical reliability of water distribution networks using data driven modeling.” J. of Hydroinformatics, 11(1), 1-17.
12- Farmani, R., Walters, G.A., and Savic, D. (2005). “Trade off between total cost and reliability for anytown water distribution network.” J. Water Resour. Plng. and Mgmt., 131(3), 161-171.
13- Tabesh, M., and Dini, M. (2009). “Fuzzy and neuro- fuzzy models for short-term water demand forecasting in Tehran.” Iranian J. of Science and Technology, Transaction B: Engineering, 33(B1), 61-77.
14- Karamoz, M., Tabesh, M., Nazif, S., and Moridi, A. (2005). “Predict the water pressure in water supply networks and fuzzy inference.” J. of Water and Wastewater, 53, 3-14. (In Persian)
15- Giustolosi, O., and Savic, D.A. (2004). “A novel genetic programming strategy: Evolutionary polynomial Regression.” 6th International Conference on Hydro Informatics, Liong, Phoon and Babovic (eds.),Singapore, 1, 787-794.
16- Tabesh, M.., and Soltani, J. (2007). “Optimal operational management of water sypply systems by evaluation of mechanical reliability using artificial neural networks (ANN).” Proc. of the 2nd National Conference on Iran Water Resources, (2WRM 2007), Faculty of Engineering,IsfahanUniversity ofTechnology,Isfahan,Iran, 23-24. (In Persian)
 
Journal of Water and Wastewater; Ab va Fazilab ( in persian )
Volume 22, Issue 2 - Serial Number 2
Serial number:78
July 2011
Pages 2-14

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