Window-Based Decision Support System for the Water Pipe Condition Assessment using Artificial Neural Network Zong Woo Geem1 1
WESTAT, RW4676, 1650 Research Blvd., Rockville, MD 20850; PH (301) 294-3893; email:
[email protected]
Abstract So far Artificial Neural Network (ANN) has gathered popularities in many engineering fields because of its ability to nonlinearly relate independent and dependent variables. This paper deals with the application of the ANN to the decision support system (DSS) for the pipe condition assessment in water distribution systems. Also the window-based interface is developed using visual tool for enhancing user-friendliness.
Introduction Recently the quality of water in pipe became one of big issues because the water quality is connected directly with public health. Water communities annually invest to preserve pipe integrity by replacing or rehabilitating. However, in order to optimize the process under the limited budget the assessment of approximate pipe condition is necessitated (Dandy and Engelhardt, 2001; Luong and Nagarur, 2001; and Loganathan et al, 2002).
Pipe Condition History
ANN
Present Pipe Data
DSS
Pipe Condition Assessment
Figure 1. Schematic of Pipe Condition Assessment.
1
Artificial Neural Network (ANN) (ASCE, 2000; Oh, 2000) can be a good methodology to assess the pipe condition without excavation. In this paper, the decision support system (DSS) for the pipe condition assessment using ANN is developed shown in Figure 1. The DSS has major two parts: one is training part using historical data and the other predicting part using new data. And the DSS has also the graphic user interface (GUI) for better usage of the system.
Artificial Neural Network for the Pipe Condition Assessment ANN model for the pipe condition assessment is trained based on seven factors such as 1) pipe material, 2) bedding condition, 3) corrosion, 4) temperature, 5) trench width, 6) pipe diameter, and 7) age.
Figure 2. Seven Factors Affecting Pipe Condition.
2
1. Pipe material. Pipe material is divided by three different ones: Cast iron pipe (pit cast), cast iron pipe (centrifugal cast), and ductile iron pipe (centrifugal cast). 2. Bedding condition. Bedding condition is divided by three different ones: skilled bedding, medium bedding, and poor bedding. 3. Corrosion. Corrosion is occurred by stray current, bending stress or so. The level of corrosion is divided by three different ones: small corrosion, medium corrosion, and severe corrosion. 4. Temperature. Temperature is considered in this study as the number of days below 0°C. The level of temperature is divided by six different ones: 0, 1, 2, 3, 4, and 5+. 5. Trench width. Trench width is the factor related with excavation ranged from 0 to 1. 6. Pipe diameter. Pipe diameter is divided by 26 different ones from 1 to 120 inches. 7. Pipe age. Pipe age is considered in this study as the constructed year ranged from 1900 to 2000. Figure 2 shows the database screen storing historical data of seven factors and corresponding pipe condition. The data in this study is arbitrary randomized.
Figure 3. Historical Data Training
3
If the [training] menu in Figure 2 is clicked, DOS-window is opened as shown in Figure 3, and the ANN model is trained based on error back-propagation algorithm.
Application of ANN model to the Test Network ANN model for the pipe condition assessment is applied to the ten-pipe test network shown in Figure 4. If the first button [Network Information] is clicked, current data of each pipe is automatically transmitted. Then, if the second button [Network Prediction] is clicked, the condition of each pipe is assessed based on trained ANN model.
Figure 4. Test Network for DSS
4
If the third button [Pipe Prediction] is clicked, pipe condition checker appears as shown in Figure 5. Using the pipe condition checker, we can assess any pipe condition depending on trained ANN model. For example, Figure 5 indicates that the assessed condition is 0.51 (medium deteriorated condition) when 1) pipe material is ductile iron pipe (centrifugal cast), 2) bedding condition is medium, 3) corrosion rate is severe, 4) number of frozen days is four, 5) trench width is 9, 6) pipe diameter is 72 inch, and 7) pipe constructed year is 1953.
Figure 5. Pipe Condition Checker
Conclusions The new DSS for the pipe condition assessment using ANN is presented in this study. The DSS consists of three parts such as database, ANN, and GUI: The database stores historical data of pipe condition; the ANN is trained by historical data using back-propagation algorithm; and The GUI can make users more friendly use the system. The data used in this study is arbitrary generated. However, real data is later required for real world application.
References ASCE Task Committee on Application of Artificial Neural Networks in Hydrology. (2000). “Artificial Neural Networks in Hydrology. I: Preliminary Concepts." Journal of Hydrologic Engineering, ASCE, 5(2), 115-123. Dandy, G. C., and Engelhardt, M. (2001). “Optimal Scheduling of Water Pipe Replacement Using Genetic Algorithms.” Journal of Water Resources Planning and Management, ASCE, 127(4), 214-223.
5
Loganathan, G. V., Park, S., and Sherali, H. D. (2002). “Threshold Break Rate for Pipeline Replacement in Water Distribution Systems.” Journal of Water Resources Planning and Management, ASCE, 128(4), 271-279. Luong, H. T., and Nagarur, N. N. (2001). “Optimal Replacement Policy for Single Pipes in Water Distribution Networks.” Water Resources Research, 37(12), 3285-3293. Oh, C. S. (2000). Introduction to the Neurocomputer. Naeha, Seoul, Korea.
6
