الفهرس 
IntroductionOnly 14 pages are availabe for public view
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Abstract
In major cities worldwide, deterioration of water mains is a condition process; the need to improve the condition of water mains becomes crucial. Condition assessment of aging assets and infrastructure is unacceptable in Egypt. It is spend a lot of money in year. Consequently, most municipalities face a great challenge of managing these replacement and new installation projects efficiently. One of these challenges is how to assess the condition rating of buried water mains. This is because water mains are typically underground, operated under pressure, and usually in accessible. While most municipalities have taken proactive measures in repairing, rehabilitating and replacing deteriorated water mains, more investigation still needs to be done in order to assist municipalities to make right decisions on limited budgets. Building deterioration models and prioritizing water main rehabilitation are more important than ever.
To assess the condition of Water Mains, the present study focuses on the collection of water main data from municipalities and the predict of water main breaks as a representative of the structural deterioration. Data have been collected from Side Salem municipalities that is easy to collect data and have large water distribution networks with a total water main length of 308.12Km. Condition rating models are developed to predict the annual breaks of individual Water Mains, which show robust condition rating for water mains using two different approaches; (i) artificial neural network (ANN) and (ii) regression by using Statistical Package Social Sciences (SPSS). The models is developed more factors in order to increase the accuracy and efficiency of the developed framework and model. Based on the developed ANN& regression model, deterioration curves are generated for Polyvinyl Chloride pipes (PVC) and Asbestos Cement pipes (AC).