الفهرس 
IntroductionOnly 14 pages are availabe for public view
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Abstract
The civil infrastructure including highways, bridges, and water/sewer systems, is crucial for economic growth and prosperity. Sustaining the safety and operability of infrastructure networks is a complex task due to the operational, environmental factors and the limited repair budgets. Among the various infrastructure systems, water pipelines networks represent a great challenge due to their diverse components that have different repair requirements. Ideally, an asset management system would include important functions such as condition assessment, deterioration prediction, repair selection, and component prioritization for repair along a planning horizon. Existing systems, however, may not adequately cover all these functions and lack optimization features that are suitable for large scale networks. Research scope:- This research introduces asset management framework to support the efficient planning of maintenance and repair programs for water pipelines. The framework introduces the following novel developments: 1) a simple approach to support speedy and less subjective assessment of the current condition of water pipelines using artificial neural network (ANN); 2) a Markov chain prediction approach for pipelines future conditions along the planning horizon; and 3) a procedure for determining the least-cost strategy to repair pipeline deficiencies in each year of the planning horizon considering practical constraints and user preferences. Research objective:- The primary objective of this research is to investigate the infrastructure asset management process and to structure a comprehensive asset management framework for water mains. The detailed objectives are: Asses the condition of water mains by using Artificial Neural Network. This system will provide guidance during the assessment process to make it faster, cheaper, less subjective, and suitable for less experienced users. Use the condition assessment data to develop deterioration models by Markovian Chain. Analyze the types and costs of different repair scenarios that apply to water mains. Develop a life-cycle-cost model for water main network, considering the deterioration model, least-cost repair strategies and overall condition of network. Then, utilize a non-traditional optimization technique (genetic algorithms) which suits large-scale problems to optimally prioritize assets for repair purposes. Steps Study:- In this research, Data were collected from the Dakahliya Water and Sewer Company (DWSC). Mansoura city water network is collected from the company but this data not fully utilized and is used only for data storage, not for tracking the performance of the network, or for inspection reports. The data for 50 pipelines were provided by the DWSC as a case study for the Water Pipes Management System (WPMS) developed in this study. Some of the data were also collected through interviews with engineers from the company. The data include general information about the water network, such as the pipe material, the pipeline length (m), the pipeline diameter (inch), pipe age (years) and number of breaks. Summary of Study:- It is expected that the proposed framework will aid municipalities and governmental agencies to make appropriate decisions that ensure the sustainable operation of the water mains networks with the least cost and optimum operational condition.