MARC details
| 000 -LEADER |
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| fixed length control field |
06419ntm a22002537a 4500 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
201210b2010 a|||f bm|| 00| 0 eng d |
| 040 ## - CATALOGING SOURCE |
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| Original cataloging agency |
EG-CaNU |
| Transcribing agency |
EG-CaNU |
| 041 0# - Language Code |
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| Language code of text |
eng |
| Language code of abstract |
eng |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
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| Classification number |
624 |
| 100 0# - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Ahmed Mohamed Atef Ali |
| 245 1# - TITLE STATEMENT |
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| Title |
Optimal Condition Assesment Policies for Water and Sewer Information |
| Statement of responsibility, etc. |
Ahmed Mohamed Atef Ali |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. |
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| Date of publication, distribution, etc. |
2010 |
| 300 ## - PHYSICAL DESCRIPTION |
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| Extent |
p. |
| Other physical details |
ill. |
| Dimensions |
21 cm. |
| 500 ## - GENERAL NOTE |
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| Materials specified |
Supervisor: Osama Moselhi |
| 502 ## - Dissertation Note |
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| Dissertation type |
Thesis (M.A.)—Nile University, Egypt, 2010 . |
| 504 ## - Bibliography |
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| Bibliography |
"Includes bibliographical references" |
| 505 0# - Contents |
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| Formatted contents note |
Contents:<br/>Chapter 2 <br/>Literature review <br/>2.1 Condition Assessment of Water and Sewer Infrastructure <br/>2.1.1 Indirect Inspection for water networks <br/>2.1.2 Indirect inspection techniques for wastewater infrastructures <br/>2.1.3 Direct Inspection for sewage and water systems: <br/>2.2 Value of information and Reliability of information <br/>2.3 Infrastructure Risk Assessment<br/>2.3.1 Risk assessment guidelines <br/>2.3.2 Grand Central model <br/>2.4 Infrastructure Performance Modeling <br/>2.4.1 Physical models <br/>2.4.2 Statistical models <br/>2.5 Markov Decision Process <br/>2.6 Level of service (LOS<br/>Chapter 3 <br/>Level of Service for Water and Wastewater Services <br/>3.1 Level of Service (LOS) <br/>3.2 Achieving customers expectation <br/>3.3 Contingent Valuation <br/>3.3.1 Contingent Valuation <br/>3.3.2 Practices of Contingent Valuation <br/>3.4 Planning and Design the water and wastewater services survey <br/>3.5 Commodity definition and payment method in WWS <br/>3.6 Survey Instrument Design<br/>3.7 Survey method and results <br/>3.8 Data Analysis <br/>3.8.1 Water main breaks <br/>3.8.2 Water outage <br/>3.8.3 Water quality <br/>3.8.4 Water pressure <br/>3.9 Improvement scenarios for water main breaks <br/>3.10 Improvement scenarios for water outage: <br/>3.11 Improved scenarios for water quality: <br/>3.12 Improved scenarios for water pressure: <br/>3.13 Summary and Conclusion <br/>Chapter 4 <br/>Costs of infrastructure failure <br/>4.1 Socio-economic costs<br/>4.2 Grand central rationale <br/>4.3 Failure costs types <br/>4.4 Reduced LOS costs and failure costs <br/>4.5 Administrative and legal costs of damage settlement <br/>4.6 Lost of product costs <br/>4.7 Public safety (Police and emergency services) <br/>4.8 Repair and return to service costs. <br/>4.9 Service outage Mitigation costs. <br/>4.10 Utility emergency response costs<br/>4.11 Customer Outage Cost (COC): <br/>4.12 Traffic disruption cost (TDC): <br/>4.13 Health damages. <br/>4.14 City of Hamilton risk model <br/>Chapter 5 <br/>Estimating the value of condition assessment information<br/>5.2 Assumption of Perfect information <br/>5.3 Assumption of POMDP <br/>5.4 Modified POMDP <br/>5.4.1 Optimal condition assessment policy Model (OCAP) <br/>5.4.2 Pipe segment condition assessment selector model (PSCAS) <br/>5.5 Pipe Network Condition Assessment Optimizer (PNCAO) <br/>5.5.1 Genetic Algorithm <br/>5.5.2 Optimization procedures for PNCAO <br/>Chapter 6 <br/>Case Study: City Of Hamilton, Canada <br/>6.1 Problem statement <br/>6.2 Segment Level analysis by PSCAS <br/>6.3 Network Level Analysis by PNCAO |
| 520 3# - Abstract |
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| Abstract |
Abstract:<br/>With ageing water and sewer infrastructure across the globe, assessing the condition of these assets has received increased attention in recent years. Condition assessment is an integral component in any asset management program. Determining the condition of buried infrastructure tends to be more cumbersome, costly and error-prone compared to other surface infrastructure like roads and buildings. For sewers, CCTV is considered the industry standard for condition assessment technologies. However, for pressurized water pipelines, technologies tend to be more costly and uncertain (e.g. electromagnetic, sonar, acoustic leak detection, infrared etc…). Faced with constrained budgets and the need to obtain reliable condition information to drive their asset management processes, infrastructure owners must balance the value of information obtained through condition assessments with the cost of obtaining this information. Such a framework is sough in order to rationalize the condition assessment processes by striking an elegant balance between the assets attributes (Level Of Service (LOS) and Asset Criticality) with the condition assessment attributes (cost of condition assessment and value of information).<br/>This research aims to develop a decision support system (DSS) to aid decision makers in selecting optimal policies for water and sewers infrastructures. This is addressed by<br/>III<br/>capturing the deterioration behavior of the assets with the impact of information reliability on critical assets by using the Reduced LOS costs and failure costs.<br/>The objectives of this research are; 1) Capturing customers expectation to establish a customer oriented LOS in Egypt, 2) Determining the reduced LOS costs and costs arising from infrastructures failures, 3) Creating a DSS for optimal condition assessment polices by balancing the value of information with the cost of information, 4) Linking the DSS to the Geographic information systems (GIS) environment to visualize the results.<br/>The user oriented LOS was formulated by contingent valuation for the willingness to pay (WTP) for different LOS ranges. Reduced LOS costs and failures costs are shaped on the basis of previously developed model by AWWRF (2001) by considering direct and indirect costs. The decision support system then utilizes Partially Observable Markov Decision Process (POMDP) to compare between the reliability and cost of the condition assessment technology. For the DSS, two models were developed to find the optimum policy at asset level and network level. At asset level, the Pipe Segment Condition Assessment Selector (PSCAS) model is developed to examine and select based on the impact of various inspection technologies on failure and recued LOS costs. At network level with budget constraints, Pipe Network Condition Assessment Optimizer (PNCAO) utilizes a genetic algorithm to find near optimum policy on non-stationary bases. The Decision support system and G.A. model are implemented on VBA code running in Excel environment and linked to GIS. The system is demonstrated on the water and sewer networks for the City of Hamilton, Canada. |
| 546 ## - Language Note |
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| Language Note |
Text in English, abstracts in English |
| 650 #4 - Subject |
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| Subject |
Construction Engineering and Management |
| 655 #7 - Index Term-Genre/Form |
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| Source of term |
NULIB |
| focus term |
Dissertation, Academic |
| 690 ## - Subject |
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| School |
Construction Engineering and Management |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
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| Source of classification or shelving scheme |
Dewey Decimal Classification |
| Koha item type |
Thesis |
| 650 #4 - Subject |
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| -- |
192 |
| 655 #7 - Index Term-Genre/Form |
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| -- |
187 |
| 690 ## - Subject |
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| -- |
192 |