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LITERATURE REVIEW
RISK MODEL FOR WATER AND WASTEWATER PIPELINES
Many research efforts have been undertaken to predict the likelihood and consequence of failure of water and wastewater pipelines. As a result, different models using various methodologies and parameters have surfaced. Rajani and Kleiner, 2001 [18] have done a comprehensive review and critiqued the deterministic and stochastic based physical and mechanical models for water pipeline failures. Also, most of the models developed are not realistic to the actual field results and thus not widely used by the water and wastewater utilities. Most of the models found in the literature and in current practice deal with the likelihood of failure of water and wastewater pipelines. The development of the consequence of failure models is still burgeoning. Different water main deterioration models have been developed to facilitate the prediction of asset condition and the possibility of failure [19]. Sadiq et al., 2004 [20] developed a methodology to evaluate the time dependent reliability of underground grey cast iron water mains and to identify the factors that contribute to the water main failures. Also for wastewater pipelines, J.P. Davies et al., 2001 [21] have reviewed various factors that have been recognized as influencing the structural stability of wastewater pipes. The authors have also described the general process of wastewater pipe deterioration and failure by categorizing the factors into three main groups: construction features, local external factors and other factors. Kleiner et al., 2004 [22] developed a fuzzy rule based, non-homogeneous Markov process to model the deterioration of buried pipes. Also, a methodology to assess the pipeline condition rating using multi-criteria decision making (MCDM) was proposed by Yan and Vairavamoorthy, 2003 [7]. Significant models to determine the failure risk of water and wastewater pipelines have also been developed, but these models rely on very few parameters. Based on the feedback from utilities, the trustworthiness of the models is minimal. Rogers, 2006 [23] developed a model to assess water main failure risk using the weighted average method which is based on the Power Law form of a NonHomogeneous Poisson Process (NHPP) and Multi-Criteria Decision Analysis (MCDA). A fuzzy logic based methodology to evaluate pipeline failure risk was developed by Kleiner et al., (2006) [24], but the practicality of this model is not yet evident.MWH, a consulting company in the wet infrastructure area, has developed a software ―MWH Soft‘s CapPlan Sewer‖ [25], which takes into consideration of the likelihood and consequence of failure for wastewater pipelines to calculate risk. Based on limited parameters to determine risk, the software uses a simple method and a matrix method to determine the risk associated with the failure of wastewater pipelines. Australian and New Zealand Standard [6] for Risk Management has determined a five-step standard risk management process, which is a. Establish the context, b. Identify Risks, c. Analyze the Risks, d. Evaluate the risks, and e. Treat the risks. Salem et al., 2003 [26] developed a network level risk-based model for highway infrastructure to estimate life-cycle costs for evaluating infrastructure rehabilitation and construction alternatives. Zayed et al., 2008 [27] proposed a network level risk model tool for highway infrastructure to evaluate sources of risk and uncertainty to prioritize highway construction projects. This research identified sources of risk and uncertainty for highway projects and developed the risk-based model tool using analytical hierarchy process.The risk model used by Washington Suburban Sanitary Commission (WSSC), an eighth largest utility in the United States, for their water and wastewater pipelines considered the following parameters to determine the risk [28] : a. Land Use Factors (LF), b. Repair History (RH), c. Operational Needs (ON), d. Known Manufacturing Defects (KD), e. Last Inspected (LI), and f. Diameter (DI). The model then uses a simple mathematical formula: Risk = (RH+DI+KD) * (ON*4+LI) * (LF). The major limitations of this model are that the environmental factors, financial impact, and social issues in the event of a pipe failure are not taken into consideration. Another major risk model reviewed was from the Commonwealth Scientific and Industrial Research Organization (CSIRO), Australia. Their risk model took into consideration of the following parameters to determine the risk associated with water and wastewater pipelines: a. Climate, b. Demographics, c. Natural Influences, d. Malicious Activity, e. Existing Operational Environment, f. Societal Influences, and g. Financial Impact. The major limitations of this model were that it was a qualitative model to define risks in a system rather than to determine risk.
1. INTRODUCTION
1.1 RESEARCH OVERVIEW
1.2 THESIS STRUCTURE
2. LITERATURE REVIEW
2.1 RISK MODEL FOR WATER AND WASTEWATER PIPELINES
2.2 WEB-BASED AND GEOSPATIALLY ENABLED TOOL FOR WATER AND WASTEWATER PIPELINE INFRASTRUCTURE
2.3 KEY FINDINGS OF LITERATURE REVIEW
3. NETWORK LEVEL MODEL FOR WATER AND WASTEWATER PIPELINE INFRASTRUCTURE RISK MANAGEMENT
3.1 ABSTRACT
3.2 INTRODUCTION
3.3 RISK MODEL DEVELOPMENT
3.3.1 DATA FOR THE RISK MODEL
3.3.2 MODEL ASSUMPTIONS
3.3.3 IDENTIFYING THE PARAMETERS
3.3.4 PARAMETER WEIGHTS AND RANGES
3.3.5 QUANTITATIVE INDEX MODEL AND SCALE
3.3.6 GIS ANALYSIS
3.3.7 DISPLAY RESULTS
3.3.8 MODEL LIMITATIONS
3.4 CASE STUDY
3.5 CONCLUSION
4. WEB-BASED AND GEOSPATIALLY ENABLED PROOF OF CONCEPT FOR WATER AND WASTEWATER PIPELINE INFRASTRUCTURE RISK MANAGEMENT
4.1 ABSTRACT
4.2 INTRODUCTION
4.3 WEB-BASED AND GEOSPATIALLY ENABLED PROOF OF CONCEPT DEVELOPMENT
4.3.1 GIS DATA
4.3.2 SERVER ARCHITECTURE
4.3.3 ArcGIS API FOR FLEX AND FLEX FRAMEWORK
4.3.4 WEB-BASED GEOSPATIAL TOOL
4.4 PILOT STUDY
4.5 CONCLUSION
5. SUMMARY, CONCLUSION, AND FUTURE RESEARCH
5.1 SUMMARY
5.2 CONCLUSION
5.3 FUTURE RESEARCH
6. REFERENCES
APPENDIX A
APPENDIX B
APPENDIX C
