Presentation on theme: "A GIS-Based Decision Support System for Optimal Renewal Planning of Sewers INFRA 2007 5-7 November 2007 Québec Mahmoud Halfawy, Leila Dridi, & Samar Baker."— Presentation transcript:
A GIS-Based Decision Support System for Optimal Renewal Planning of Sewers INFRA November 2007 Québec Mahmoud Halfawy, Leila Dridi, & Samar Baker NRC-Centre for Sustainable Infrastructure Research
2 November 6, 2007 Background Fragmentation of sewer management data and processes. Need for proactive and optimized renewal planning. State-of-practice in sewer management software. Challenges for integrating sewer management data, processes, and software systems.
3 November 6, 2007 NRC-CSIR Integrated Asset Management Project Objective: –Develop consistent/generalized models and protocols for asset management process systematization and data integration – Bridging the vertical (departmental) and horizontal (cross- disciplinary) gaps. –Develop algorithms and a set of interoperable GIS-based decision support tools for optimizing and coordinating asset management plans for water, sewer, and road networks. Project Partners: City of Regina.
5 November 6, 2007 The Renewal Planning Step-Wise Algorithm Year = 1 Calculate condition index Calculate risk index Calculate prioritization ranking Select feasible rehabilitation options Prepare Asset Data Repository Define planning horizon Specify condition/risk minimum requirements Specify budget scenario Specify option criteria & run MOO & calculate Pareto fronts Select a satisfying solution Revise budget scenario and/or condition/risk requirements Create Delta Tables yes no Print Renewal plan for one scenario yes no Year = Year + 1 End Apply Delta Tables Solution found? Year = horizon?
6 November 6, 2007 GIS and Data Management Services Intranet/ Extranet Sewer Management Stakeholders Integrated Asset Data Repository Spatial and Inventory Data Performance Data Maintenance and rehabilitation Data Financial/Cost Data Deterioration/Life cycle cost Data Simulation Models/Results Inspection and Condition Data References to other databases (ERP, CIS, SCADA, etc.) Work Order and Operational Data Data and Process Integration Using Centralized Shared Repositories
7 November 6, 2007 Part of the UML class diagram for the integrated sewer data model
8 November 6, 2007 Renewal Planning Algorithm Implementation Define an integrated data model and build asset data repository (inventory, hydraulic data, condition data, repair/incidence records, risk parameters, cost data, service levels, etc.). Define an integrated condition rating index. Define /calibrating deterioration curves. Define risk assessment model. Define asset prioritization criteria. Define renewal technologies database, and algorithm for selecting feasible options. Define a multi-objective optimization (MOO) algorithm (maximize condition, minimize risk, and minimize budget).
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13 November 6, 2007 Sanitary sewers and Vitrified Clay (2881 records) Sanitary sewers and pipe condition = 3 (823 records)
14 November 6, 2007 Deterioration Modeling Largely depends on the quantity and quality of available condition data. –May use deterministic or probabilistic models –Employs many different techniques: regression analysis, Markov processes, ANN, fuzzy models, etc. Our approach: –Store a library of known or previously calibrated models –If a sewer or a group has sufficient data to do regression analysis, define a new model or calibrate an existing model. –If data is not enough, assist user to select a “suitable” model based on his/her intuition/experience with the system and the data available. –As more data become available, the models can be re-defined or re- calibrated.
17 November 6, 2007 Risk Assessment & Prioritization Models Risk = Consequence of Failure * Probability of Failure Criticality factors affecting consequence of failure: –Sewer type, diameter, depth, embedment soil, land use, road classification, traffic volume, proximity to critical assets, socio-economic impact, site seismicity, etc. Procedure: –Calculate a Risk Factor (1-5 scale) that reflects the consequence of failure using a weighted average equation. –Calculate the likelihood of failure index (LFI) for the sewer, based on its current age and expected service life. –Risk Index = RF * LFI Prioritization ranks sewers based on their “priority index” (1-5 scale) to select candidates for renewal. Priority index is derived from the condition index and risk indices according to a set of user-defined rules.
18 November 6, 2007 Renewal Methods Selection Tool Applicability criteria for method selection: –Sewer characteristics (diameter, material, depth, type: gravity or pressure, structural condition state, hydraulic capacity, etc.) –Method characteristics (renewal type (NS/SS/FS), limitations, site and installation requirements) –Site characteristics (soil type, traffic, water table, etc.) Cost vs. Condition Improvement: –Expected condition improvement –Technology construction cost –Technology overall cost (socio-economic cost) –Expected operational cost after improvement
19 November 6, 2007 Renewal Methods Selection Tool (Cont.) Pipe Bursting Pipe Removal Replacement Sliplining CIPP Close fit pipe Formed in place Thermoformed Spiral wound Panel lining UCL Fully Structural Semi-Structural Non-Structural Renewal Technologies Open-Cut(Dig) In-Line Off-Line Horizontal Directional Drilling (HDD) Pipe Jacking Micro-Tunelling Auger Boring Rehabilitation - Lining
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21 November 6, 2007 GA-Based Multi-Objective Optimization Tool Trade-offs of the renewal costs vs. network condition and risk improvements. Three objective functions for cost, condition, and risk. Solution using the NSGA II algorithm and the Open Beagle class library. Calculate 2 Pareto fronts for condition-cost and risk- cost criteria. Evaluate feasible solutions and select or synthesize a solution.
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23 November 6, 2007 Conclusions & Future Work Integrated approaches and DSSs are critical for supporting proactive asset management strategies. The proposed approach and software prototype provided promising results. Work is ongoing to refine/improve several models employed in the prototype, and to validate the software with more data sets. These activities are conducted in collaboration with the City of Regina as well as other industry partners. The software modular architecture facilitated incremental development and testing, and would also facilitate future extensions, refinement, and interoperability with other (e.g. legacy) software systems. Need to define an industry-wide agenda for developing and adopting open/standard integrated data & process models, and software architectures.