Decision support systems (DSS) for pipeline renewal have mostly focused on asset management to help owners and engineers decide on which portions of a system to prioritize for actions. These models do not typically determine whether to rehabilitate or replace a pipe or how to do it. The U.S. Environmental Protection Agency (EPA) proposed this project to: (1) identify the current methods and tools being used for determining how to rehabilitate pipes; (2) identify gaps in current models through comparison to current utility practices; and (3) assess the feasibility of improving existing approaches.? For more details and a full list of references contained throughout this paper, please see the 2011 U.S. EPA report ?Decision Support for Renewal of Wastewater Collection and Water Distribution Systems? at http://nepis.epa.gov/Adobe/PDF/P100BWUR.pdf.
Review of Decision Making Tools
Few DSSs are commercially available for technology selection as most utilities make decisions based on in-house and consultant expertise (Matthews et al., 2011). This review presents some of the models proposed over the past 15 years for selecting technologies in the U.S. and worldwide. The models are divided into three groups: (I) wastewater collection; (II) water distribution; and (III) general (i.e., models used for both wastewater collection and water distribution). Each model was analyzed based on its ability to: process condition assessment data; consider multiple technologies; perform a technical evaluation for site-specific conditions; perform a cost analysis; and perform a method ranking.
Wastewater Models
Baur et al. (2003) developed a model that included selecting rehabilitation methods at an appropriate time. The model used multi-criteria methodologies to provide decision support, which included choosing the most economic rehabilitation method from a database of more than 40 technologies. Matthews (2006) developed Trenchless Assessment Guide (TAG), which contained an industry vetted evaluation process and database of more than 50 methods. One critical gap was the lack of cost data that was addressed in Matthews (2010), which included a process for evaluating direct and indirect costs.
Water Models
Kleiner et al. (2001) developed a procedure that considered relining and replacement with an equal or larger diameter as options. The approach minimized the total costs associated with the repair of every pipe in the system.
The model lacked specific technologies and their associated costs.? Deb et al. (2002) developed a DSS that included a module for selecting water main renewal methods for mains less than 24-in. and considered five rehabilitation and six replacement technologies. The DSS considered site conditions and cost parameters to select and rank alternatives.
Saegrov (2005) developed a model comprised of five components, with the fifth component containing a rehabilitation strategy to rank technologies. The critical gap of this DSS is the lack of technology-specific information or evaluation of site-specific conditions. Ammar et al. (2010) proposed the first model dedicated to the selection of methods for the repair of water mains. The model focuses on life-cycle costs of commonly used technologies to determine which option was most cost-effective. The critical gaps of the model included lack of validation by industry users. A summary of these models is given in Table 2.
General Models
Hastak and Gokhale (2000) developed a model for selecting rehabilitation technologies for both wastewater and water systems.? The model used the analytical hierarchy process to evaluate user?s requirements, site characteristics, pipe condition and design life. Matthews (2010) developed TAG- Rehabilitation (TAG-R), which focused on rehabilitation technologies for wastewater pipes, storm drains and water mains. The combined model contains data for more than 100 technologies capable of installing and replacing sewer and water pipes. A summary of these models is given in Table 3.
Utility Approaches
In addition to the DSS review, a review of utility decision making approaches was conducted by visiting eight large water/wastewater utilities. Only three of those utilities were using models for rehabilitation decision support while the rest based their decisions solely on in-house expertise and consultant recommendations (Matthews et al., 2012). The City of Atlanta used a Rehabilitation Selection Tool (RST) for a segment-by-segment evaluation of pipes. Evaluation parameters included defect, hydraulic capacity, site photos, pipe characteristics and cost effectiveness of methods. Table 4 summarizes the approaches for each visited utility.
Utility Needs
Each utility participating in the study was asked what other capabilities each would like to see in a DSS tool. The areas mentioned as crucial included: (I) provide more rehabilitation options; (II) use regional cost data; (III) use case histories; and (IV) provide contact information of utility users. Of the models reviewed, most considered areas I and II, while none contained areas III and IV. Interaction among utility users helps drive the use of technologies and allows the utilities to gain insight into issues difficult to quantify through other utilities? lessons learned.
Conclusions and Recommendations
Based on the model review and approaches used by the utilities, four models were identified as offering the best practices for making rehabilitation versus replacement technology selection decisions, which are summarized in Table 5.
Improving on the best practices is feasible if the best aspects of each of the models are incorporated into a single model (i.e., including defect codes from Halfawy and Baker (2009); deterioration data from Ammar et al. (2010); a robust database and industry vetted evaluation process from Matthews (2010); and a platform for incorporating cost data for specific technologies from Atlanta RST). The primary gap in each model considered is the lack of case studies, which is needed for the single model to be useful.
The U.S. Environmental Protection Agency, through its Office of Research and Development, funded and managed, or partially funded and collaborated in, the research described herein. It has been subjected to the Agency?s peer and administrative review and has been approved for external publication. Any opinions expressed in this paper are those of the author(s) and do not necessarily reflect the views of the Agency, therefore, no official endorsement should be inferred. Any mention of trade names or commercial products does not constitute endorsement or recommendation for use.
John C. Matthews is principal research scientist, Battelle, based in Baton Rouge, La., and Ariamalar Selvakumar is an environmental engineer, U.S. EPA, Edison, N.J.
