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Louisville MSD Integrates Sewer Pipe Probability of Failure and Consequence of Failure to Guide Their Continuing Sewer System Assessment Program

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In August 2005, Louisville & Jefferson County Metropolitan Sewer District (MSD) signed a Consent Decree with United States Environmental Protection Agency (EPA) and the Kentucky Environmental Protection Cabinet (EEPC). Under the terms of the Consent Decree, MSD completed a Capacity, Management, Operations and Maintenance (CMOM) Self Assessment in 2006 to identify programs or activities that could improve or enhance MSD's level of service or compliance performance. One of the tasks recommended in the CMOM Self Assessment was to develop a Continuing Sewer System Assessment (CSSA) Program.

The overall purpose of the CSSA program was to reduce sewer overflows caused by blockages, pipe failure, or other maintenance related causes. MSD decided to develop a sewer maintenance program using a risk-based asset management approach. Therefore, MSD focused their early planning efforts on selecting appropriate asset management tools and processes and developing the two primary risk components of the risk equation; the asset's probability of failure (POF) and consequence of failure (COF). MSD purchased Hansen&s Advanced Asset Management (AAM) software module and hired CH2M HILL to help develop localized POF curves and tables and COF tables.

MSD has a long history of using the Hansen Information Management System (IMS) as its central depository for collection system information. Therefore, Hansen's s AAM provided for efficient data transfer and analysis. The Hansen IMS also links to the Louisville/Jefferson County Information Consortium database, which is the community's s Geographic Information System. This link enables MSD to spatially display the information contained in Hansen, in GIS software and on maps, for analysis and presentation purposes. The AAM module software uses the pipe material service life (remaining life) tables and sewer condition (condition decay) tables to calculate POF for various pipe materials. The software also uses the sewer Consequence of Failure (COF) information to calculate the asset&s COF score. The COF of an asset was measured by developing a table that included MSD&s Wet Weather Team's s stakeholder consensus values for overflow abatement plotted on one axis and developed criteria that was scaled and plotted on the other axis to measure the severity to each value under the presumption that the asset had failed. Consequence depend largely on the type and size of the asset, as well as the assets location in the community whether near a stream or under a major thoroughfare. When combined in the AAM, the POF and COF produced the risk score for the sewer asset.

MSD knew the condition assessment process could not rely on data strictly from detailed closed circuit television (CCTV) inspections of the pipe's interior. Rather, in order to productively keep their asset management program moving forward, MSD outlined three condition assessment levels based on progressive knowledge of the system and pipe condition. As MSD's knowledge about the asset improved, then the higher integrity data would be used to recalculate POF in the risk equation. The three data integrity levels were:

Level 1: POF curves and tables were developed based simply on pipe material and pipeservice life age. The pipe material and installation date were readily available or assumed and the POF quickly computed from MSD's existing Hansen IMS.

Level 2: Level 1 conditions were overridden based on the condition assigned to the pipe from the staff's institutional knowledge of the pipe condition gained from work performed on the sewers and captured in the work order and computer maintenance management systemsuch as MSD's s Hansen and SAP software.

Level 3: Level 1 and 2 conditions were modified based on the aggregate of availableinformation gathered from multiple activities associated with the sewers. Informationincluded the pipe's s attribute information and investigation data such as; external factors and stresses acting on the pipe, and internal pipe condition obtained based on CCTV and other inspection data. Sewer modeling and flow monitoring also provides condition data on areas experiencing high levels of inflow and infiltration, which may move areas up in priority.

To test this approach, MSD decided to apply the asset management process to a representative segment of the conveyance system before applying to their whole system. The segment consisted of approximately 168,000 linear feet of gravity sewer above MSD's s combined sewer discharge facility number CSO 166. The segment included both combined and sanitary sewer pipes ranging from 6 to 123 inch-diameters and variable pipe age and material. Factors that influenced the decision to focus on an initial segment were the intricacies of the Advanced Asset Management software and its relationship definition with the Hansen IMS; their historical assessment data formats and availability; and the integration and computation of the large data sets associated with the whole gravity sewer system. MSD would have more time to work through issues that would be certain to arise and to refine and document the protocols and necessary preparatory tasks for system wide application of the asset management activities.

Once the Hansen CMMS and AAM software were integrated and the data mapping and protocols established for the initial service area, the AAM computed the risk score for each asset. The AAM module allowed MSD to query and report the results in various formats and sorting arrangements. The paper will provide examples of the risk prioritization results and the projected cost of rehabilitating the different asset groups in the representative sample based on cost tables integral to the AAM module. Also, the paper will discuss what MSD learned from the initial segment service area and is applying to the remaining portion of the conveyance system and CSSA program.

This paper will benefit anyone who needs to start or enhance their asset management program, whether to comply with CMOM regulatory requirements or employ good engineering practices. The intent and usefulness of the program will be covered as well as the different approaches in determining risk for an asset. Integration of the CSSA with existing utility programs and mixing condition ratings based on inspection and remaining life will be included, emphasizing the need for an aggressive, prioritized inspection program with proactive rehabilitation. The results of the literature research conducted to develop the localized remaining service and condition decay curves for several different pipe materials will also be presented.

Keywords: Asset Management; CMOM; Condition Assessment; Condition Decay; Consequence of Failure; Continuing Sewer System Assessment; Probability of Failure; Remaining Life; Risk; Service Life

Document Type: Research Article


Publication date: January 1, 2009

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