WEF Collection Systems Conference 2017

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1 Large Scale, Priority-Driven Field Data Collection through to Rehabilitation: How DeKalb County Department of Watershed Management Managed Multiple Assessment Contractors to Target and Coordinate Sewer Rehabilitation Darren Eastall 1, Courtney Call Kennedy 2, Burhan Shaikh 2, Reggie Rowe 3 1 DeKalb County Department of Watershed Management 2 CH2M 3 HKA ABSTRACT DeKalb County Department of Watershed Management (DWM) is performing a large scale conveyance condition assessment and rehabilitation program under a United States Environment Protection Agency (EPA) Consent Decree. DWM has collected and coordinated vast quantities of condition assessment data from various contractors to meet tight program schedules. DWM s assessment work has involved from 15 to 20 assessment crews working simultaneously to perform combinations of six inspection technologies over approximately 1,300 km (~800 miles) of sewers and 27,000 manholes. Managing the data, prioritizing the asset conditions, and developing a rehabilitation plan was an enormous task, especially when overseeing and integrating hydraulic modeling capacity improvements with the various condition inspection techniques and contractors. Using four key components (succinct data collection protocols, mandated quality control procedures, integrated data flow and robust decision logic) DWM automatically generated prioritized lists of assets needing either further inspection or corrective actions. The lists were confirmed by rehab review teams prior to assigning to design-build contractors. DWM realized the value of automating as many of the condition assessment business process steps as possible in order to efficiently process large quantities of condition assessment data and prioritize corrective actions. To date, DWM has collected over 10 Terabytes of media and 300 GB of inspection data. Processing this information by hand is not an option. DWM s automated tools have made processing the data with a small staff possible. KEYWORDS Data integration, condition assessment, Consent Decree, SCREAM, CCTV, quality control, corrective action INTRODUCTION DWM is performing a large scale conveyance condition assessment and rehabilitation program under an EPA Consent Decree. DWM has collected and coordinated vast quantities of condition assessment data from various contractors to meet tight program schedules. Managing the data, prioritizing the asset conditions, and developing a rehabilitation plan is an enormous task, especially when overseeing and integrating hydraulic modeling capacity improvements with the various inspection techniques and contractors. 817

2 This paper describes the data collection processes, decision logic, and coordinated software systems DWM uses to manage a variety of inspections and prioritize rehabilitation. DWM s approach is presented so that other utilities may benefit from their implementation experiences. DWM S INFRASTRUCTURE AND ORGANIZATION DWM owns and operates approximately 4,300 km (2,600 miles) of sewers ranging from 15 cm to 183 cm (6 to 72-inch) diameter; 65 lift stations; and two treatment plants serving approximately 193,000 customers. The assessment project is subdivided into two components 48 priority areas known as the Priority Area Sewer Assessment and Rehabilitation Program (PASARP) and the remaining portion of DWM s system are known as the Ongoing Sewer Assessment and Rehabilitation Program (OSARP) areas. PASARP covers approximately 1300 km (30%) and 27,000 of the DeKalb County sanitary sewer system and the OSARP area covers the remainder of the County maintained assets. Figure 1 shows the collection and treatment system service areas and the 48 priority areas. Figure 1: DWM s Wastewater Service Area. 818

3 Figure 2 displays the PASARP priority areas which were the primary focus areas of the EPA Consent Decree. 48 Areas ~1300 Km Gravity Mains ~27,000 Manholes Figure 2: DWM s PASARP Priority Areas DWM S COORDINATED DATA COLLECTION APPROACH DWM s assessment work has involved from 15 to 20 assessment crews working simultaneously performing combinations of six inspection technologies over approximately 1,300 km (~800 miles) of sewers and 27,000 manholes. To manage the complexities of this program, DWM developed a thorough data collection, integration and analysis approach. DWM decided to use a tiered approach to inspect the pipes in the PASARP areas. Instead of performing resource-intensive CCTV inspections on all the pipes, all the pipes were pre-screened using the less expensive Tier 1inspection techniques. Tier 1 pipes with high condition scores (poor condition) were assigned to Tier 2, receiving subsequent CCTV inspections. The inspection techniques included: Acoustic inspections for all Tier 1 pipes. Smoke inspections for all Tier 1 pipes. 819

4 CCTV inspections for pipes in Tier 2. WEF Collection Systems Conference 2017 TISCIT inspections for all large diameter pipes. The methodology for moving from Tier 1 to Tier 2 is detailed later in this paper, but generally follows the logic shown in Figure 3. Figure 3: Tiered inspection logic DWM knew that having a tiered approach to field inspection and subsequent data collection would help prioritize their assets with regards to condition and need for corrective action. DMW also needed to provide inspections that were properly conducted and that accurately documented the representative conditions of the pipes and manholes. Additionally, the program needed to carefully manage and standardize the data for easy and repeatable analysis. For instance, the SCREAM application enabled each asset s inspection data from the different field methodologies to be combined to provide a single condition score. The data handling process, from start to finish, required standardized data formatting protocols. Otherwise, the data would not have been useful to the semi-automated analysis processes set up by DWM. Finally, DWM established data protocols and decision algorithms to screen the inspections and target assets in need of corrective action review. Four key components were initially developed which has made DWM s program successful: 1. Succinct data collection protocols 2. Mandated inspection quality control procedures 3. Integrated data flow processes for easy analysis 4. Robust decision logic matrix Data collection protocols DWM recognized early in their program the importance of creating succinct data collection protocols for each of the inspection techniques: smoke testing, acoustic testing, CCTV inspections, TISCIT, and manhole inspections. The protocols went beyond requiring the National Association of Sewer Service Companies (NASSCO) PACP and MACP (Pipeline Assessment Certification Program and Manhole Assessment Certification Program, respectively) defect coding formats and included listing additional information and photos/videos to collect as well as procedures for transferring digital records and performing quality control. These protocols were 820

5 necessary to confirm that the field contractors would provideg complete inspection data for DWM analysis. The following are example protocols that were used: Smoke testing protocols detailed how contractors used the ESRI collector application to collect smoke information. The collector forms included mandatory fields such as gravity main ID, diameter and smoke source. Acoustic testing protocols detailed the acoustic process for pipes with diameters below 38cm (15 inch) and required gravity main identification and length. CCTV inspection protocols detailed the key data requirements, for instance, gravity main identification, diameter, invert elevation, etc. The protocols also described how contractors would collect and manage photos and videos. For example, snapshots of all significant defects were required to be taken and named using a smart naming convention. The protocols also detailed how the data was to be transferred to DWM. For example, the contractors delivered hard drives with both the databases and media files weekly. Quality Control Procedures WEF Collection Systems Conference 2017 Quality control (QC) was an essential component of the data collection process. To encourage the field contractors to meet established data standards the contractor submittals would only be approved when the quality control was properly and successfully performed using tools developed both for the contractor and DWM s program management team. A quality control tool was developed for each inspection technique. With the help of an easy user interface, contractors imported their electronic submittals into the Microsoft-Access based tool. Figure 4 provides a screen capture of the tool s interface page. Contractors ran QC queries to check for a variety of potential problems, from unusual manhole depths to missing photos or data. The quality control checks helped contractors and DWM s program managers easily target problem inspections and make corrections. Additional quality control was performed by DWM through review of generally 10 to 15 percent of the randomly selected inspections. 821

6 Figure 4: Example quality control interface To confirm that the contractor performed the QC checks and resolved any important errors, DWM required the QC reports with any data submittal. Each of the contractors were given classroom training with hands-on case studies of how to handle the inspection data. DWM s emphasis on quality control has increased the quality of the field inspection data, as evidenced through less time spent on data issues during monthly progress meetings and fewer formal data submittal rejections. Integrated data analysis methodology The automated, integrated field-to-recommendation applications helped leverage the value of the inspection data more quickly, minimized data errors, prioritized rehabilitation projects, and save costs by more appropriately targeting contractor activities and the assets needing rehab. The decision logic was automated through the use of several integrated systems. Figure 5 displays how data flows from the field through the quality control process to analysis using various software. First, CH2M s SQL Server-based System Condition Risk-Enhanced Assessment Model (SCREAM) processes all of the QC d inspections through an automated 822

7 upload procedure in the QC tool. More detailed discussions of SCREAM have been previously published (for example, Martin, L; Rowe, R; 2008). SCREAM produces condition scores for each inspection type as well as a combined condition scores, representing all of the inspection techniques used on each asset. For instance, smoke testing and CCTV scores were combined if performed on the same asset. The SCREAM scores were easily shared with DWM s InfoMaster software (Innovyze). InfoMaster s logic tables were customized to approximate SCREAM Next Step s logic, evaluating each asset s condition scores and risk consequence of failure (COF) score as well as inspection details, hydraulic capacity needs, and asset attributes to determine the next logical step for the asset. Figure 5: Data Flow Sequence through Software Applications 823

8 InfoMaster created a prioritized list of assets needing either CCTV inspection, emergency action, cleaning or priority rehab/repair work. DWM then used a custom workorder management system, FALCON, produced by CH2M as an interim tool until DWM s Cityworks computer maintenance management system could be installed. FALCON was used to track priority assets, and to help project managers create work orders to track contractor s work, invoices, performance metrics, and payments. Decision Logic Flow Diagrams DWM used two decision flow diagrams to make decisions about the tiered inspection approach and about which type of rehab was needed. The tiered flow diagram, shown in Figure 6, was used as assets were inspected. The logic determined which inspection technique to use. As the assets were inspected, condition scores were calculated for each of the inspection technologies. Additionally, combined scores were calculated to represent a blend of all the inspection scores. The tiered logic included pre-defined limits to the scores. First tier inspections were quick inspections, using smoke and acoustic testing to target assets in need of more thorough inspection techniques. Second tier inspections were more intensive, using CCTV and manhole inspections. Assets that met certain criteria, were sent to DWM s rehab logic, Figure 7. Pipes entering the rehab logic potentially needing repair, rehabilitation or replacement. Included with the scoring criteria and decision logic were other factors like emergency needs that might be observed during condition inspection work and capacity upsizing requirements that were separately identified by DWM s hydraulic model runs. The logic was created in InfoMaster and was designed to be similar to CH2M s SCREAM logic. The recommended corrective actions, coming out of the rehab logic were provided to the rehab teams as they begin their review. 824

9 Figure 6: DWM s Tiered Decision Logic 825

10 Figure 7: DWM s Rehab logic 826

11 STATUS OF WORK DWM is approximately halfway through the assessment period which ends in December Approximately 55% of the priority system has been inspected. Rehab packaging and assignments to the design-build contractor have just initiated. The data submittal process has been progressively shortened and the contractor s submittals are cleaner with fewer QC problems as the contractors were encouraged to focus more on the importance of data quality. To date, DWM has collected 10 Terabytes of media and 300 GB of inspection data. Processing this information by hand is not an option. DWM s automated tools have made processing the data with a small staff possible. Results of Tier 1 (Acoustic & Smoke Inspections) Assessment Leading to Tier 2 (CCTV): To date DWM has performed a total of 17,832 Tier 1 assessments on gravity mains within the PASARP Areas. As Figure 7 illustrates, from the Tier 1 assessments there were 2,626 (15%) CCTV Inspection work orders that were issued and/or performed on the gravity mains. The leading factor for mains needing CCTV is the Acoustic score given by SCREAM to each main based on the inspection. SCREAM s scoring range is from 1 to 100 where 100 is the worst condition. The breakdown on the factors leading up to CCTV inspection were 1991 (76%) based on an Acoustic score of 60 or more, 32 (1%) were based on a Smoke score of 75 or more and 603 (23%) were based on Combined Score (both Acoustic and Smoke) of 75 or more. These thresholds were agreed upon in early planning meetings. Figure 7: Percent Tier 1 Leading to Tier 2 Inspections 827

12 Result of Tier 2 (CCTV) Assessment Leading to Rehabilitation: To date DWM has performed approximately 14,600 Tier 2 assessments on gravity mains within the both PASARP and OSARP Areas. The results of these assessments were classified into three categories as illustrated in Figure 8; Monitor (Re-CCTV between 2 years to 10 years), Assess (In-Complete Assessments based on Obstacle(s) in Pipe or Re-CCTV less the 2 years), or Rehab (Replace, Lining, or Point Repair). A breakdown of the Tier 2 inspection scoring results have been 9,523 (65%) as Monitor; 795 (5%) as Assess; and 4,298 (30%) as Rehab. The high trending percent needing Rehab might be due to the fact that DWM s work has been pre-screened and directed towards the parts of the sewer system which were prioritized to be of greater concern. Figure 8: Breakdown of CCTV Scoring Results. 828

13 BENEFITS AND SIGNIFACANCE OF ROBUST DATA PLANNING AND IMPLEMENTATION Using the steps and processes described above, DWM automatically generated prioritized lists of assets needing either further inspection or corrective actions, which were confirmed by Rehab Review Teams prior to assigning to the design-build contractor. DWM realized the value of automating as many of the condition assessment business process steps as possible in order to efficiently process large quantities of condition assessment data and prioritize corrective actions. Large or small, other utilities can also integrate and automate their own software applications and platforms within their organization. What is common to DWM and other utilities, however, is the planning and design of how the condition assessment field data is applied and manipulated throughout the organization to more efficiently perform work. Processing the vast quantities of data and optimizing the rehab packaging according to geospatial proximity within the consent decree timeline would not have been possible without DWMs automated tools. REFERENCES WEF Collection Systems Conference 2017 Martin, L.; Rowe, R. (2008) Sanitation District No. 1 of Northern Kentucky Selects gbams and CH2M HILL to Integrate Continuous Sewer Assessment Program Data; gbams Annual Users Conference, Atlanta, Georgia; September. 829