DeKalb County: A Large Utility Gaining Efficiencies to Accurately Rehab Assets in a Timely Fashion

摘要

Assessing pipes and manholes of a large sewer network to provide rehab recommendations can be challenging and daunting. Performing this analysis accurately and quickly takes a different meaning for a utility that is under a consent decree, like DeKalb County, GA. Assessing all sewer assets requires collecting and managing high volumes of pipeline assessment certification program (PACP) and anhole assessment certification program (MACP) inspection data provided by contractors in multiple submittals. After submitting the inspection data, it is crucial to manage, review, and process them in a timely fashion. Unfortunately, many utilities perform this manually, significantly delaying data processing time, and reducing their capability to prevent sanitary sewer overflows (SSOs). During review, data can be duplicated or lost, making data management, and work order tracking difficult. Additionally, the condition assessment tools currently in the market lack the capability to process multiple databases, forcing the analyst to run each database individually. To be more efficient, utilities often perform multiple inspections of related rehab projects concurrently, increasing the number of submitted databases. This increases processing time and creates a data storage/management issue. The County recognized these issues during the collection phase and developed and implemented new tools to resolve the issues. This presentation will focus on demonstrating the value of automating quality check (QC) tools to provide quality data feedback to inspectors, drastically reducing iteration cycles during the data submittal process. The presentation will also demonstrate gained efficiencies by creating a database consolidation tool for analysis. These tools in combination with data management best practices helped County staff to better track and analyze PACP and MACP inspection databases. As a result, DeKalb County reduced any opportunity to lose or duplicate data, hindering analysis gaps, and providing unreliable results. Previously, a process that could take up to 880 h can now run under 40 h, freeing up staff to focus on other critical tasks that needed their attention.