Speaking

Columbus Department of Public Utilities (DPU) implemented the business case evaluation (BCE) process and avoided $70 million in costs after seven years. The added social and environmental value of conducting BCEs brings the cost avoidance to over $100 million, and services levels, safety and risk exposure all improved. The BCE process received international recognition from the Water Services Association of Australia and was featured in a McGraw-Hill Report, “Water Infrastructure Asset Management: Adopting Best Practices to Enable Better Investments.”

The presented case study will show how BCEs are environmental factors with no market-driven value at PWX21, and agencies discussions about the difficulties of monetizing these factors. 

This presentation will share implementation lessons, including how to establish BCE policy, and how to avoid common implementation problems. 

Traditional sewer inspection can be time-consuming and overlook defects. Coding accuracy is critical when assessing a sewer line’s future degradation or determining which asset to prioritize for rehabilitation.

Artificial intelligence (AI) in sewer inspection supplements workers in the field rather than replacing them. AI takes on the more common defects allowing field workers to focus on work at hand and rarer, more difficult codes.

AI captures and recreates the workflow in coding and performing sewer inspection quality assurance/quality control. This technology substantially reduces the time required to review sewer inspection data, increases the number and accuracy of defects coded, and supplements the human element prone to bias.

This presentation will demonstrate the benefits of using AI as a low-cost way to evaluate systems and better maintain assets to prioritize rehabilitation and coordinate with other work such as roadway improvements.