Gone are the days of spending countless hours (and dollars) combing through CCTV footage. Artificial Intelligence (AI) has emerged as a tool that can result in cost and time savings by incorporating machine learning algorithms that capture and recreate the workflow and thought process a person goes through in coding and performing the QA/QC of sewer inspections.
With this technology, the time required in the field to obtain and review inspection data is substantially reduced, and the accuracy of defects identified and coded is increased while minimizing the human element that is prone to bias. Removing the burden of coding all defects from the contractor allows them to inspect more footage in a day and reduce the cost-per-foot for the owner.
The AI platform developed by B&N also incorporates GIS into the reporting by creating a digital twin of the collection system. This shows the condition of sewer assets and geospatially locates each defect and construction feature along a pipe. Utility providers can quickly evaluate the condition of their storm and sanitary infrastructure, allowing them to make data-driven decisions that are transparent and repeatable. A digital twin is also a tool that allows utility providers a more effective way to maintain their system by creating a central hub to visualize the condition of each pipe.
This presentation will focus on a case study of a municipality in Ohio. The municipality budgeted multiple years and hundreds of thousands of dollars to inspect its sewers. AI cut the project timeline and cost in half while providing more accurate and detailed data.
Join Josh Ford and Chris Dommert as they 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 capital improvement projects.