Gone are the days of spending countless hours (and dollars) combing through CCTV footage. Artificial Intelligence (AI) has emerged as a tool incorporating machine learning algorithms that capture and recreate a person's workflow and thought process 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. 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 Burgess & Niple 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. The digital twin allows utility providers a more effective way to maintain their system by creating a central hub to quickly 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 their sewers. AI cut the project timeline and cost in half while providing more accurate and detailed data.
Join Josh and Chris 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.