Session 16B: Collection & Conveyance - Intelligent Collection Management - Livestream
3:00pm - 3:45pm
Artificial Intelligence, Real Solutions. Identifying Sewer Defects with AI
Burgess & Niple; ,
Traditional sewer inspection methods such as internal CCTV can be time-consuming and can overlook defects due to inaccurate identification and subjectivity in how people code. When assessing the future degradation of a sewer line or determining which asset to prioritize for rehabilitation, the difference and accuracy of coding is critical.
The goal of integrating artificial intelligence (AI) with sewer inspection is to supplement workers in the field, not to replace them. AI takes on the more common defects allowing field workers to focus on work at hand (access, MOT, cleaning) and codes that are more difficult and less frequent.
As with any project, the more accurate the data that goes in, the better quality of data that comes out. It is necessary to capture clear and unobstructed video, whether a field technician or an AI-based platform performs the assessment. When provided clear video, B&N’s AI has an accuracy rating of approximately 90%.
Our AI captures and recreates the workflow in coding and performing the quality assurance/quality control (QA/QC) of sewer inspections. With this technology, we have substantially reduced the time required to review sewer inspection data, increased the number and accuracy of defects identified and coded, and supplemented 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. Utility providers can quickly move through existing video to provide a database that shows the condition of their storm and sanitary infrastructure, allowing them to make data-driven decisions that are transparent and repeatable.
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.
3:45pm - 4:30pm
Real Time – Decision Support Systems For Intelligent Watershed Management
Xylem Inc., United States of America;
Technological advances have enabled Real-Time Decision Support Systems (RT-DSS) to dynamically optimize collection system operations using a stream of data from sensors placed in the network, Supervisory Control and Data Acquisition (SCADA) systems, and real-time weather ensemble forecasts.
Giant leaps forward in computing power, combined with advances and cost reductions in sensor and telemetry technologies, have made it possible to go far beyond the status quo and break into a new echelon of opportunities. We can now run high-resolution models in real-time, with real-world precipitation data, while correcting critical downstream model nodes with observed sensor data. The outcome is perpetually calibrated digital copies of the urban watershed designed for operators providing far more effective real-time operational decision making and control.
The RT-DSS provides operational intelligence, including:
The RT-DSS output is actionable information provided to the operation staff, engineering, and leadership using web-based dashboards.
Attendees of this presentation will benefit by better understanding what a Real-Time Decision Support System (RT-DSS) is and how they can help utilities better manage their collection systems. This presentation will discuss the development and implementation of several RT-DSS for utilities here in the Pacific Northwest and across the Country.