Public health and environmental well-being depend on the reliability of the infrastructure hidden beneath our communities. Sewer systems, for example, play a critical role in protecting water quality and preventing the spread of disease. If these systems fail, the consequences extend far beyond inconvenience. Untreated sewage can contaminate local waterways, threaten drinking water sources, and create dangerous conditions for our health and surroundings.
Across the country, the environmental and financial costs of sewer system failure continue to rise. Last summer, 50 beaches in Massachusetts closed due to sanitary sewer overflows (SSOs) following heavy rain, while frequent episodes of sewage flowing into streets and local waterways in Charleston, SC have forced residents to consider suing their water utility.
AI-powered systems offer a way to address inefficient sewer inspections and delayed maintenance decisions that lead to SSOs. Technology can help tackle some of the tedious aspects of inspection to maintain community health.
Sewer system evaluations are traditionally labor-intensive and prone to error. Trained inspectors must navigate dangerous work zone traffic, filthy sewers, inaccurate maps, and other difficult conditions to snake robotic closed-circuit television (CCTV) cameras through underground pipes, looking for defects and documenting them based on industry standards.
Municipalities struggle to allocate resources due to staffing levels, training and equipment requirements, and budgets. Less surveillance means more opportunities for issues to go unseen and services to suffer. Utilities can’t appropriately plan for maintenance expenditures without accurate and timely inspections of their sewers.
Even seasoned CCTV operators can make mistakes when detecting defects in sewer pipes. The range of operational challenges encountered in the field, such as navigating difficult environments or dealing with technical issues, further increases the likelihood of errors. To address this, office staff typically follow up an inspection with a review of the coded CCTV video to ensure no defects were missed or mislabeled—an essential step before recommending repairs.
However, the sheer volume of data and repetitive nature of reviewing footage can lead to fatigue and oversight. If defects are missed during this process, necessary maintenance may be delayed, allowing minor issues to escalate into significant problems such as sanitary sewer overflows (SSOs).
This is where AI steps in. Automating the review of CCTV videos brings subjective precision and speed to a traditionally time-consuming process. Machine learning models can be trained to instantly identify sewer defects like fractures, cracks, roots, and other issues, delivering rapid results—a critical requirement when managing large volumes of video. And AI models can work continuously, maintaining accuracy regardless of the number of videos analyzed.
By implementing AI-powered sewer defect detection, consultants and contractors can streamline their inspection processes and quickly provide their clients with a full understanding of the overall state of their sewers. This allows municipalities to make data-driven decisions about repair and maintenance priorities, develop budgets for future system repairs, and tackle the most pressing issues before they escalate into costly failures, like SSOs.
The BRYX Sewer Pipeline Defect Detection is a powerful AI model that analyzes CCTV inspection video and automatically detects common sewer defects, using NASSCO standards. Designed to optimize sewer assessments, this BRYX model helps municipalities make smarter maintenance decisions and keep their communities safe and healthy. Start a free trial today at gobryx.com.