Speakers
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CFM
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Tom Heinemann PE
Local Time
- Timezone: America/New_York
- Date: Sep 16 2026
- Time: 3:30 PM - 4:30 PM
Smarter Scans, Stronger Drains: Revitalizing Resiliency Through AI Driven Stormwater Infrastructure Assessment at UT Austin
As aging stormwater networks challenge long term infrastructure reliability, innovative assessment tools are essential to strengthening system resilience. The University of Texas at Austin partnered with Freese and Nichols (FNI) to modernize its storm drain system evaluation program by integrating Artificial Intelligence (AI) into the defect identification process for approximately 50,000 linear feet of CCTV inspected storm drain pipe. Using SewerAI’s automated defect coding platform, the project team rapidly analyzed more than 35 hours of inspection footage, identifying structural and operational defects with improved consistency and accuracy. AI processing reduced defect coding costs by 60% and accelerated delivery of a complete, GIS ready defect inventory. This validated dataset enabled development of a defect-based prioritization framework tailored to UT Austin’s drainage network. The prioritization framework incorporated defect severity, pipe characteristics, and improvement costs to produce actionable recommendations such as point repairs, open cut replacements, cleaning, and targeted monitoring. This framework yielded a balanced asset management plan to enhance system performance while supporting long term fiscal stewardship. This presentation demonstrates how AI enabled inspection workflows and data driven prioritization strengthen stormwater management. Attendees will learn practical strategies for integrating emerging technologies into asset management programs to improve decision making, extend system service life, and advance resilient infrastructure planning.
