Where AI Can (& Can't) Assist Public Works
This latest episode of APWA's Public Works Radio showcases how the City of Sugar Land, TX, built a dual-horizon predictive management system to advance, streamline, and modernize their water utility infrastructure.
The framework works to predict long-term pipe wear and flags pipes at risk using Artificial Intelligence (AI) and machine learning. The model correctly flagged more than 82% of the pipes that failed in Sugar Land. By 2025, water main breaks had dropped by 25%.
But sometimes AI falls short, and while everyone is excited to introduce tech to meet the demands of growing costs, budget concerns, and rising community expectations, speakers from Sugar Land agree that introducing a shiny new piece of sophisticated tech might not always be needed … or smart.
While the promise of what this technology can do is incredible, they remind us not to forget we still have a long way to go, and nothing right now can replace human judgement.
Featured Guests:
- Alence Poudel, PE, Senior Engineering Manager, City of Sugar Land, TX
- Trevor Surface, Utility Field Operations Manager, City of Sugar Land, TX
Each episode of Public Works Radio dives into a wide range of topics designed to educate and inspire, making public works more visible to everyone—from the general public and elected officials to industry peers and the media.
About APWA
The American Public Works Association (APWA) is an international educational and professional association of public agencies, private sector companies, and individuals dedicated to providing high quality public works goods and services.
With a worldwide membership of more than 32,000, APWA includes not only personnel from local, county, state, and federal agencies, but also private sector personnel who supply products and services to those professionals.
