An AI-Enabled Condition Assessment of the Lake Winona Bike Path
Presenter(s)
Anton Kadlec
Abstract
This project was developed as part of the Winona State University Artificial Intelligence Innovation and Engagement Pilot Fund, which supports extracurricular AI projects that address campus and community challenges and strengthen career readiness through hands on experience. This project is presented as part of the Warrior.AI Symposium.
This project develops an AI-powered trail condition assessment for the Lake Winona Bike Path, a 5.3-mile paved loop encircling the East and West Lakes in Winona. The system correlates three data sources to produce cost efficient maintenance recommendations for city staff and improve Return on Investment: International Roughness Index (IRI) scores from Total Pave, Pavement Condition Index (PCI) scores collected through a field survey, and community experience feedback gathered via QR Code surveys posted at five trailhead locations. IRI measures pavement roughness in inches per mile using a mobile phone sensor and GPS. Higher IRI values indicate rougher surfaces with scores generally ranging from 0 to 500. PCI rates overall pavement distress on a scale from 0-100 where 0-10 is a failed surface, 10-25 is a very poor surface, 25-40 is a poor surface, 40-55 is a fair surface, 55-70 is a good surface, 70-85 is a very good surface, and 85-100 is an excellent surface. Together these two quantitative indicators provide a comprehensive picture of trail health at the segment level.
The project draws on my three years of professional infrastructure experience as an intern at GoodPointe Technology, a St. Paul based infrastructure asset management firm. This work included pavement condition data collection and management for municipalities including Apple Valley, Eden Prairie, and the St. Cloud Area Planning Organization, where I collected 150 miles of IRI data during Summer 2024.
The AI advisory system is built using Microsoft Copilot Studio. Survey responses are correlated with IRI and PCI measurements to produce a serviceability score for each trail segment. This allows the city staff to identify where their limited maintenance budgets will have the greatest impact.
College
College of Business
Department
Business Administration
Campus
Winona
First Advisor/Mentor
Pat Paulson
Location
Kryzsko Great River Ballroom, Winona, Minnesota; United States
Start Date
4-23-2026 1:00 PM
End Date
4-23-2026 2:00 PM
Presentation Type
Poster Session
Format of Presentation or Performance
In-Person
Session
2a=1pm-2pm
Poster Number
27
An AI-Enabled Condition Assessment of the Lake Winona Bike Path
Kryzsko Great River Ballroom, Winona, Minnesota; United States
This project was developed as part of the Winona State University Artificial Intelligence Innovation and Engagement Pilot Fund, which supports extracurricular AI projects that address campus and community challenges and strengthen career readiness through hands on experience. This project is presented as part of the Warrior.AI Symposium.
This project develops an AI-powered trail condition assessment for the Lake Winona Bike Path, a 5.3-mile paved loop encircling the East and West Lakes in Winona. The system correlates three data sources to produce cost efficient maintenance recommendations for city staff and improve Return on Investment: International Roughness Index (IRI) scores from Total Pave, Pavement Condition Index (PCI) scores collected through a field survey, and community experience feedback gathered via QR Code surveys posted at five trailhead locations. IRI measures pavement roughness in inches per mile using a mobile phone sensor and GPS. Higher IRI values indicate rougher surfaces with scores generally ranging from 0 to 500. PCI rates overall pavement distress on a scale from 0-100 where 0-10 is a failed surface, 10-25 is a very poor surface, 25-40 is a poor surface, 40-55 is a fair surface, 55-70 is a good surface, 70-85 is a very good surface, and 85-100 is an excellent surface. Together these two quantitative indicators provide a comprehensive picture of trail health at the segment level.
The project draws on my three years of professional infrastructure experience as an intern at GoodPointe Technology, a St. Paul based infrastructure asset management firm. This work included pavement condition data collection and management for municipalities including Apple Valley, Eden Prairie, and the St. Cloud Area Planning Organization, where I collected 150 miles of IRI data during Summer 2024.
The AI advisory system is built using Microsoft Copilot Studio. Survey responses are correlated with IRI and PCI measurements to produce a serviceability score for each trail segment. This allows the city staff to identify where their limited maintenance budgets will have the greatest impact.
