AI-powered digital twins for construction and infrastructure
By Mani Golparvar-Fard on 04/17/2025 from 2:00 p.m. to 3:00 p.m. in 1611 Titan Dr., Rantoul, IL 61866
Join Mani Golparvar-Fard of the University of Illinois Urbana-Champaign as he presents in person at the Spring 2025 Kent Seminar Series Thursday, April 17 from 2-3 p.m. (CT).
The Spring 2025 semester is set to feature 14 presentations, each addressing a topic related to autonomy in transportation. See the full lineup of speakers for Spring 2025 semester.
Pizza and soft drinks will be provided beginning at 1:30 p.m. in the ICT Classroom.
All presentations will be held on Zoom, but some speakers will present in person at ICT.
Join Zoom Meeting
https://go.illinois.edu/KentSeminar
Meeting ID: 898 9078 1073
Passcode: 116680
Abstract and Bio
Understanding the spatial and temporal state of a project is fundamental to effective construction and maintenance. As reality capture and building information modeling become more prevalent — and project timelines grow more aggressive — the need for AI-driven solutions that detect risks and support real-time decision-making is critical. This talk presents our work in computer vision and machine learning to build spatio-temporal models that capture the geometry and semantics of evolving physical assets. By integrating these models with BIM and schedule data, we create digital twins that detect deviations, forecast risks and recommend corrective actions — delivering measurable ROI across thousands of projects.
Golparvar-Fard is a professor of civil engineering, computer science, and technology entrepreneurship at the University of Illinois Urbana-Champaign and serves as chief science officer and co-founder of Reconstruct. His research advances computer vision and machine learning for construction and management of physical assets in the built environment. He holds degrees in civil engineering and computer science, has authored more than 200 publications, and holds dozens of patents. His work supports digital transformation across infrastructure and construction through AI-enabled modeling and automation.