Events in January 2026
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Minnesota DOT Innovates: Modernizing Bridge Management with Digital Twins & Artificial Intelligence Minnesota DOT Innovates: Modernizing Bridge Management with Digital Twins & Artificial Intelligence
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January 22, 2026Jennifer Randall, P.E., State Bridge Inspection Engineer, MnDOT; Barritt Lovelace, P.E., Vice President of Emerging Technologies, Collins Engineers Inc.; Intro by: Ed Lutgen, P.E., State Bridge Engineer, MnDOT
Presentation begins: 06:00
Q&A begins: 41:30Documents:
January Intro & News Slides
2026 MnDOT Colins FIU Presentation SlidesDescription:
This presentation explores how Unmanned Aerial Systems, Artificial Intelligence (AI), and digital twins are transforming bridge inspection and management by improving safety, reducing costs, and enhancing quality. It demonstrates workflows for autonomous data collection, AI-driven defect detection, and digital twin creation to streamline communication and documentation. Real-world large-scale bridge projects will showcase how these technologies modernize asset management and address aging infrastructure and staffing challenges.
Presenters:
Jennifer Randall, P.E.
State Bridge Inspection Engineer
Minnesota Department of Transportation
Bridge OfficeJennifer Randall, has been an engineer with MnDOT for the past 20 years, the last 12 in fracture critical bridge inspection and 5 years in bridge design and bridge standards. Jennifer has a BSCE from Michigan Tech University and an MSCE from the University of Minnesota. She is a licensed professional engineer, NBIS Team Leader, SPRAT Level 1 rope access technician, FAA Certified Small Unmanned Aircraft System pilot, and lead investigator on MnDOT drone research for bridges.
Presentation Photos/Graphics:
Barritt Lovelace, P.E.
Vice President of Emerging Technologies
Collins Engineers, Inc.Barritt Lovelace is a licensed professional engineer and has over 29 years of structure design and inspection experience. He has designed over 50 structures including bridges and piers and has performed over 3,000 bridge and port inspections. Barritt currently serves as Vice President of UAS, Artificial Intelligence and Reality Modeling for Collins Engineers, Inc.
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Predicting Key Bridge Characteristics Using Machine Learning Predicting Key Bridge Characteristics Using Machine Learning
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January 30, 2026In this quarterly IBT/ABC-UTC Research Seminar, University of Washington professors Marc Eberhard (Civil and Environmental Engineering) and John Choe (Industrial and Systems Engineering) present work related to the Use of Machine Learning to Predict Key Bridge Properties. This work supports the improvement of Bridge Asset Management practices by identifying subsets of bridges most likely to have seismic vulnerabilities.
presentation begins: 5:28
Q & A begins: 38:48Presentation Documents:
IBT-ABC January 2026 Quarterly Research Seminar Presentation Slides
Intro & News SlidesDescription:
We explore the use of machine learning (ML) to predict key structural characteristics of bridges using data from the U.S. National Bridge Inventory (NBI). The publicly available NBI (2023) documents more than 100 characteristics of approximately 617k bridges in the United States, but few characteristics directly relate to structural or earthquake engineering. To provide ground-truth values, key structural characteristics not documented in the NBI were extracted from structural drawings of 796 bridges within Washington State. Using the corresponding NBI characteristics (features) and extracted properties (targets), we trained and evaluated standard ML algorithms and an automated ML (AutoML) approach using AutoGluon to develop predictive models for four target characteristics. We demonstrate that ML can feasibly generate a more detailed and comprehensive bridge inventory, supporting regional-scale planning, resource prioritization, and post-earthquake response. The predicted characteristics were accurate enough to improve the identification of shear-critical columns. The practical value of quick identification lies in enhancing resilience by enabling rapid decision-making, such as prioritizing pre-earthquake mitigation and post-earthquake inspections, particularly when reviewing plans for every bridge in a region is not feasible.Presenters:
Marc Eberhard, Ph.D.
Professor
Department of Civil & Environmental Engineering
University of Washington, Seattle
Email: eberhard@uw.eduMarc Eberhard received his Bachelor of Science in Civil Engineering from the UC Berkeley in 1984. After working for the Bridge Design Division of the California Department of Transportation, he attended the University of Illinois at Urbana-Champaign, where he received his Master’s Degree (1987) and PhD (1989). Marc Eberhard teaches course on structural analysis, reinforced and prestressed concrete structures, and earthquake engineering. His current research focuses on the rapid construction and performance of reinforced and prestressed concrete building and bridges, subjected to gravity loads, earthquakes and tsunamis.
John Choe, Ph.D.
Associate Professor
Department of Industrial & Systems Engineering
University of Washington, Seattle
Email: ychoe@uw.eduDr. John Y. Choe is the Director of the Disaster Data Science Lab and the Deputy Director of the Center for Disaster Resilient Communities. He received his Ph.D. in Industrial & Operations Engineering (Concentration: Quality Engineering & Applied Statistics) and M.A. in Statistics from the University of Michigan, Ann Arbor. He holds bachelor’s degrees in Physics and Management Science from KAIST in Korea.
Presentation Graphics:
Figure 1. Machine learning framework overview.
Figure 2. Text embedding framework overview.
