Borjigin, O., Sturts Dossick, C., Treece, B., Thonstad, T., Bernard, T., & Motley, M. (2026). Developing digital twins for bridge infrastructure: Roadmap and challenges from a proof-of-concept R&D study. Journal of Information Technology in Construction (ITcon), 31, 917-938. https://doi.org/10.36680/j.itcon.2026.039.
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Abstract
The exploration of Digital Twins (DT) technology in the Architecture, Engineering, Construction, and Operation (AECO) industry has gained momentum in recent years, with numerous studies proposing frameworks for its development. However, few real-world infrastructure applications have been systematically documented to guide future practitioners and researchers. This study addresses this gap by providing an empirical example of DT design and development through a proof-of-concept research and development (R&D) project. The I-90 Homer M. Hadley Bridge (I-90 Bridge) DT project, led by the University of Washington (UW) DT research team, was selected as a case study to demonstrate the application of DT technology for bridge health monitoring. Using a vignette-style narrative approach, the study integrates data from semi-structured interviews with project team members and project documentation generated during the project to illustrate how the system was conceptualized, developed, and delivered. The findings reveal that interoperability emerged as a consistent theme throughout the project, including technical challenges (legacy systems, proprietary programming, lack of standardization) and organizational challenges (multi-stakeholder engagement, organizational silos). Recognizing these challenges can help practitioners and researchers better identify key problems to address, thereby supporting a successful adoption of DT technologies.
Keywords
Digital Twins; Bridge Infrastructure; Organizational Interoperability; Technical Interoperability; Research & Development; Case Study
BE PhD Candidate Ori Borjigin received the Best Research Poster award at the 2026 Construction Industry Institute (CII) Annual Research Conference in Denver, CO on July 22nd, 2026. The poster featured research on the I-90 Digital Twin project, which is a collaboration between the Mobility Innovation Center, and faculty from the Departments of Construction Management and Civil and Environmental Engineering. View the Poster here. Related news items: Digital twin – proof of technology evaluation on the I-90 Homer Hadley floating…
My research is deeply rooted in the intersection of advanced technology and the construction industry. I am particularly interested in the exploration and application of Building Information Modeling (BIM) and Virtual Design and Construction (VDC). Beyond these technological frontiers, sustainable engineering holds a special place in my academic pursuits. I am dedicated to merging innovative technology with sustainable methods, aiming for a construction industry that is both advanced and environmentally responsible.
The Population Health Initiative has awarded eight early-stage pilot grants in November 2025. The project, “Embodied Nature Engagement: Developing the Interaction Pattern Preference Inventory (IPPI) for Nature Prescriptions in Primary Care” includes Sebastian Tong (Department of Family Medicine), Peter Kahn (Department of Psychology & School of Environmental and Forest Sciences), Ashley Park (Department of Family Medicine), and Hongfei Li (College of Built Environments). Hongfei Li is a lecturer and interdisciplinary PhD student in the Landscape Architecture department. Congratulations to Hongfei…
My research interests include housing policy, affordable housing, smart cities, housing markets, real estate markets, appraisals, development, sustainability and investments. Other areas that are of interest to me include facilities management, urban and city planning, and real estate economics.
Liu, Y., & Abbasabadi, N. (2025). Enhancing urban building energy models with Vision Transformers: A Case study in material classification from Google street view. Energy and Buildings, 333, Article 115457. https://doi.org/10.1016/j.enbuild.2025.115457.
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Abstract
The growing urbanization and increased urban energy consumption highlight the need for energy use and greenhouse gas emissions reduction strategies. Urban Building Energy Modeling (UBEM) emerged as a valuable tool for managing and optimizing energy consumption at the neighborhood and city scales to support carbon reduction goals. However, the accuracy of the UBEM is often limited by the lack of large-scale building façade material dataset. This study introduces a new approach to enhance UBEM by integrating an automatic deep learning material classification pipeline. The pipeline leverages multiple views of Google Street View Images (SVIs) to extract building façade material information, utilizing two Swin Vision Transformer (ViT) models to capture both global and local features from the SVIs. The pipeline achieved a main material classification accuracy reached 97.08%, and the sub-category accuracy reached 91.56% in a multi-class classification task. As the first study to apply a deep learning model for material classification to enhance the UBEM framework, this work was tested on the University of Washington campus, which features diverse facade materials. The model demonstrated its effectiveness by achieving an overall accuracy increase of 11.4% in year-round total operational energy simulations. The scalability of this material classification pipeline enables a more accurate and cost-effective application of UBEM at broader urban scales.
My interest lies in urban-scale building energy modeling and carbon accounting for climate mitigation. Specifically, I am focused on how digital documentation of the built environment can automate and enhance the accuracy of current accounting methods. Moreover, I am intrigued by how these advancements enable the broader application of bottom-up accounting approaches, informing early-stage design and influencing energy policy decisions.
Professor Chris Lee and team are beginning a project entitled “Taxi and Transportation Network Company (TTNC) Electrification Policy Guidance,” funded by the Port of Seattle. This project aims to support the Port of Seattle—including Seattle-Tacoma International Airport and the Maritime Division—in developing strategies to reduce carbon emissions from passenger ground transportation. Drawing on outreach to taxi and transportation network company (TNC) drivers (e.g., Uber, Lyft), the project will identify key barriers and opportunities for electrifying commercial ground transportation serving key…
The 2025 Inspire Fund Awardees have been selected! See more information about their projects below. Project Title: “Enhancing Feasibility and Evaluation for the Housing Choice Voucher Homeownership Program in King County” Team: Vince Wang (Runstad Department of Real Estate), Zhongmin Evy Luo (PhD student, Built Environments), Kristin Pace (KCHA) Project Title: “Wildfire Smoke Readiness of Low-Income Households in Seattle” Amos Darko (Construction Management), Alvina Ekua Ntefua Saah (PhD Student, College of Built Environments) Project Title: “Equitable Public Electric Vehicle Charging…
Tabatabaei Manesh, M., Nikkhah Dehnavi, A., & Rajaian, M. (2024). Using Machine Learning To Predict And Visualize Acoustic Quality In Educational Buildings. The 2024 International ConCave Ph.D. Symposium: Divergence in Architectural Research. Georgia Tech, Atlanta, April 4-5.
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