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.
Ling, C., Chen, P., Chu, Y.-C., & Shen, Q. (2026). Not all teleworkers reduce travel: An intensity-dependent behavioral framework with explainable machine learning. Transport Policy, 187, Article 104267. https://doi.org/10.1016/j.tranpol.2026.104267
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Abstract
Previous studies have yielded mixed findings on the relationship between teleworking and travel, possibly due to overlooking teleworking intensity and its evolving impacts. This study examines how teleworking is associated with overall vehicle miles traveled (VMT) and vehicle travel time (VTT) across a spectrum of intensities, measured by the number of remote-work days per week. Using travel survey data in the Puget Sound Region, Washington, we estimate four explainable machine learning models (XGBoost with SHAP) that incorporate behavioral, socio-demographic, and built-environmental characteristics. Results find an intensity-dependent relationship among typical workers. Low-intensity teleworking (1 day/week) is associated with increased travel both pre- and post-pandemic, while moderate teleworking (2 days/week) shows near-neutral or marginally positive associations. In contrast, regular teleworking (3–5 days/week) is correlated with reduced travel. In 2023, teleworking intensity ranks among the top six contributors, accounting for 6.84% and 4.08% of model explainability for VMT and VTT, respectively, higher than in 2017. The reductions scale with intensity. Holding other factors constant, high-frequency teleworkers (3 days/week) in 2023 accrued approximately 10 fewer miles and 3 fewer minutes per five-day workweek than fully on-site workers, whereas full teleworkers (5 days/week) saved 19 fewer miles and 23 fewer minutes. These impacts are concentrated among high-frequency teleworkers with longer commute distances, while the effects are mixed among shorter-distance commuters. Teleworking can moderate impacts of other factors, such as commute distance, income, and population density. These findings help reconcile prior inconsistencies, showing that teleworking's role depends on intensity rather than simple adoption.
Research Interests: Machine learning, work from home, urban economics, transportation policy, urban data science, economic inequality
Professor Carrie Sturts Dossick (Construction Management) is on the project team that has created a digital twin model of the I-90 bridge in Seattle. Monitoring this bridge, which now includes the world’s first light rail crossing on a floating bridge, is an innovative and collaborative project evolving in real-time. Read the full feature story on the Civil & Environmental Engineering website here.
Assistant Professor of Architecture Narjes Abbasabadi has been awarded one of 36 seed grants from AI@UW. Narjes’ project entitled “SEED-AI 2026: DesignAI – An AI-Augmented Framework for Design Learning” was selected for funding alongside a range of other projects. The project summary is included below. More information is available here. Artificial intelligence (AI) offers powerful new ways to support design reasoning, exploration, and data-informed decision-making, yet these capabilities remain largely untapped in architectural education. This project addresses a critical dual-literacy…
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.
Abbasabadi, N., & Ashayeri, M. (2024). Machine Learning in Urban Building Energy Modeling. In Abbasabadi, N., & Ashayeri, M. (Eds.), Artificial Intelligence in Performance-Driven Design : Theories, Methods, and Tools: Theories, Methods, and Tools. Wiley-Blackwell.
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Urban building energy modeling (UBEM) plays a pivotal role in effective urban energy management and the holistic understanding of citywide energy performance. This book chapter delves into the integration of machine learning (ML) in UBEM, covering applications such as predictive energy consumption modeling and optimization, and providing insights into how ML techniques enhance modeling accuracy and efficiency. It explores current UBEM methods, highlighting their strengths and limitations, and discusses the opportunities presented by ML for advancing UBEM approaches. The chapter also introduces a hybrid UBEM approach that combines data-driven and physics-based simulations to enhance modeling accuracy and reduce uncertainties in capturing urban energy use. This fusion of ML and UBEM offers promising prospects for improving urban energy management practices.
Abbasabadi, N., & Ashayeri, M. (2024). Understanding Social Dynamics in Urban Building and Transportation Energy Behavior. In Abbasabadi, N., & Ashayeri, M. (Eds.), Artificial Intelligence in Performance-Driven Design : Theories, Methods, and Tools: Theories, Methods, and Tools. Wiley-Blackwell.
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This chapter explores the impact of human dynamics, social determinants of public health ( SDPH ), mobility, and occupancy on urban energy use behavior, a topic previously overlooked due to individual buildings or transportation models. A novel, data-driven urban energy model is developed using Artificial Neural Networks ( ANN ), augmented by Garson, Lek's profile and Partial dependence Plot ( PDP ) methods, to holistically evaluate urban energy behavior across Chicago communities, integrating both building and transportation energy use. Utilizing diverse public datasets from the city of Chicago, and validated through cross-validation, the model assesses human dynamics in development of an integrated urban energy modeling. The findings reveal a significant association between SDPH status, mobility, occupancy, and urban energy behavior with household income being a major contributor post accounting for urban spatial patterns and building physical attributes. The study suggests that meeting decarbonization targets in cities requires a broader evaluation encompassing various urban energy determinants. It advocates for emerging technologies and detailed analytical scrutiny, urging researchers and policymakers towards a comprehensive understanding of urban energy use behaviors.