[Re]Visioning the Ave: Students Devise Real-World Strategies for a Thriving, Accessible Neighborhood Hub was published on the College of Built Environments website, discussing the future of “The Ave.” The U-District Partnership (UDP)—a nonprofit organization worked with Teaching Affiliate David Blum and a diverse team of 16 urban planning graduate students through the process of assessing potential improvement strategies for the Ave in Winter 2022. Read more here.
Research Theme: Design & Building
Built environment scholarship at the scales of the interior space to the building
New Tool Created by CBE Researchers in partnership with Charles Pankow Foundation
CBE researchers worked with the Charles Pankow Foundation to develop a new Building Owner Assessment Tool (BOAT). Team members included CBE Dean Renée Cheng, FAIA; Associate Dean for Research Carrie Sturts Dossick, Ph.D, P.E.; and Laura Osburn, Ph.D. Other team members included Lingzi Wu, Ph.D. Daniel Dimitrov, and Xianxiang Sean Zhao. The tool was developed in partnership with the American Institute of Architects and the Integrated Project Delivery Alliance. The Building Owner Assessment Tool (BOAT): Helping You Understand Your Culture and Its…
Automated two-dimensional geometric model reconstruction from point cloud data for construction quality inspection and maintenance
Kim, Minju & Lee, Dongmin. (2023). Automated two-dimensional geometric model reconstruction from point cloud data for construction quality inspection and maintenance. Automation in Construction, 154. https://doi.org/10.1016/j.autcon.2023.105024.
Abstract
Despite the availability of 3D digital models, 2D floor plans remain extensively used for quality inspection and maintenance as they offer firsthand information. While laser scanners enable efficient capture and reconstruction of real-world scenes, challenges arise in accurately extracting building geometry from laser scanning data due to the loss of geometric features. This paper describes a method for accurately reconstructing 2D geometric drawings of built facilities using laser scanning data. These techniques involve transforming the dimension of 3D data into 2D and displaying the registered data as pixels to extract solid lines that represent wall structures. By employing dimensionality transformation and pixelation techniques, the method supports reliable quality inspection and maintenance processes, overcoming the challenges of extracting precise geometry from laser scanning data. This paper contributes to the automated extraction of geometric features from point clouds and inspires the future development of fully automated 2D CAD and 3D BIM in alignment with Scan-to-BIM.
Yang Shen
Yang Shen is a research engineer for the Carbon Leadership Forum at the University of Washington. Before joining CLF, he was a Postdoctoral Research Fellow in George Mason University focusing on multidisciplinary research such as Computer Vision/Deep Learning applications in the Built Environment. Yang got his PhD in Civil Engineering (Structural Engineering) from Texas A&M University. His Ph.D. research was tightly associated with building science, embodied carbon quantification/optimization, building operational energy simulation, parametric modeling, structural analysis, data analytics, and machine learning. He is passionate about using interdisciplinary studies to achieve climate change adaptation and mitigation.
Mel Chafart
Mel Chafart is a Researcher with the Carbon Leadership Forum where he is primarily focused on researching Whole Building Life Cycle Assessments. Prior to joining the CLF, Mel was a structural engineer at Buro Happold. There, he assisted in the design of steel and concrete structures in the US and abroad. He has worked on projects from concept design through construction administration. On the embodied carbon side, Mel has deep experience performing embodied carbon assessments and helped Buro Happold build out their portfolio of benchmarked projects. Outside of work, he enjoys watching soccer and baseball, woodworking, gardening, and tinkering with Raspberry Pis.
Milad Ashtiani
Milad Ashtiani is a Building and Materials Researcher with Carbon Leadership Forum. Milad is a civil engineer who received his PhD from the University of Washington in the summer of 2022. Milad is responsible for the execution of research and analysis, development of guidance documents and educational resources, and outreach across the design community to improve the quality, accuracy, and effectiveness of building performance tools, methods and data that address embodied carbon. As a building and materials researcher, Ashtiani works collaboratively with CLF’s internal research team as well as with architecture and engineering firms and research consortiums across North America with a focus on building performance, computation, embodied carbon assessments, and life cycle assessment (LCA).
Architecture 231: Making and Meaning featured in UW Today News
Architecture 231: Making and Meaning was featured in a UW Today news story. The benches from this class can be seen around Gould and Architecture Hall. See the full story here.
Urban Infrastructure Lab Report on High-Speed Rail
The Urban Infrastructure Lab researchers have released a report on a Cascadia region high-speed rail project. College of Built Environments faculty Jan Whittington and Qing Shen were authors on the report, along with 3 Interdisciplinary Ph.D. in Urban Design and Planning students (Siman Ning, Haoyu Yue, and Chin-Wei Chen), and a Master of Urban Planning candidate (Richard McMichael). This report examines the successes and lessons learned from existing high-speed rail projects in Europe and Asia, including 50 hours of interviews…
Gould Gallery is featured in UW Magazine
Gould Gallery was featured in the June issue of the University of Washington Magazine. Read the full story here.
Higher Depression Risks in Medium- Than in High-Density Urban Form Across Denmark
Chen, T.-H. K., Horsdal, H. T., Samuelsson, K., Closter, A. M., Davies, M., Barthel, S., Pedersen, C. B., Prishchepov, A. V., & Sabel, C. E. (2023). Higher depression risks in medium- than in high-density urban form across Denmark. Science Advances, 9(21), eadf3760–eadf3760. https://doi.org/10.1126/sciadv.adf3760
Abstract
Urban areas are associated with higher depression risks than rural areas. However, less is known about how different types of urban environments relate to depression risk. Here, we use satellite imagery and machine learning to quantify three-dimensional (3D) urban form (i.e., building density and height) over time. Combining satellite-derived urban form data and individual-level residential addresses, health, and socioeconomic registers, we conduct a case-control study (n = 75,650 cases and 756,500 controls) to examine the association between 3D urban form and depression in the Danish population. We find that living in dense inner-city areas did not carry the highest depression risks. Rather, after adjusting for socioeconomic factors, the highest risk was among sprawling suburbs, and the lowest was among multistory buildings with open space in the vicinity. The finding suggests that spatial land-use planning should prioritize securing access to open space in densely built areas to mitigate depression risks.