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…
Department: College of Built Environments
Data-driven real-time visualization of urban heat islands using mean radiant temperature for urban design
Rashtian, Z., Manesh, M. T., Tahsildoost, M., & Zomorodian, Z. S. (2025). Data-driven real-time visualization of urban heat islands using mean radiant temperature for urban design. Energy and Buildings, 115470-. https://doi.org/10.1016/j.enbuild.2025.115470
Abstract
The Mean Radiant Temperature (Tmrt), is a critical indicator for understanding urban thermal comfort and microclimate conditions, particularly in urban areas experiencing higher temperatures compared to rural surroundings. Tmrt is heavily influenced by urban morphology, including building layout, street design, and green spaces which alter airflow, shading, and heat retention. Evaluating geometry alternatives during the early design stages in urban neighborhoods is challenging due to lengthy simulations and the need for extensive expertise in physical models. Recent studies have employed data-driven methods for quick design comparisons and new urban layout evaluations, successfully predicting Thermal indicators of Urban heat Island phenomenon but often limited by the diversity of urban configurations inputs used in training datasets. To address these limitations, this study proposes a novel framework that uses machine learning models to predict Tmrt as the primary indicator. A comprehensive training dataset of 200 cases was generated in Rhino7 using Grasshopper, Ladybug, and Dragonfly plugins. Sensitivity analysis was conducted to assess the impact of input uncertainties on model predictions, and the model’s performance was validated against unseen configurations. Among six machine learning algorithms tested, the CatBoost Regressor achieved the highest accuracy, predicting Tmrt with an R2 = 0.93, RMSE = 4.30 °C, and MAE = 2.34 °C. Validation using 20 additional cases showed an accuracy of R2 = 0.71, RMSE = 3.34 °C, and MAE = 2.27 °C in predicting Tmrt heat maps for new urban configurations. This framework successfully enables real-time Tmrt heat map analysis in simplified cubic neighborhoods within a 3D environment. Additionally, it enhances the temporal and spatial resolution of thermal patterns predictions, offering rapid and detailed insights into various urban design alternatives.
CBE Office of Research Publishes FY24 Annual Report
The CBE Office of Research has published the FY24 annual report. This report summarizes the impact and outcomes of CBE Research, and highlights other accomplishments by researchers within the college. Read the one-page summary of the FY24 report here. Read the full FY24 report here. Questions? Contact the CBE Office of Research at be-research@uw.edu.
Pacific Coast Architecture Database (PCAD)
PCAD archives a range of information on the buildings and architects of California, Oregon and Washington. Also included are professionals in other fields who have made an impact on the built environment, such as landscape architects, interior designers, engineers, urban planners, developers, and building contractors. Building records are tied to those of their creators (when known) and include historical and geographical information and images. Bibliographical information, such as magazine and book citations and web sites, has also been linked for creators and their partnerships and structures.
Acoustic design evaluation in educational buildings using artificial intelligence
Tabatabaei Manesh, M., Nikkhah Dehnavi, A., Tahsildoost, M., & Alambeigi, P. (2024). Acoustic design evaluation in educational buildings using artificial intelligence. Building and Environment, 261, 111695-. https://doi.org/10.1016/j.buildenv.2024.111695
Abstract
Speech intelligibility is a critical aspect of building science, particularly in educational buildings where poor sound quality may have a detrimental impact on students' learning and teachers’ health. However, considering the numerous building regulations proposing varying definitions and ranges of acoustic comfort, calculating the necessary acoustic indicators can be challenging for designers. Speech intelligibility is a crucial component of indoor acoustics and acoustic comfort and can be calculated using formulas, simulation software, and data-based web tools. While formulas are fast, they lack details; acoustic simulation software is highly accurate but time-consuming and expensive. Data-based web tools, including machine learning algorithms, offer both speed and accuracy and are widely accessible. In this study, we present a system utilizing machine learning techniques to predict acoustic indicators, numeric and heatmap, in an educational building. The Pachyderm plugin in the Grasshopper was utilized to conduct extensive simulations in a single educational space, focusing on acoustic indicators in six different frequencies and general modes. Using Catboost and the pix2pix algorithm, the prediction models provide numerical and image indices on the developed dataset. Also, SHAP values were employed to interpret the Catboost model, analyzing the significance of each feature. The results showed remarkable accuracy, (i.e., between 89 % and 99 %) in the numerical portion, and PSNR index ranging from 0.817 to 0.970, and an SSIM index ranging from 15.56 to 31.57 in the image section. By utilizing data-driven methods, the system provides an efficient and accurate approach to calculating acoustic indicators, helping to ensure optimal acoustic environment in educational buildings.
Keywords
Building acoustics; Catboost; Pix2pix; Educational building; Speech intelligibility
2024 Climate Solutions Symposium
The Inaugural CBE Climate Solutions Symposium took place on May 23, 2024. The event began with a reception and poster session, followed by an invited lecture “Every Project is a Climate Opportunity” with Don Davies, PE, SE and Joan Crooks. 36 research posters were submitted and accepted to the symposium. The posters covered a range of topics, from affordable housing in Indonesia (Bella Septianti, Architecture/Design Technology), to CLT and structural steel comparative lifecycle assessment (Mira Malden, Community, Environment, and Planning)….
Using Machine Learning To Predict And Visualize Acoustic Quality In Educational Buildings
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.
Mohammad Tabatabaei Manesh
Mohammad Tabatabaei Manesh is a computational designer and building science researcher with expertise in programming and building performance. He works on the application of machine learning and deep learning in building performance, developing web apps and tools for architects. Currently, Mohammad’s work focuses on the design, fabrication, and evaluation of acoustic metamaterials for the built environment.
Effects of Hydrothermal Modification on The Mechanical Properties of Red Alder (Alnus Rubra) Native to The Pacific Northwest
Muiruri, K., Ganguly, I., McPeters, K., & Alonzo, B. (2024). Effects of Hydrothermal Modification on The Mechanical Properties of Red Alder (Alnus Rubra) Native to The Pacific Northwest. The 2024 International ConCave Ph.D. Symposium: Divergence in Architectural Research. Georgia Tech, Atlanta, April 4-5.
Building equity into public park and recreation service investment: A review of public agency approaches
Beck, H., Berney, R., Kirk, B., & Yocom, K. P. (2024). Building equity into public park and recreation service investment: A review of public agency approaches. Landscape and Urban Planning. https://doi.org/10.1016/j.landurbplan.2024.105069
Abstract
In recent decades, academic and professional research has increased understanding of the importance of city and landscape planners engaging with social and environmental justice issues, including contemporary inequities inherent in the planning, distribution, use, and access of public green and open spaces. However, there is a gap between this research centering equity and the planning, development, and implementation rate demonstrated by public agencies. In this article, we examine examples of emerging practice in the public park and recreation sector to understand the strategies and approaches public agencies are taking to provide equitable park and recreation systems. Our research identifies and analyzes 17 examples of North American public park and open space management agencies using equity-based planning frameworks to prioritize park investment and resource distribution. Equity-focused resource analysis is distinct because while it assesses budget and project-based funding distributions, it further incorporates assessments of historical allocations to understand better areas of under-investment and the evolving needs of different communities. As economic inequities become more pronounced, local governments, and other public institutions providing services to populations, are important in helping communities navigate changes. Our findings support the ongoing advancement of equity-driven planning and implementation for public park and recreation agencies by providing practical information on existing approaches to redress the impact of unfair patterns of under-investment.