Tabatabaei Manesh, M., Rajaian Hoonejani, M., Ghafari Gousheh, S., Abdolmaleki, A., Nikkhah Dehnavi, A., & Shahrashoob, A. (2025). AI-driven control algorithm using machine learning and genetic optimization for enhancing visual comfort in adaptive façades. Automation in Construction, 179, Article 106474. https://doi.org/10.1016/j.autcon.2025.106474.
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Abstract
Effective management of daylight and visual comfort in office spaces remains a challenge, as existing shading systems often lack adaptability to changing environmental conditions and occupant needs. This paper presents an AI-driven real-time shading control algorithm that optimizes visual comfort using machine learning-based surrogate models and evolutionary optimization. A non-conventional adaptive façade was simulated using Radiance and Ladybug Tools across nine U.S. climates. Four machine learning models were evaluated for predicting Task Illuminance (Et) and Vertical Eye Illuminance (Ev), with Extra Trees achieving the highest accuracy (R2
= 0.95). A Non-dominated Sorting Genetic Algorithm II (NSGA-II) balances glare reduction and daylight utilization by optimizing façade configurations in real time. In contrast to prior approaches constrained to fixed geometries and single-objective control, this paper introduces a generalizable multi-objective control framework. Results show that AI-driven optimization significantly improves adaptive façade performance, offering a scalable solution for intelligent daylight and comfort management.
Keywords
Smart façade control; Machine learning; Surrogate models; Visual comfort; Task illuminance; Vertical eye illuminance; Dynamic shading
Akerele, D. D., & Aguayo, F. (2025). A Comparative Evaluation of Polymer-Modified Rapid-Set Calcium Sulfoaluminate Concrete: Bridging the Gap Between Laboratory Shrinkage and the Field Strain Performance. Buildings (Basel), 15(15), 2759. https://doi.org/10.3390/buildings15152759.
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Abstract
Rapid pavement repair demands materials that combine accelerated strength gains, dimensional stability, long-term durability, and sustainability. However, finding materials or formulations that offer these balances remains a critical challenge. This study systematically evaluates two polymer-modified belitic calcium sulfoaluminate (CSA) concretes—CSAP (powdered polymer) and CSA-LLP (liquid polymer admixture)—against a traditional Type III Portland cement (OPC) control under both laboratory and realistic outdoor conditions. Laboratory specimens were tested for fresh properties, early-age and later-age compressive, flexural, and splitting tensile strengths, as well as drying shrinkage according to ASTM standards. Outdoor 5 × 4 × 12-inch slabs mimicking typical jointed plain concrete panels (JPCPs), instrumented with vibrating wire strain gauges and thermocouples, recorded the strain and temperature at 5 min intervals over 16 weeks, with 24 h wet-burlap curing to replicate field practices. Laboratory findings show that CSA mixes exceeded 3200 psi of compressive strength at 4 h, but cold outdoor casting (~48 °F) delayed the early-age strength development. The CSA-LLP exhibited the lowest drying shrinkage (0.036% at 16 weeks), and outdoor CSA slabs captured the initial ettringite-driven expansion, resulting in a net expansion (+200 µε) rather than contraction. Approximately 80% of the total strain evolved within the first 48 h, driven by autogenous and plastic effects. CSA mixes generated lower peak internal temperatures and reduced thermal strain amplitudes compared to the OPC, improving dimensional stability and mitigating restraint-induced cracking. These results underscore the necessity of field validation for shrinkage compensation mechanisms and highlight the critical roles of the polymer type and curing protocol in optimizing CSA-based repairs for durable, low-carbon pavement rehabilitation.
Keywords
calcium sulfoaluminate cement (CSA); polymer-modified confrete (PMC); rapid-set concrete; early-age shrinkage; temperature-induced strain; outdoor vs. laboratory performance; sustainable concrete; field performance; mechanical properties
The College of Built Environments Office of Research has published the FY25 annual report. The graphic summary below along with the full report are available on the CBE Intranet Office of Research website here.
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…
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
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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.
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.
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.
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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
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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
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)….
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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