Ianchenko, Alex; Simonen, Kathrina; Barnes, Clayton. (2020). Residential Building Lifespan and Community Turnover. Journal Of Architectural Engineering, 26(3).
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Abstract
Environmental impact studies within the built environment rely on predicting building lifespan to describe the period of occupation and operation. Most life cycle assessments (LCAs) are based on arbitrary lifespan values, omitting the uncertainties of assessing service life. This research models the lifespan of American residential housing stock as a probabilistic survival distribution based on available data from the American Housing Survey (AHS). The log-normal, gamma, and Weibull distributions were fit to demolition data from 1985 to 2009 and these three models were compared with one another using the Bayesian information criterion. Analysis revealed that the estimated average housing lifespan in the United States is 130 years given model assumptions, although a probabilistic approach to lifespan can yield higher accuracy on a case-by-case basis. Parameters for modeling housing lifespan as log-normal, gamma, and Weibull survival functions are published with the intent of further application in LCA. The application of probabilistic housing lifespan models to community-wide turnover and integration with existing simulations of natural disaster are proposed as potential ways to achieve community sustainability and resilience goals. (c) 2020 American Society of Civil Engineers.
Keywords
Energy-consumption; Service Life; Cycle; Demolition; Emissions; Design; Impact; Model; Housing Stock Lifetime; Residential Buildings; Housing Turnover; Life Cycle Assessment; Service Life Prediction
Ochsner, Jeffrey Karl; Rash, David A. (2012). The Emergence of Naramore, Bain, Brady & Johanson and the Search for Modern Architecture in Seattle, 1945-1950. Pacific Northwest Quarterly, 103(3), 123 – 141.
Zhu, Panyu; Gilbride, Michael; Yan, Da; Sun, Hongshan; Meek, Christopher. (2017). Lighting Energy Consumption in Ultra-Low Energy Buildings: Using a Simulation and Measurement Methodology to Model Occupant Behavior and Lighting Controls. Building Simulation, 10(6), 799 – 810.
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Abstract
As building owners, designers, and operators aim to achieve significant reductions in overall energy consumption, understanding and evaluating the probable impacts of occupant behavior becomes a critical component of a holistic energy conservation strategy. This becomes significantly more pronounced in ultra-efficient buildings, where system loads such as heating, cooling, lighting, and ventilation are reduced or eliminated through high-performance building design and where occupant behavior-driven impacts reflect a large portion of end-use energy. Further, variation in behavior patterns can substantially impact the persistence of any performance gains. This paper describes a methodology of building occupant behavior modeling using simulation methods developed by the Building Energy Research Center (BERC) at Tsinghua University using measured energy consumption data collected by the University of Washington Integrated Design Lab (UW IDL). The Bullitt Center, a six-story 4831 m(2) (52,000 ft(2)) net-positive-energy urban office building in Seattle, WA, USA, is one of the most energy-efficient buildings in the world (2013 WAN Sustainable Building of the Year Winner). Its measured energy consumption in 2015 was approximately 34.8 kWh/(m(2)a (TM) yr) (11 kBtu/(ft(2)a (TM) yr)). Occupant behavior exerts an out-sized influence on the energy performance of the building. Nearly 33% of the end-use energy consumption at the Bullitt Center consists of unregulated miscellaneous electrical loads (plug-loads), which are directly attributable to occupant behavior and equipment procurement choices. Approximately 16% of end-use energy is attributable to electric lighting which is also largely determined by occupant behavior. Key to the building's energy efficiency is employment of lighting controls and daylighting strategies to minimize the lighting load. This paper uses measured energy use in a 330 m(2) (3550 ft(2)) open office space in this building to inform occupant profiles that are then modified to create four scenarios to model the impact of behavior on lighting use. By using measured energy consumption and an energy model to simulate the energy performance of this space, this paper evaluates the potential energy savings based on different occupant behavior. This paper describes occupant behavior simulation methods and evaluates them using a robust dataset of 15 minute interval sub-metered energy consumption data. Lighting control strategies are compared via simulation results, in order to achieve the best match between occupant schedules, controls, and energy savings. Using these findings, we propose a simulation methodology that incorporates measured energy use data to generate occupant schedules and control schemes with the ultimate aim of using simulation results to evaluate energy saving measures that target occupant behavior.
Keywords
Control-systems; Patterns; Offices; Lighting Control; Ultra-low Energy Building; Occupant Behavior; Building Simulation; Energy Consumption
Liu, Yue; Colburn, Alex; Inanici, Mehlika. (2020). Deep Neural Network Approach for Annual Luminance Simulations. Journal Of Building Performance Simulation, 13(5), 532 – 554.
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Abstract
Annual luminance maps provide meaningful evaluations for occupants' visual comfort and perception. This paper presents a novel data-driven approach for predicting annual luminance maps from a limited number of point-in-time high-dynamic-range imagery by utilizing a deep neural network. A sensitivity analysis is performed to develop guidelines for determining the minimum and optimum data collection periods for generating accurate maps. The proposed model can faithfully predict high-quality annual panoramic luminance maps from one of the three options within 30 min training time: (i) point-in-time luminance imagery spanning 5% of the year, when evenly distributed during daylight hours, (ii) one-month hourly imagery generated during daylight hours around the equinoxes; or (iii) 9 days of hourly data collected around the spring equinox, summer and winter solstices (2.5% of the year) all suffice to predict the luminance maps for the rest of the year. The DNN predicted high-quality panoramas are validated against Radiance renderings.
Keywords
Scattering Distribution-functions; Daylight Performance; Glare; Model; Prediction; Daylighting Simulation; Luminance Maps; Machine Learning; Neural Networks; Hdr Imagery; Panoramic View
The Aga Khan Award for Architecture (AKAA) recently announced 20 shortlisted projects for the 2022 Award cycle. The projects will compete for a share of the US$ 1 million prize, one of the largest in architecture. The 20 shortlisted projects were selected by an independent Master Jury from a pool of 463 projects nominated for the 15th Award Cycle (2020-2022). The Aga Khan Award for Architecture was established by His Highness the Aga Khan in 1977 to identify and encourage…
Launching the Inspire Fund: An early step for CBE’s Office of Research “For a small college, CBE has a broad range of research paradigms, from history and arts, to social science and engineering.” — Carrie Sturts Dossick, Associate Dean of Research Upon taking on the role of Associate Dean of Research, Carrie Sturts Dossick, professor in the Department of Construction Management, undertook listening sessions to learn about the research needs of faculty, staff and students across the College of Built…
The EarthLab Innovations Grant Program was launched in 2019 to fund actionable environmental research. The 2022-23 EarthLab Innovation Grants program received 33 high-quality proposals for research at the intersection of climate change and social justice. One awarded project titled, “Centering Place and Community to Address Climate Change and Social Justice” was led by P.I. Daniel Abramson, Associate Professor of Urban Design & Planning and Adjunct Associate Professor of Architecture & Landscape Architecture, and Community Lead, Jamie Judkins, of the Shoalwater…
ARPA-E announced $5 million in funding to two universities—the University of Washington and University of California, Davis—working to develop life cycle assessment tools and frameworks associated with transforming buildings into net carbon storage structures. The funding is part of the Harnessing Emissions into Structures Taking Inputs from the Atmosphere (HESTIA) Exploratory Topic. Parametric Open Data for Life Cycle Assessment (POD | LCA) – $3,744,303 The University of Washington’s Carbon Leadership Forum will develop a rigorous and flexible parametric Life Cycle Assessment (LCA)…
Ann Marie Borys, Associate Professor in Architecture recently published a book titled American Unitarian Churches: Architecture of a Democratic Religion. The Unitarian religious tradition was a product of the same eighteenth-century democratic ideals that fueled the American Revolution and informed the founding of the United States. Its liberal humanistic principles influenced institutions such as Harvard University and philosophical movements like Transcendentalism. Yet, its role in the history of American architecture is little known and studied. In American Unitarian Churches, Ann Marie…
In 2021 the College of Built Environments launched the CBE Inspire Fund, designed to support CBE research activities for which a relatively small amount of support can be transformative. The second year of awards have just been announced, supporting five projects across 4 departments within the college as they address topics such as food sovereignty, anti-displacement, affordable housing, and health & wellbeing. This year’s awardees include: Defining the New Diaspora: Where Seattle’s Black Church Congregants Are Moving and Why Rachel…