Department of Architecture

Daylighting standards provide an assessment method that can be used to evaluate the quality of window views. As part of this evaluation process, designers must achieve five environmental information criteria (location, time, weather, nature, and people) to obtain an excellent view. To the best of our knowledge, these criteria have not yet been verified and their scientific validity remains conjectural. In a two-stage experiment, a total of 451 persons evaluated six window view images. Using machine learning models, we found that the five criteria could provide accurate predictions for window view preferences. When one view was largely preferred over the other, the accuracy of decision tree models ranged from 83% to 90%. For smaller differences in preference, the accuracy was 67%. As ratings given to the five criteria increased, so did evaluations for psychological restoration and positive affect. Although causation was not established, the role of most environmental information criteria was important for predicting window view preferences, with nature generally outweighed the others. We recommend the use of the environmental information criteria in practice, but suggest some alterations to these standards to emphasize the importance of nature within window view design. Instead of only supporting high-quality views, nature should be promoted across all thresholds dictating view quality.

Cohort Comfort Models (CCM) are introduced as a technique for creating a personalized thermal prediction for a new building occupant without the need to collect large amounts of individual comfort-related data. This approach leverages historical data collected from a sample population, who have some underlying preference similarity to the new occupant. The method uses background information such as physical and demographic characteristics and one-time onboarding surveys (satisfaction with life scale, highly sensitive person scale, personality traits) from the new occupant, as well as physiological and environmental sensor measurements paired with a few thermal preference responses. The framework was implemented using two personal comfort datasets containing longitudinal data from 55 people. The datasets comprise more than 6000 unique right-here-right-now thermal comfort surveys. The results show that a CCM that uses only the one-time onboarding survey information of an individual occupant has generally as good or better performance as compared to conventional general-purpose models, but uses no historical longitudinal data as compared to personalized models. If up to ten historical personal preference data points are used, CCM increased the thermal preference prediction by 8% on average and up to 36% for half of the occupants in the first of the tested datasets. In the second dataset, one-third of the occupants increased their thermal preference prediction by 5% on average and up to 46%. CCM can be an important step toward the development of personalized thermal comfort models without the need to collect a large number of datapoints per person.

Mesh segmentation has become an important and well-researched topic in computational geometry in recent years (Agathos et al 2008) . As a result, a number of new approaches have been developed that have led to innovations in a diverse set of problems in computer graphics (CG) (Shamir 2008). Specifically, a range of effective methods for the division of a mesh have recently been proposed, including by K-means (Shlafman et al. 2002), graph cuts (Golovinskiy and Funkhouser 2008; Katz and Tal 2003 ), hierarchical clustering (Garland et al 2001; Gelfand and Guibas 2004 ; Golovinskiy and Funkhouser 2008 ), primitive fitting (Athene et al 2006), random walks(Lai et al), core extraction(Katz et al), tubular multi-scale analysis(Mortara et al. 2004), spectral clustering(Liu and Zhang 2004), and critical point analysis(Lin et al 2007), all of which depend upon a weighted graph representation, typically the dual of the given mesh (Shamir 2008). While these approaches have been proven effective within the narrowly-defined domains of application for which they have been developed (Chen 2009), they have not been brought to bear on wider classes of problems in fields outside of CG, specifically on problems relevant to generative architectural design (GAD). Given the widespread use of meshes and the utility of segmentation in GAD, by surveying the relevant and recently matured approaches to mesh segmentation in CG that share a common representation of the mesh dual, this paper identifies and takes steps to address a heretofore unrealized transfer of technology that would resolve a missed opportunity for both subject areas. Meshes are often employed by architectural designers for purposes that are distinct from and present a unique set of requirements in relation to similar applications that have enjoyed more focused study in computer science. This paper presents a survey of similar applications, including thin-sheet fabrication(Mitani and Suzuki 2004), rendering optimization(Garland et al 2001), 3d mesh compression(Taubin et al 1998), morphing (Shapira et al 2008) and mesh simplification(Kalvin and Taylor 1996), and distinguish the requirements of these applications from those presented by GAD, including non-refinement in advance of fabrication (such that the mesh geometry remain unaltered), the constraining of mesh geometry to planar-quad faces, and the ability to address a diversity of mesh features (such as creased edges or patterns) that may or may not be preserved. Following this survey of existing approaches and unmet needs, the authors assert that if a generalized framework for working with graph representations of meshes is developed, allowing for the interactive adjustment of edge weights, then the recent developments in mesh segmentation may be better brought to bear on GAD problems. This paper presents recent work toward the development of just such a framework, implemented as a plug-in for the visual programming environment Grasshopper.

We evaluated diurnal trends of size-resolved indoor and outdoor fluorescent biological airborne particles (FBAPs) and their contributions to particulate matter (PM) within 0.5-20 μm. After a ten-week continuous sampling via two identical wideband integrated bioaerosol sensors, we found that both indoor and outdoor diurnal trends of PM were driven by its bioaerosol component. Outdoors, the median [interquartile range] FBAP mass concentration peaked at 8.2 [5.8-9.9] μg/m³ around sunrise and showed a downtrend from 6:00 to 18:00 during the daytime and an uptrend during the night. The nighttime FBAP level was 1.8 [1.4-2.2] times higher than that during the daytime, and FBAPs accounted for 45 % and 56 % of PM during daytime and nighttime, respectively. Indoors, the rise in concentrations of FBAPs smaller than 1 μm coincided with the starting operation of the heating, ventilation, and air conditioning (HVAC) system at 6:00, and the concentration peaked at 8:00 and dropped to the daily average by noontime. This indicated that the starting operation of the HVAC system dislodged the overnight settled and accumulated fine bioaerosols into the indoor environment. For particles larger than 1 μm, the variation of mass concentration was driven by occupancy. Based on regression modeling, the contributions of indoor PM, non-FBAP, and FBAP sources to indoor mass concentrations were estimated to be 93 %, 67 %, and 97 % during the occupied period.

This paper introduces a database of 34 field-measured building occupant behavior datasets collected from 15 countries and 39 institutions across 10 climatic zones covering various building types in both commercial and residential sectors. This is a comprehensive global database about building occupant behavior. The database includes occupancy patterns (i.e., presence and people count) and occupant behaviors (i.e., interactions with devices, equipment, and technical systems in buildings). Brick schema models were developed to represent sensor and room metadata information. The database is publicly available, and a website was created for the public to access, query, and download specific datasets or the whole database interactively. The database can help to advance the knowledge and understanding of realistic occupancy patterns and human-building interactions with building systems (e.g., light switching, set-point changes on thermostats, fans on/off, etc.) and envelopes (e.g., window opening/closing). With these more realistic inputs of occupants' schedules and their interactions with buildings and systems, building designers, energy modelers, and consultants can improve the accuracy of building energy simulation and building load forecasting.

The global effects of climate change will increase the frequency and intensity of extreme events such as heatwaves and power outages, which have consequences for buildings and their cooling systems. Buildings and their cooling systems should be designed and operated to be resilient under such events to protect occupants from potentially dangerous indoor thermal conditions. This study performed a critical review on the state-of-the-art of cooling strategies, with special attention to their performance under heatwaves and power outages. We proposed a definition of resilient cooling and described four criteria for resilience—absorptive capacity, adaptive capacity, restorative capacity, and recovery speed —and used them to qualitatively evaluate the resilience of each strategy. The literature review and qualitative analyses show that to attain resilient cooling, the four resilience criteria should be considered in the design phase of a building or during the planning of retrofits. The building and relevant cooling system characteristics should be considered simultaneously to withstand extreme events. A combination of strategies with different resilience capacities, such as a passive envelope strategy coupled with a low-energy space-cooling solution, may be needed to obtain resilient cooling. Finally, a further direction for a quantitative assessment approach has been pointed out.

Assessment of integrated personal exposure (PE) to airborne particulate matter (PM) across diverse microenvironments (MEs) over 24 hours under different exposure scenarios is necessary to identify appropriate strategies to improve urban air quality and mitigate the health effects of PM. We carried out a collaborative study in a densely populated city-state (Singapore) to assess the integrated PE to fine particles (PM2.5), ultrafine particles (UFPs) and black carbon (BC) across diverse indoor and outdoor urban MEs, estimate related health risks and make suitable recommendations for healthy living in cities. Two volunteers with different lifestyles participated in the study by tracking their PE to particulate air pollution and the time-activity patterns over 24 hours using portable PM monitoring devices and recording their whereabouts using GPS coordinates. Home, transport and recreation (i.e., food court) MEs represented pollution hotspots of PM2.5 (21.0 μg/m3), BC (3.4 μg/m3) and UFP (33.0 × 103 #/cm3), respectively. Among the different modes of transport used by the participants (walking, cycling, e-scooter, mass rapid transport (MRT), bus, car and taxi), the air pollutants had elevated concentrations while commuting by public transport (bus and MRT) as well as during active modes of transport (walking and cycling). Air-conditioned cars and taxis, equipped with air filtration systems, represented the lowest PE. The health risk assessment revealed that there are potential carcinogenic risks associated with the long-term exposure to elevated levels of PM2.5-bound toxic trace elements. These risks can be mitigated with the introduction of low-carbon and active modes of transport in place of internal combustion engines and the use of indoor air pollution exposure mitigation devices.

Periodic disinfection of workspaces can reduce SARS-CoV-2 transmission. In many buildings periodic disinfection is performed manually; this has several disadvantages: it is expensive, limited in the number of times it can be done over a day, and poses an increased risk to the workers performing the task. To solve these problems, we developed an automated decontamination system that uses ultraviolet C (UVC) radiation for disinfection, coupled with occupancy detection for its safe operation. UVC irradiation is a well-established technology for the deactivation of a wide range of pathogens. Our proposed system can deactivate pathogens both on surfaces and in the air. The coupling with occupancy detection ensures that occupants are never directly exposed to UVC lights and their potential harmful effects. To help the wider community, we have shared our complete work as an open-source repository, to be used under GPL v3.

The human body emits a wide range of chemicals, including CO₂ and isoprene. To examine the impact of cognitive tasks on human emission rates of CO₂ and isoprene, we conducted an across-subject, counterbalanced study in a controlled chamber involving 16 adults. The chamber replicated an office environment. In groups of four, participants engaged in 30 min each of cognitive tasks (stressed activity) and watching nature documentaries (relaxed activity). Measured biomarkers indicated higher stress levels were achieved during the stressed activity. Per-person CO₂ emission rates were greater for stressed than relaxed activity (30.3 ± 2.1 vs 27.0 ± 1.7 g/h/p, p = 0.0044, mean ± standard deviation). Isoprene emission rates were also elevated under stressed versus relaxed activity (154 ± 25 μg/h/p vs 116 ± 20 μg/h/p, p = 0.041). The chamber temperature was held constant at 26.2 ± 0.49 °C; incidental variation in temperature did not explain the variance in emission rates. Isoprene emission rates increased linearly with salivary α-amylase levels (r² = 0.6, p = 0.02). These results imply the possibility of considering cognitive tasks when determining building ventilation rates. They also present the possibility of monitoring indicators of cognitive tasks of occupants through measurement of air quality.

Buildings influence diverse factors (e.g. health, wellbeing, productivity, and social connection). Occupants’ direct experiences with their indoor environments allow them to determine whether those spaces support or hinder the activities performed. However, most post-occupancy evaluations (POEs) focus solely on measuring people’s levels of comfort and environmental satisfaction. With increasing attention and interest in occupant health and wellness, there is a need to reassess whether occupant surveys are evaluating all they need to. An analysis is presented of data collected from a widely used online POE tool: The Center for the Built Environment’s (CBE) Occupant Survey (more than 90,000 respondents from approximately 900 buildings) in order to summarise its database and evaluate the survey’s structure and benchmarking metrics. A total of 68% of the respondents are satisfied with their workspace. Satisfaction is highest with spaces’ ease of interaction (75% satisfied), amount of light (74%), and cleanliness (71%). Dissatisfaction is highest with sound privacy (54% dissatisfied), temperature (39%), and noise level (34%). Correlation, principal component, and hierarchical clustering analyses identified seven distinct categories of measurement within the 16 satisfaction items. Results also revealed that a reduction in the scale may be possible. Based on these results, potential improvements and new directions are discussed for the future of POE tools. PRACTICE RELEVANCE Assessing the measurement properties in a widely used occupant satisfaction survey reveals what is still useful to include and what may be missing from occupant surveys. These insights are increasingly important as built-environment research evolves and an increasing emphasis is placed on the physical and mental wellbeing of occupants and their productivity. Typical occupant satisfaction rates are reported for indoor environmental quality parameters and benchmark values. These can be used as references by practitioners and other survey tools. Based on this analysis, recommendations are made for different clustering and themes of measurement categories, along with the scope of additional questions that can be posed to occupants.