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Equity in the built environment refers to the extent to which the built environment meets the needs of different groups through planning, design, construction, operation, management, and regulation. Though much studied in recent years, some needs and groups have received a greater research focus than others, and significant inequities continue to exist. Following PRISMA guidelines, we systematically reviewed the distributional and recognitional aspects of inequities experienced by vulnerable groups regarding their needs while using/occupying different types of built environments. We find that more studies focus on inequities regarding residential buildings, transportation facilities, and public open spaces, whereas comparatively few studies examine water and energy infrastructure, commercial buildings, educational buildings, and healthcare facilities. More studies focus on well-being, mobility, and access needs than shelter and safety needs. Inequities experienced by minorities, people with low socioeconomic status, people with health concerns, and vulnerable age groups receive more attention than the inequities experienced by people with gender/sexual-orientation vulnerability or displaced groups. The literature exhibits a relatively narrow focus on some subgroups, such as refugees, people experiencing homelessness, people with cognitive differences, people with visual or hearing impairments, children, and women. We argue that these findings demarcate high-impact future research directions to address vulnerable groups’ needs worldwide and suggest measures to alleviate inequities in the built environment.

Background: Video-based training has been widely adopted by private organizations and public authorities to educate occupants on various types of building emergencies. However, the effectiveness of video-based training for preparing occupants for building emergencies has not been rigorously studied nor has the impact of emergency type been investigated on training effectiveness. Objectives: This study examines whether video-based training is an effective method to prepare occupants for building emergencies and how the effectiveness differs in the context of different building emergencies. Methods: We simulated fire and active shooter emergencies in a virtual office building and conducted evacuation experiments to examine participants' emergency responses using both objective and subjective metrics. A total of 108 participants were recruited and responded to the fire or active shooter incident with or without video-based training. Results and Conclusions: The results revealed that participants with video-based training more often chose to follow other recommendations when responding to building emergencies instead of simply following others. Results from ANOVA showed that training increased participants' self-efficacy significantly, especially for those in the active shooter group. Moreover, participants in the active shooter simulation had a higher level of response efficacy than those in the fire emergency simulation. Our results also demonstrated the influence of emergency type on participants' final decisions and considerations of the recommendations. Implications: Our results suggested that video-based training is effective in improving participants' emergency preparedness and changing their behaviour patterns to a certain extent such as reducing following behaviour and encouraging safe evacuations. Additionally, statistically significant interactions between video-based training and emergency types suggested that training effectiveness should be considered in accordance with the emergency type.

Human-Building Interaction (HBI) is a convergent field that represents the growing complexities of the dynamic interplay between human experience and intelligence within built environments. This paper provides core definitions, research dimensions, and an overall vision for the future of HBI as developed through consensus among 25 interdisciplinary experts in a series of facilitated workshops. Three primary areas contribute to and require attention in HBI research: humans (human experiences, performance, and well-being), buildings (building design and operations), and technologies (sensing, inference, and awareness). Three critical interdisciplinary research domains intersect these areas: control systems and decision making, trust and collaboration, and modeling and simulation. Finally, at the core, it is vital for HBI research to center on and support equity, privacy, and sustainability. Compelling research questions are posed for each primary area, research domain, and core principle. State-of-the-art methods used in HBI studies are discussed, and examples of original research are offered to illustrate opportunities for the advancement of HBI research.

We introduce a systematic approach for analyzing the energy consumption of four control policies (i.e., zone level daily optimal control, zone level annual optimal control, building level daily optimal control, building level annual optimal control), which differed based on their temporal and spatial control scales. In order to integrate occupant thermal comfort requirements, we defined uniformly distributed random constraint functions on the setpoints. We used the DOE reference small office building in three U.S. climate zones for simulating the performances of control policies, using EnergyPlus. Among the four control policies, the building level annual control policy showed close to the highest energy efficiency (27.76% to 50.91% (average of 39.81%) savings depending on the climate) with comparatively small training data requirements. In addition, the building level daily optimal setpoint selection, subject to thermal comfort constraints, leads to 17.64 – 38.37% (average of 26.61%) energy savings depending on the climate. We also demonstrate that temporal scale of the policies have a statistically significant impact on the small office building’s energy consumption while spatial scale’s impact is insignificant.

Maintaining thermal comfort in built environments is important for occupant health, well-being, and productivity, and also for efficient HVAC system operations. Most of the existing personal thermal comfort learning methods require occupants to provide feedback via a survey to label the monitored environmental or physiological conditions in order to train the prediction models. However, the accuracy of these models usually drops after the training process as personal thermal comfort is dynamic and changes over time due to climatic variations and/or acclimation. In this paper, we present a hidden Markov model (HMM) based learning method to capture personal thermal comfort using infrared thermography of human face. We chose human face since its blood vessels has a higher density and it is not covered while performing regular activities in built environments. Learning algorithm has 3 hidden states (i.e., uncomfortably warm, comfortable, uncomfortably cool) and uses discretization for forming the observed states from the continuous infrared measurements. The approach can potentially be used for continuous monitoring of thermal comfort to capture the variations over time. We tested and validated the method in a four-day long experiment with 10 subjects and demonstrated an accuracy of 82.8% for predicting uncomfortable conditions.

Previous research efforts for optimizing energy usage of HVAC systems require either mathematical models of HVAC systems to be built or they require substantial historical operational data for learning the optimal operational settings. We introduce a model-free control policy that begins learning optimal settings with no prior historical data and optimizes HVAC operations. The control policy is an adaptive hybrid metaheuristic that uses real-time data, stored in building automation systems (e.g., gas/electricity consumption, weather, and occupancy), to find optimal setpoints at the building level and controls the setpoints accordingly. The algorithm consists of metaheuristic (k-nearest neighbor stochastic hill climbing), machine learning (regression decision tree), and self-tuning (recursive brute-force search) components. The control policy uses smart selection of daily setpoints as the control basis, making the control schema complementary to legacy building management systems. To evaluate, we used the DOE reference small office building in all U.S. climate zones and simulated different control policies using EnergyPlus. The proposed algorithm resulted in 31.17% energy savings compared to the baseline operations (22.5 °C and 3 K). The algorithm has a superior performance in all climate zones for the goodness of measure (i.e., normalized root mean square error) with a value of 0.047.

Previous studies have solely focused on establishing Machine Learning (ML) models for automated detection of stress arousal. However, these studies do not recognize stress appraisal and presume stress is a negative mental state. Yet, stress can be classified according to its influence on individuals; the way people perceive a stressor determines whether the stress reaction is considered as eustress (positive stress) or distress (negative stress). Thus, this study aims to assess the potential of using an ML approach to determine stress appraisal and identify eustress and distress instances using physiological and behavioral features. The results indicate that distress leads to higher perceived stress arousal compared to eustress. An XGBoost model that combined physiological and behavioral features using a 30 second time window had 83.38% and 78.79% F1-scores for predicting eustress and distress, respectively. Gender-based models resulted in an average increase of 2-4% in eustress and distress prediction accuracy. Finally, a model to predict the simultaneous assessment of eustress and distress, distinguishing between pure eustress, pure distress, eustress-distress coexistence, and the absence of stress achieved a moderate F1-score of 65.12%. The results of this study lay the foundation for work management interventions to maximize eustress and minimize distress in the workplace. [ABSTRACT FROM AUTHOR]

The common practice of defining operational settings for Heating, Ventilation and Air Conditioning (HVAC) systems in buildings is to use fixed set points, which assume occupants have same and static comfort requirements. However, thermal comfort varies from person to person and also changes due to climatic variations or acclimation, making it dynamic. In addition, thermal comfort in transient conditions are different from the steady state conditions, which makes the prediction of thermal comfort more difficult. Thus, thermal comfort has to be monitored over time. In this paper, we present a novel infrared thermography based technique to monitor an individual’s thermoregulation performance and thermal comfort levels by measuring the skin temperature on several points on human face, which has a high density of blood vessels and is not usually covered by clothing. Unlike other methods, our method requires no continuous user input or interaction. Our results demonstrate that the monitored facial points behave differently under the heat and cold stresses and it can be explained based on the underlying vascular territories. We define two heuristics to describe the thermoneutral zone based on the observed behaviors and estimate thermal comfort for individuals with 95% confidence level. Considerable variations are observed in the thermoregulation performance and uncomfortably cool conditions metrics between the males and females. Females’ thermoregulation system responses are less sensitive to the perception of warm conditions. However, similar behaviors are observed for uncomfortably cool conditions across genders.

This paper provides a systematic approach for quantifying the influence of building size, construction category, climate, occupancy schedule, setpoint, and deadband on HVAC energy consumption in office buildings. Simulating the DOE reference office buildings of three sizes and three construction categories in all United States climate zones, using the EnergyPlus, we conducted several N-way ANOVA analyses to study the interrelationships between setpoints, deadbands and several building related and environment related factors. In summary, daily optimal deadband selection of 0, 1, 2, 4, 5, and 6 K would result in an average energy savings of -70.0, -34.9, -13.7, 9.6, 16.4, and 21.2 %, respectively, compared to baseline deadline of 3 K. Selecting the daily optimal setpoint in the range of 22.5 ± 1 °C, 22.5 ± 2 °C, and 22.5 ± 3 °C would result in an average savings of 7.5, 12.7, and 16.4 %, respectively, compared to the baseline setpoint of 22.5 °C. Additionally, we found that when the outdoor temperature is within -20 to 30°C, the optimal setpoint depends on the building size. We also observed a range of outdoor temperatures (e.g., 9 – 14 °C for small buildings and 8 – 11 °C for medium buildings) where the setpoint selection would only slightly influence the energy consumption. However, the choice of setpoints becomes very influential (up to 30% of energy savings) where the outdoor temperatures are slightly outside the mentioned ranges on either direction. The potential savings from selecting daily optimal setpoints in the range of 22.5 ± 3 °C in different climates and for small, medium and large office buildings, would lead to 10.09 – 37.03%, 11.43 – 21.01%, and 6.78 – 11.34 % savings, respectively, depending on the climate.

HVAC systems are the major energy consumers in commercial buildings in the United States. These systems are operated to provide comfortable thermal conditions for building occupants. The common practice of defining operational settings for HVAC systems is to use fixed set points, which assume occupants have static comfort requirements. However, thermal comfort has been shown to vary from person to person and also change over time due to climatic variations or acclimation. In this paper, we introduce an online learning approach for modeling and quantifying personalized thermal comfort. In this approach, we fit a probability distribution to each comfort condition (i.e., uncomfortably warm, comfortable, and uncomfortably cool) data set and define the overall comfort of an individual through combing these distributions in a Bayesian network. In order to identify comfort variations over time, Kolmogorov–Smirnov test is used on the joint probability distributions. In order to identify comfortable environmental conditions, a Bayesian optimal classifier is trained using online learning. In order to validate the approach, we collected data from 33 subjects, and an average accuracy of 70.14% and specificity of 76.74% were achieved. In practice, this approach could transform the comfort objectives to constrain functions and prevents pareto optimality problems.