Quality Of Built Environment Research Articles

Interpretable reduced-order optimization research for improving indoor environmental quality in buildings based on dimensional analysis and surrogate-based optimization

Implementing large-scale optimization designs and identifying optimal design variables in fields such as architectural engineering is crucial for improving indoor environmental quality. However, the high-dimensional nonlinear characteristics complicate the understanding of the optimization process and increase the difficulty of finding optimal solutions. This study proposes an interpretable reduced-order optimization (ROO) method based on dimensional analysis, which integrates numerous variables into a single reduced-order coefficient through a power form. The goal is to develop a reduced-order and approximately one-dimensional nonlinear relationship during the optimization process and to reveal the mechanisms of single-objective and multi-objective optimization, thereby enhancing the interpretability of the optimization process. The reliability of the ROO is validated through single-objective optimization of building thermal bridges and multi-objective optimization of squirrel cage fans. The study shows that the power in the reduced-order coefficient can be used to determine the sensitivity and impact of design variables on optimization targets. The nonlinear relationship between the composite structural parameters of the building thermal bridge and the total heat flux can be reduced to an approximately one-dimensional linear relationship, significantly reducing the total heat flux of the thermal bridge by 10.3 %. The ROO accurately describes the aerodynamic performance of the fan and its high-dimensional nonlinear relationship with eight design variables, eliminating optimization conflicts among multiple operating conditions and significantly enhancing the aerodynamic performance. The ROO method proposed in this study offers a new approach to making the optimization process more interpretable, playing a crucial role in optimization design and revealing optimization mechanisms in engineering applications.

Expanding the associations between built environment characteristics and residential mobility in high-density neighborhood unit

Global trend of urbanization has led to frequent residential mobility and urban shrinkage issues. Planners and policy makers advocate for enhancing built environment quality of neighbourhood units to address these issues. Although the impact factors and degree of the influence of the built environment on residential mobility have been studied, the nonlinear effects at the neighbourhood level, and the relative importance when considered alongside other factors, remain unclear. In this study, the central area of Nanjing using mobile phone signalling data, the 5Ds framework, machine learning algorithms, and interpretable model Shapley Additive exPlanations (SHAP) are examined. The finding shows that (1) economy and neighbourhood ties are still key drivers of relocation. (2) Optimizing highly accessible road network for short-distance travel and developing low-density urban mode has become significant methods to attract relocators, interacting with other factors to influence residential mobility. (3) High-quality neighbourhood design, diverse amenities, and proximity to natural landscapes increase willingness to relocate, (4) while religion, socio-demographics, and large-scale transportation accessibility have minimal impact. The study offers four urban development recommendations to help municipal planners and policy makers create liveable cities and cohesive communities, providing essential insights for early or renewal stage urban planning stages.

Advanced Technologies on Indoor Environment Quality in Sustainable Buildings

With the improvement of living standards and the enhancement of health awareness, Indoor Environmental Quality (IEQ) has become a hot topic of concern for the public and researchers [...]

Multi-objective hierarchical strategy for university dorm renovation in severe cold areas

In China, there are many old buildings whose renovation could enhance building environment quality, reduce energy consumption, lower carbon emissions, and create economic benefits. However, there is currently a lack of efficient architectural renovation optimization methods to provide decision-makers and designers with highly feasible renovation plans. This paper proposes an innovative optimization method for renovating old buildings to address this issue. The research employs a hierarchical optimization framework integrating Artificial Neural Networks (ANN), the NSGA-II algorithm, and the entropy-weighted TOPSIS method. The first layer's three-objective optimization using the trained neural network model significantly improves the algorithm's efficiency in searching for optimal solutions. The second layer's entropy-weighted TOPSIS optimization includes eight objective optimizations, such as energy, economy, and lighting, thus broadening the assessment dimensions. Taking an old multi-story university dormitory in the Shenyang area as an example, the study optimizes nine passive design variables across eight objectives. The hierarchical optimization results show that the high-scoring renovation plans can save 1.52 to 3.17 million yuan in energy costs over the building's remaining lifecycle and reduce carbon dioxide emissions by 644–962 kg per square meter. SRRC results indicate that external windows significantly impact economic efficiency and energy consumption, emphasizing the importance of selecting the right type of windows in actual engineering projects. The new method proposed in this study can offers a comprehensive and highly feasible renovation plan for old buildings.

A Systematic Review on the Use of AI for Energy Efficiency and Indoor Environmental Quality in Buildings

Global warming, climate change and the energy crisis are trending topics around the world, especially within the energy sector. The rising cost of energy, greenhouse gas (GHG) emissions and global temperatures stem from the over-reliance on fossil fuel as the major energy resource. These challenges have highlighted the need for alternative energy resources and urgent intervention strategies like energy consumption reduction and improving energy efficiency. The heating, ventilation, and air-conditioning (HVAC) system in a building accounts for about 70% of energy consumption, and a decision to reduce energy consumption may impact the indoor environmental quality (IEQ) of the building. It is important to adequately balance the tradeoff between IEQ and energy management. Artificial intelligence (AI)-based solutions are being explored for improving building energy performance without compromising IEQ. This paper systematically reviews recent studies on AI and machine learning (ML) for building energy management and IEQ by exploring common use areas, the methods or algorithms applied and the results obtained. The overall purpose of this research is to add to the existing body of work and to highlight energy-related AI applications in buildings and the related gaps. The result shows five common application areas: thermal comfort and indoor air quality (IAQ) control; energy management and energy consumption prediction; indoor temperature prediction; anomaly detection; and HVAC controls. Gaps involving policy, real-life scenario applications, and insufficient study of the visual and acoustic comfort areas are also identified. Very few studies take into consideration the need to follow IEQ standards in the selection process and positioning of sensors in AI applications for IEQ in buildings. This study reveals a need for more systematically summarized research.

Effects of neighborhood built environment on cognitive function in older adults: a systematic review

BackgroundIn the background of an aging population, the risk of cognitive impairment in the older population is prominent. Exposure to complex neighborhood built environments may be beneficial to the cognitive health of older adults, and the purpose of this study was to systematically review the scientific evidence on the effects of neighborhood built environments on cognitive function in older adults.MethodsKeywords and references were searched in Web of Science, Pubmed, PsycINFO, and MEDLINE. Studies examining the relationship between the built environment and cognitive function in older adults were included. The neighborhood built environment as an independent variable was classified according to seven aspects: density, design, diversity, destination accessibility, public transportation distance, blue/green space, and built environment quality. The cognitive function as the dependent variable was classified according to overall cognitive function, domain-specific cognitive function, and incidence of dementia. The quality of the included literature was assessed using the National Institutes of Health's Observational Cohort and Cross-Sectional Study Quality Assessment Tool.ResultsA total of 56 studies were included that met the inclusion criteria, including 31 cross-sectional studies, 23 longitudinal studies, 1 cross-sectional study design combined with a case-control design, and 1 longitudinal study design combined with a case-control design. Most of the studies reviewed indicate that the built environment factors that were positively associated with cognitive function in older adults were population density, street connectivity, walkability, number of public transportation stops around the residence, land use mix, neighborhood resources, green space, and quality of the neighborhood built environment. Built environment factors that were negatively associated with cognitive function in older adults were street integration, distance from residence to main road. The relationship between residential density, destination accessibility, and blue space with cognitive function in older adults needs to be further explored.ConclusionPreliminary evidence suggests an association between the neighborhood built environment and cognitive function in older adults. The causal relationship between the built environment and cognitive function can be further explored in the future using standardized and combined subjective and objective assessment methods, and longitudinal or quasi-experimental study designs. For public health interventions on the cognitive health of older adults, it is recommended that relevant authorities include the neighborhood built environment in their intervention programs.

Indoor Environmental Quality In Preschool Buildings In an Andean City In Ecuador

ABSTRACT Indoor environmental quality has been associated with the health and wellbeing of building occupants; nevertheless, there is limited evidence in this regard for Latin American schools. This research aimed to characterize indoor environmental quality in public and private preschools in an Andean city in Ecuador. Data collection comprised onsite monitoring for the thermal-humidity microclimate of 90 classrooms in 30 preschools in Cuenca-Ecuador (March-August 2018). Infrared thermography and direct observation were applied to determine dampness. Classrooms seemed to be inadequate thermal-humidity microclimates; only a few maintained a comfortable temperature (6%) and relative humidity (11%) throughout the shift. When comparing public and private schools, in private schools, temperatures below the comfort range (61.3% in private schools vs 31.4% in public schools, p<0.001) and relative humidity measures above the comfort range were more frequent (74.3% in private schools vs. 58.6% in public schools, p<0.001). Hollow blocks were the primary construction material in private and public schools. Sixty-four per cent of private schools operated in adaptive, reused buildings, vs 19% in public schools (p<0.05). Infrared thermography confirmed dampness in 26% (n=23) of the classrooms in the covering structures indoors (15% in public vs 33% in private schools, p<0.05). This research reveals the urgent need to develop specific regulations and control mechanisms for building sustainable and healthy environments for preschools in Ecuador.

The regulation of families with children in apartments

Condominium housing is now ubiquitous in contemporary cities globally. For residents, the success of compact cities depends on built environment quality, social relationships within buildings, and broader social norms. This includes how private and shared apartment spaces are navigated, regulated, and controlled. This article focuses on the experiences of families with children who comprise a growing cohort of apartment residents. We build on existing research that documents families’ experiences of poor design, to recognize the role regulation plays in shaping a sense of home. We theorize regulation at three levels: (1) social regulation as interactions between individuals who influence each other’s behaviour; (2) self-regulated behaviours; and (3) institutional regulation through rules and laws. Utilizing narratives of parents raising children in apartments in Sydney, Australia, we argue social norms, neighbourly interactions, and by-laws interact to enforce codes of behaviour that impinge upon family life. Our insights have important implications for the wellbeing and inclusion of families within compact city agendas.

Comparative Analysis of Indoor Environmental Quality and Self-Reported Productivity in Intelligent and Traditional Buildings

People tend to spend considerable amounts of time in buildings; thus the issue of providing proper indoor environmental quality is of significant importance. This paper experimentally analyses the subjective sensations of the occupants of intelligent and traditional buildings with the focus on possible differences between these two types of buildings. The study is based on a large database of 1302 questionnaires collected in 92 rooms where simultaneous measurements of the indoor environment physical parameters (air and globe temperature, relative humidity, carbon dioxide concentration, and illuminance) were carried out. Their impact on the subjective assessment of the indoor environment has been presented and analysed. The results show that the occupants seemed to be more favourable towards the indoor conditions in the intelligent building; however, the differences in comparison to the traditional buildings were not considerable. Similarly, self-reported productivity proved to be higher in the intelligent building, while the optimal range of air temperature, which ensured highest productivity, was 22–25 °C. Moreover, a strong correlation between the occupants’ overall comfort and their perception of the air quality has been found.

Window state or action modeling? An explainable AI approach in offices

Window operation significantly impacts energy use and indoor environmental quality in buildings. Individuals behave differently, making it difficult for models trained on a specific dataset to encompass the entire spectrum of these actions. A generalizable model is essential to predict the behavior of diverse occupants in office spaces. To address this need, this paper presents a systematic approach that captures this diversity, thereby contributing to developing a model towards generalizability. The approach involves state and action modeling through a Random Forest algorithm on the ASHRAE Global Occupant Behavior Database. The data pre-processing, hyperparameter tuning, and evaluation are deeply described and applied to window action and state datasets.Our results demonstrated that including metadata in a state model and applying a G-Mean threshold moving technique can result in an F1-score of 0.74. This score slightly outperformed the state room-wise model, which was trained only on its own dataset and achieved an F1-score of 0.73. However, both models had similar accuracies of 77%. The action model did not fare as well as the state models, with an F1-score and accuracy score of just 0.42 and 49%, respectively. In contrast, the action model showed more explainable results for domain experts than state models.

The Role of the Energy Use in Buildings in Front of Climate Change: Reviewing a System’s Challenging Future

Energy keeps the global economy alive, while also being extensively exposed to various climate change impacts. In this context, severe business competition (e.g., the building sector) and the unwise use of natural resources and ecosystem services (e.g., fossil fuel energy sources) seem to sharpen the relevant effects of climate change. Indicatively, contemporary issues at the interface of building energy performance and environmental quality levels include consequences from global warming, the increasing release of carbon dioxide to peak electrical loads, power grids, and building planning, and energy demand and supply issues. In light of such concerns, the present review paper attempts to disclose the multifaceted and multidisciplinary character of building energy use at the interface of the economy, the environment, and society against climate change. This review highlights energy efficiency concepts, production, distribution, consumption patterns, and relevant technological improvements. Interestingly, the reviewed contributions in the relevant literature reveal the need and necessity to alter the energy mix and relevant energy use issues. These include developments in climate-proof and effective systems regarding climate change impacts and shocks. Practical implications indicate that the sustainable development goals for clean energy and climate action should be followed if we wish to bring a sustainable future closer and faster to our reality.

The mediating effect of the built environment on the relationship between socioeconomic status and mental health: findings from the 2019 Shanghai Community Survey

Abstract Objective: Based on the theory of spatial production, the built environment is considered a mediating mechanism for health inequality. This study tested the hypothesis that communities, where populations with a higher socioeconomic status live, have a higher-quality built environment, which, in turn, promotes their mental health. Methods: Based on data from the 2019 Shanghai Community Survey (n = 3233), the general linear regression model (Ordinary Least Squares) and the classic three-step method developed by Baron and Kenny (1986) were used to test the mediation effects; subsequently, a structural equation model (bootstrap) was used to examine average direct effects and average causal mediation effects. Results: There were fewer types of aversive facilities, more green spaces or sports fields conducive to leisure and exercise, and better environmental hygiene in communities with higher-income populations, all of which improved their mental health. The total effect of income on mental health was 0.145 (P < 0.001), of which the average causal mediation effect through the built environment was 0.013 (P < 0.05), accounting for ~8.97%. Among the 3 dimensions of the built environment, the sanitation environment had a greater effect, accounting for 55.07% of the total mediation effect. Surrounding facilities accounted for 34.73%, whereas community green and sports spaces accounted for ~10.19%. Conclusions: The built environment quality of communities where low-income groups reside is worse, resulting in lower levels of their mental health. In the spatial planning of built environments, the socioeconomic characteristics of different residential groups should be considered, and the environmental quality of low-income communities should be improved using targeted measures.

A personalized bikeability-based cycling route recommendation method with machine learning

Urban built environment quality along the travel route is positively correlated with active mobility. However, most previous personalized route recommendation research for cycling emphasizes non-visual attributes, ignoring the significance of visual attributes such as the built environment. Therefore, to take the built environment into consideration in the cycling route planning, we propose the personalized bikeability-based cycling route recommendation (PBCRR) method. The method consists of three steps: (1) deep neural networks are utilized to achieve statistics on built environment attributes and features of bikeability; (2) the weights road impedance calculation function is used to calculate route recommendation indicators according to minimum impedance sum of road segments; and (3) the recommended cycling route is found and shown to the user based on the calculated indicators. The results indicate that our PBCRR presents a high level of effectiveness and differences, and achieves better results than the current route recommendation algorithm in guiding cyclists towards routes with a better built environment.

The effect of the perceptible built environment on pedestrians’ walking behaviors in commercial districts: Evidence from Hong Kong

In order to structure an efficient and comfortable commercial district for pedestrians, we need to understand the interaction between pedestrian walking behavior and the complex elements of the built environment. Previous studies have focused on people’s activities in the context of the neighborhood rather than the commercial district. This study investigates the potential associations between multi-dimensional environmental factors and pedestrians under various temporal distributions in a densely populated commercial district. Multi-source urban data and semantic segmentation technics have been adopted to measure the built environmental quality from four classic dimensions of urban design, and combining the observations of pedestrian volumes of representative streets in the commercial district, we assess the relationship between the two at different times on the basis of a generalized linear model (GLM). The analytical results identify that the Morphology, Visual perception, Function, and Street configuration features of the commercial environment have a significant impact on walking activity, and temporal differences exist. The findings highlight the importance of built environment quality to pedestrians and street attractiveness, and inform designers, stakeholders, and municipalities on the revitalization of traditional commercial districts.

The Influence of the Cultural Aspect on Built Environment of Traditional Villages

The paper explores re-thinking designing traditional villages. It focuses on the cultural aspects that form traditional villages and their different architectural and urban design qualities. Several questionnaires are distributed to assess the relation between the cultural aspect and the qualities of the physical built environment to suggest an assessment tool for investigating and quantifying the cultural aspect and physical built environment qualities of future traditional villages’ designs.

A Review on Adaptive Thermal Comfort of Office Building for Energy-Saving Building Design

The thermal environment quality of office buildings has an important role because thermal comfort is directly related to human productivity. Thermal comfort conditions are influenced by climate, location, and the built environment; hence, comfort standards are required to assist building designers in creating a comfortable indoor environment for building occupants. In this context, the present study analyzes the adaptive thermal comfort studies conducted in office buildings from various countries. A large number of research articles selected from the Scopus database were considered for this study. Based on the analysis, outdoor climatic conditions have a greater influence on indoor thermal conditions in naturally ventilated than in air-conditioned office buildings. The temperature required for comfort is as low as 17.6 °C and as high as 31.2 °C in naturally ventilated buildings. An adaptive comfort equation for naturally ventilated and air-conditioned office buildings has also been proposed to predict the indoor comfort temperature. Various studies show that a substantial amount of energy can be saved by changing the set point and natural ventilation. Furthermore, this study successfully provides hearty evidence that there is a need for climate-specific standards on thermal comfort for energy-efficient design development because existing comfort standards might not be applicable to all climates.

Social environments still matter: The role of physical and social environments in place attachment in a transitional city, Guangzhou, China

Place attachment, as an important emotional and functional bond between people and place, has received extensive attention in the early-21st century. This trend is especially evident in China, where rapid market transitions have fundamentally changed urban neighborhoods. Using a survey of residents in Guangzhou, China, remote sensing imagery, geographic information system (GIS) techniques, and hierarchical linear modeling (HLM), this study investigates the role of physical and social environments in place attachment. Results indicate that commodity housing residents generally do not necessarily have strong place attachment. However, higher quality physical and social environments do contribute to stronger place attachment. Specifically, perceived housing conditions, social ties, sense of security, and lower floor area ratio (FAR) are positively associated with place identity, while perceived housing conditions, perceived neighborhood environment, social ties, lower FAR, and built environment quality are positively associated with place dependence. Social ties are the strongest predictor of place identity while housing conditions are the strongest predictor of place dependence. We conclude that social environments still matter as social and physical environments play different roles in place identity and place dependence. Based on the findings, we advocate that urban (re)development and housing policies should shift the focus from large-scale demolitions to micro-scale dwelling improvements; and in addition to objective measures, subjective evaluation by residents should also be used to evaluate urban (re)development projects.

Measuring Urban Poverty Spatial by Remote Sensing and Social Sensing Data: A Fine-Scale Empirical Study from Zhengzhou

Urban poverty is a major obstacle to the healthy development of urbanization. Identifying and mapping urban poverty is of great significance to sustainable urban development. Traditional data and methods cannot measure urban poverty at a fine scale. Besides, existing studies often ignore the impact of the built environment and fail to consider the equal importance of poverty indicators. The emerging multi-source big data provide new opportunities for accurately measuring and monitoring urban poverty. This study aims to map urban poverty spatial at a fine scale by using multi-source big data, including social sensing and remote sensing data. The urban core of Zhengzhou is selected as the study area. The characteristics of the community’s living environment are quantified by accessibility, block vitality, per unit rent, public service infrastructure, and socio-economic factors. The urban poverty spatial index (SI) model is constructed by using the multiplier index of the factors. The SOM clustering method is employed to identify urban poverty space based on the developed SI. The performance of the proposed SI model is evaluated at the neighborhood scale. The results show that the urban poverty spatial measurement method based on multi-source big data can capture spatial patterns of typical urban poverty with relatively high accuracy. Compared with the urban poverty space measured based on remote sensing data, it considers the built environment and socio-economic factors in the identification of the inner city poverty space, and avoids being affected by the texture information of the physical surface of the residential area and the external structure of the buildings. Overall, this study can provide a comprehensive, cost-effective, and efficient method for the refined management of urban poverty space and the improvement of built environment quality.

Seismic risk assessment for the North Eastern Region of India by integrating seismic hazard and social vulnerability

ABSTRACT The present study aims at conducting a comprehensive seismic risk assessment for the North Eastern Region of India at regional and sub-regional levels by integrating probabilistic seismic hazard and social vulnerability assessments. Bedrock-level peak ground acceleration varied from 0.14 to 0.69g for the return period of 475 years. Using PCA, the social vulnerability index (SVI) was generated considering district-level socioeconomic indicators. Built environment quality, illiteracy, access to amenities, dependent population, and employment opportunities contributed tohigh SVI. Most vulnerable districts were concentrated in the Brahmaputrafloodplains, Tripura fold belt, and Imphal valley. At the regional level,significant parts of Assam, Meghalaya, Arunachal Pradesh, and Tripura lie inmoderate to very high-risk zones. At the sub-regional level, Nagaland accountsfor the highest proportion of areas in high to very high-risk zones. The findings will aid site-specific resilient infrastructure design, disaster risk reduction, and effective resource allocation for the risk-prone areas.

Multi-domain indoor environmental quality in buildings: A review of their interaction and combined effects on occupant satisfaction

Occupant satisfaction is influenced by the interaction and combined effects of multi-domain indoor environmental quality (IEQ) in buildings. While there are literature reviews on multi-domain IEQ, two limitations exist: 1) the reviewed IEQ interactions are mainly limited to two IEQ domains and climate chamber studies; and 2) those reviews have not quantitatively discussed the impact of IEQ interactions on overall satisfaction and only cover limited building types. To address this gap, this study adopts a broad, top-down approach with a three-stage screening to select relevant publications about the four most concerned IEQ domains—thermal comfort, acoustic comfort, visual comfort, and indoor air quality. A total of 74 publications have been selected and reviewed. Our study shows that: 1) existing findings on IEQ interaction and combined effects are inconsistent and somewhat contradictory; 2) interactions among three IEQ domains exist but are much less examined compared to two IEQ interactions. Studies on four IEQ are not found; 3) the current occupant satisfaction models are mainly generalized models. Personal models are an emerging area of research and practice; and 4) the one-vote veto effect exists, i.e., some IEQ domains show a predominating influence on overall satisfaction. We advocate future research 1) to collaborate on a global IEQ database to enable model comparison and the development of standards and guidelines spanning multiple IEQ domains, 2) to investigate personal overall satisfaction models encompassing IEQ interactions; and 3) to further examine non-linear modeling approaches for interpreting the relationships between IEQ and overall satisfaction.