Search Results (2,034)

Enhancing the real-world performance of sustainably designed and certified green buildings remains a significant challenge, particularly in hot climates where efforts to improve thermal comfort often conflict with energy efficiency goals. In the United Arab Emirates (UAE), even newly constructed facilities with green building certifications present opportunities for retrofitting and performance optimization. This study investigates the energy and thermal comfort performance of a LEED Gold-certified, mixed-use university campus in Dubai through a calibrated digital twin developed using IES thermal modelling software. The analysis evaluated existing sustainable design strategies alongside three retrofit energy conservation measures (ECMs): (1) improved building envelope U-values, (2) installation of additional daylight sensors, and (3) optimization of fan coil unit efficiency. Simulation results demonstrated that the three ECMs collectively achieved a total reduction of 15% in annual energy consumption. Thermal comfort was assessed using operative temperature distributions, Predicted Mean Vote (PMV), and Predicted Percentage of Dissatisfaction (PPD) metrics. While fan coil optimization yielded the highest energy savings, it led to less favorable comfort outcomes. In contrast, enhancing envelope U-values maintained indoor conditions consistently within ASHRAE-recommended comfort zones. To further support energy reduction and progress toward Net Zero targets, the study also evaluated the integration of a 228.87 kW rooftop solar photovoltaic (PV) system, which offset 8.09% of the campus’s annual energy demand. By applying data-driven thermal modelling to assess retrofit impacts on both energy performance and occupant comfort in a certified green building, this study addresses a critical gap in the literature and offers a replicable framework for advancing building performance in hot climate regions. Full article

Blast mitigation of structures is an important research topic due to increasing intentional and accidental human-induced threats and hazards. This research area is essential to building capabilities in sustaining structural protection, site planning, protective design efficiency, occupant safety, and response and recovery plans. This paper investigates experimental tests and finite element analysis (FEM) of thin A36 steel sheets subjected to blast. Six field blast tests were performed at standoff distances of 300 mm and 500 mm. The explosive charges comprised 334 g of bare Composition B, and the steel sheets were 2 mm thick. The experimental results, derived from the analysis of high-speed camera recordings of the blast events, were compared with FEM simulations conducted using Abaqus^®/Explicit version 6.10. Three constitutive material models were considered in these simulations. First, the FEM simulation results were compared with experimental results. It was shown that the FEM analysis provided reliable results and was proven to be robust and cost-effective. Second, an extensive set of 460 additional numerical simulations was carried out as a parametric study involving varying standoff distances and steel sheet thicknesses. The results and methodologies presented in this paper offer valuable and original insights for engineers and researchers aiming to predict damage to steel structures during real detonation events and to design blast-resistant structures. Full article

Green buildings are recognised for their potential to reduce energy consumption, minimise environmental impact, and improve occupants’ well-being, benefits that are especially critical in rapidly urbanising regions. However, questions remain about whether these buildings fully meet occupant comfort expectations while delivering energy efficiency. This is particularly relevant in Africa, where climate conditions and energy infrastructure challenges make sustainable building operation essential. Although interest in sustainable construction has increased, limited research has examined the real-world performance of green buildings in Africa. This study helps address that gap by evaluating indoor thermal comfort in a green-certified office building and two conventional office buildings in Abuja, Nigeria, through post-occupancy evaluation (POE). The Predicted Mean Vote (PMV) and Thermal Sensation Vote (TSV) were used to assess comfort, revealing discrepancies between predicted and actual occupant responses. In the green building, PMV indicated near-neutral conditions (0.28), yet occupants reported a slightly cool sensation (TSV: −1.1). Neutral temperature analysis showed that the TSV-based neutral temperature (26.5 °C) was 2.2 °C higher than the operative temperature (24.3 °C), suggesting overcooling. These findings highlight the importance of incorporating occupant feedback into HVAC control. Aligning cooling setpoints with comfort preferences could improve satisfaction and reduce unnecessary cooling, promoting energy-efficient building operation. Full article

This study investigates indoor and outdoor air quality monitoring in a student dormitory located in northern Milan (Italy) using low-cost sensors. This research compares two monitoring periods in June and October 2024 to examine common PM_2.5 vertical patterns and differences at the building level, as well as their influence on the indoor spaces at the corresponding positions. In each period, around 30 sensors were installed at various heights and orientations across indoor and outdoor spots for 2 weeks to capture spatial variations around the building. Meanwhile, qualitative surveys on occupation presence, satisfaction, and well-being were distributed in selected rooms. The analysis of PM_2.5 data reveals that the building’s lower floors tended to have slightly higher outdoor PM_2.5 concentrations, while the upper floors generally had lower PM_2.5 indoor/outdoor (I/O) ratios, with the top-floor rooms often below 1. High outdoor humidity reduced PM infiltration, but when outdoor PM fell below 20 µg/m³ in these two periods, indoor sources became dominant, especially on the lower floors. Air pressure I/O differences had minimal impact on PM_2.5 I/O ratios, though slightly positive indoor pressure might help prevent indoor PM infiltration. Lower ventilation in Period-2 possibly contributed to more reported symptoms, especially in rooms with higher PM from shared kitchens. While outdoor air quality affects IAQ, occupant behavior—especially window opening and ventilation management—remains crucial in minimizing indoor pollutants. Users can also manage exposure by ventilating at night based on comfort and avoiding periods of high outdoor PM. Full article

Dynamic and Adaptive solar systems demonstrate a greater potential to enhance the satisfaction of occupants, in terms of indoor environment quality and the energy efficiency of the buildings, than conventional shading solutions. This study has evaluated Dynamic and Adaptive Photovoltaic Shading Systems (DAPVSSs) through a comprehensive analysis of six shading designs in which their energy production and the comfort of occupants were considered. Energy generation, thermal comfort, daylight, and glare control have been assessed in this study, considering multiple orientations throughout the seasons, and a variety of tools, such as Rhino 6.0, Grasshopper, ClimateStudio 2.1, and Ladybug, have been exploited for these purposes. The results showed that the prototypes that were geometrically more complex, designs 5 and 6 in particular, had approximately 485 kWh higher energy production and energy savings for cooling and 48% better glare control than the other simplified configurations while maintaining the minimum daylight as the threshold (min DF: 2%) due to adaptive and control methodologies. Design 6 demonstrated optimal balanced performance for all the aforementioned criteria, achieving 587 kWh/year energy production while maintaining the daylight factor within the 2.1–2.9% optimal range and ensuring visual comfort compliance during 94% of occupied hours. This research has established a framework that can be used to make well-informed design decisions that could balance energy production, occupants’ wellbeing, and architectural integration, while advancing sustainable building envelope technologies. Full article

Residential green space is an important component of urban green space and one of the major indicators for evaluating the quality of a residential community. Traditional indicators such as the green space ratio only consider the relationship between green space area and total area of the residential community while ignoring the difference in the amount of green space enjoyed by household residents in high-rise and low-rise buildings. Therefore, it is meaningful to estimate household green space and its spatial distribution in residential communities. However, there are frequent difficulties in obtaining specific green space area and household number through ground surveys or consulting with property management units. In this study, taking a composite residential community in Chongqing, China, as the study site, we first employed a five-lens drone to capture its oblique RGB images and generated the DOM (Digital Orthophoto Map). Subsequently, the green space area and distribution in the entire residential community were extracted from the DOM using VDVI (Visible Difference Vegetation Index). The YOLACT (You Only Look At Coefficients) instance segmentation model was used to recognize balconies from the facade images of high-rise buildings to determine their household numbers. Finally, the average green space per household in the entire residential community was calculated to be 67.82 m², and those in the high-rise and low-rise building zones were 51.28 m² and 300 m², respectively. Compared with the green space ratios of 65.5% and 50%, household green space more truly reflected the actual green space occupation in high- and low-rise building zones. Full article

According to the United Nations Environment Programme reports, buildings are responsible for nearly 40% of energy-related emissions; therefore, energy-optimized building design is crucial to reduce the reliance on non-renewable energy sources as well as greenhouse gas emissions. The OECD reports indicate the use of Building Information Modelling (BIM) as one of the effective strategies for decarbonization of buildings, since a 3D digital representation of both physical and functional characteristics of a building can help to design a more efficient infrastructure. An efficient integration of solar energy in building design can be vital for the enhancement of energy performance in terms of heating, cooling, and lighting demand. This paper presents results of an analysis of how factors related to the use of daylight, such as automatic control of artificial lighting, external shading, or the visual absorptance of internal surfaces, influence the energy efficiency within an example room in two different climatic zones. The simulation was conducted using Design Builder software, with predefined occupancy schedules and internal heat gains, and standard EPW weather files for Warsaw and Genua climate zones. The study indicates that for the examined room, when no automatic sunshades or a lighting control system is utilized, most of the final energy demand is for cooling purposes (45–54%), followed by lighting (42–43%), with only 3–12% for heating purposes. The introduction of sunshades and/or the use of daylight allowed for a reduction of the total demand by up to half. Moreover, it was pointed out that often neglected factors, like the colour of the internal surfaces, can have a significant effect on the final energy consumption. In variants with light interior, the total energy consumption was lower by about 3–4% of the baseline demand, compared to their corresponding ones with dark surfaces. These results are consistent with previous studies on daylighting strategies and highlight the importance of considering both visual and thermal impacts when evaluating energy performance. Similarly, possible side effects of certain actions were highlighted, such as an increase in heat demand resulting from a reduced need for artificial lighting. The results of the analysis highlight the potential of a simulation-based design approach in optimizing daylight use, contributing to the broader goals of building decarbonization. Full article

Smart living is strongly promoted to enhance the quality of life via the application of innovative solutions, and this is driven by domain specialists and policymakers, including designers, urban planners, computer engineers, and property developers. Nonetheless, the actual user, whose views ought to be considered during the design and development of smart living systems, has received little attention. Thus, this study aims to identify and examine the critical smart functions to achieve smart living in smart buildings based on occupants’ perceptions. The aim is achieved using a sequential quantitative research method involving a literature review and 221 valid survey data gathered from a case of a smart student residence in Hong Kong. The method is further integrated with descriptive statistics, the Kruskal–Walli’s test, and the criticality test. The results were validated via a post-survey with related experts. Twenty-six critical smart functions for smart living were revealed, with the top three including the ability to protect personal data and information privacy, provide real-time safety and security, and the ability to be responsive to users’ needs. A need was discovered to consider the context of buildings during the design of smart living systems, and the recommendation is for professionals to understand the kind of digital technology to be integrated into a building by strongly considering the context of the building and how smart living will be achieved within it based on users’ perceptions. The study provides valuable insights into the occupants’ perceptions of critical smart features/functions for policymakers and practitioners to consider in the construction of smart living systems, specifically students’ smart buildings. This study contributes to knowledge by identifying the critical smart functions to achieve smart living based on occupants’ perceptions of smart living by considering the specific context of a smart student building facility constructed in Hong Kong. Full article

Effective building operation requires a careful balance between energy conservation and indoor environmental comfort. Although numerous methods have been developed to reduce energy consumption during the operational phase, their objectives and applications vary widely. However, the complexity of building energy management makes it challenging to identify the most suitable methods that simultaneously achieve both comfort and efficiency goals. Existing studies often lack a systematic framework that supports integrated decision-making under comfort constraints. This research aims to address this gap by proposing a decision-making tree for selecting energy conservation methods during building operation with an explicit consideration of indoor environmental comfort. A comprehensive literature review is conducted to identify four main energy-consuming components during building operation: the building envelope, HVAC systems, lighting systems, and plug loads and appliances. Three key comfort indicators—thermal comfort, lighting comfort, and air quality comfort—are defined, and energy conservation methods are categorized into three strategic groups: passive strategies, control optimization strategies, and behavioural intervention strategies. Each method is assessed using a defined set of evaluation criteria. Subsequently, a questionnaire survey is administered for the calibration of the decision tree, incorporating stakeholder preferences and expert judgement. The findings contribute to the advancement of understanding regarding the co-optimization of energy conservation and occupant comfort in building operations. Full article

Improving occupational health and safety (OHS) in the construction industry can contribute to the advancement of the Sustainable Development Goals (SDGs), particularly Goals 3 (Good Health and Well-being) and 8 (Decent Work and Economic Growth). Yet, workers’ risk-taking behaviors (RTBs) remain a persistent challenge. Drawing on Social Cognitive Theory and Social Information Processing Theory, this study develops and tests a social influence model to examine how foremen’s safety attitudes (SAs) shape workers’ RTBs. Drawing on survey data from 301 construction workers in China, structural equation modeling reveals that foremen’s SAs significantly and negatively predict workers’ RTBs. However, the three dimensions of SAs—cognitive, affective, and behavioral—exert their influence through different pathways. Risk perception (RP) plays a key mediating role, particularly for the cognitive and behavioral dimensions. Furthermore, interpersonal trust (IPT) functions as a significant moderator in some of these relationships. By identifying the micro-social pathways that link foremen’s attitudes to workers’ safety behaviors, this study offers a testable theoretical framework for implementing the Sustainable Development Goals (particularly Goals 3 and 8) at the frontline workplace level. The findings provide empirical support for organizations to move beyond rule-based management and instead build more resilient OHS governance systems by systematically cultivating the multidimensional attitudes of frontline leaders. Full article

Effective earthquake early warning (EEW) is essential for disaster prevention in the built environment, enabling a rapid structural response, system shutdown, and occupant evacuation to mitigate damage and casualties. However, most current EEW systems lack rigorous reliability analyses of their predictive outcomes, limiting their effectiveness in real-world scenarios—especially for on-site warnings, where data are limited and time is critical. To address these challenges, we propose a Bayesian neural network (BNN) framework based on Stein variational gradient descent (SVGD). By performing Bayesian inference, we estimate the posterior distribution of the parameters, thus outputting a reliability analysis of the prediction results. In addition, we incorporate a continual learning mechanism based on elastic weight consolidation, allowing the system to adapt quickly without full retraining. Our experiments demonstrate that our SVGD-BNN model significantly outperforms traditional peak displacement (Pd)-based approaches. In a 3 s time window, the Pearson correlation coefficient R increases by 9.2% and the residual standard deviation $SD$ decreases by 24.4% compared to a variational inference (VI)-based BNN. Furthermore, the prediction variance generated by the model can effectively reflect the uncertainty of the prediction results. The continual learning strategy reduces the training time by 133–194 s, enhancing the system’s responsiveness. These features make the proposed framework a promising tool for real-time, reliable, and adaptive EEW—supporting disaster-resilient building design and operation. Full article

While personal thermal comfort is critical for well-being and productivity, it is often overlooked by traditional building management systems that rely on uniform settings. Modern data-driven approaches often fail to capture the complex interactions between various data streams. This pilot study introduces a high-accuracy, interpretable framework for thermal comfort classification, designed to identify the most significant predictors from a comprehensive suite of environmental, physiological, and anthropometric data in a controlled group of young adults. Initially, an XGBoost model using the full 24-feature dataset achieved the best performance at 91% accuracy. However, after using SHAP analysis to identify and select the most influential features, the performance of our ensemble models improved significantly; notably, a Random Forest model’s accuracy rose from 90% to 94%. Our analysis confirmed that for this homogeneous cohort, environmental parameters—specifically temperature, humidity, and CO₂—were the dominant predictors of thermal comfort. The primary strength of this methodology lies in its ability to create a transparent pipeline that objectively identifies the most critical comfort drivers for a given population, forming a crucial evidence base for model design. The analysis also revealed that the predictive value of heart rate variability (HRV) diminished when richer physiological data, such as diastolic blood pressure, were included. For final validation, the optimized Random Forest model, using only the top 10 features, was tested on a hold-out set of 100 samples, achieving a final accuracy of 95% and an F1-score of 0.939, with all misclassifications occurring only between adjacent comfort levels. These findings establish a validated methodology for creating effective, context-aware comfort models that can be embedded into intelligent building management systems. Such adaptive systems enable a shift from static climate control to dynamic, user-centric environments, laying the critical groundwork for future personalized systems while enhancing occupant well-being and offering significant energy savings. Full article

Urban agriculture (UA) is emerging as a promising strategy for sustainable food production in response to growing environmental pressures. Indoor vertical farming (IVF), combining Controlled Environment Agriculture (CEA) with Building-Integrated Agriculture (BIA), enables efficient resource use and year-round crop cultivation in urban settings. This study assesses the environmental performance of a prospective IVF system located on a university campus in Portugal, focusing on the integration of photovoltaic (PV) energy as an alternative to the conventional electricity grid (GM). A Life Cycle Assessment (LCA) was conducted using the Environmental Footprint (EF) method and the LANCA model to account for land use and soil-related impacts. The PV-powered system demonstrated lower overall environmental impacts, with notable reductions across most impact categories, but important trade-offs with decreased soil quality. The LANCA results highlighted cultivation and packaging as key contributors to land occupation and transformation, while also revealing trade-offs associated with upstream material demands. By combining EF and LANCA, the study shows that IVF systems that are not soil-based can still impact soil quality indirectly. These findings contribute to a broader understanding of sustainability in urban farming and underscore the importance of multi-dimensional assessment approaches when evaluating emerging agricultural technologies. Full article

Wood, as a natural material that stores carbon, is gaining increasing attention and has potential for use in interior architectural applications. Given the long indoor stay time characteristic of modern society, it is important to scientifically understand the effects of indoor wood application on the occupants. In this study, three residential buildings with an identical area and structure were constructed with different degrees of wood coverage (0%, 45%, 90%) using Larix kaempferi. Subsequently, indoor air quality (IAQ) evaluations and relative humidity measurements were conducted to assess the physical and chemical changes in each environment. The IAQ in wooden and non-wooden environments met the recommended IAQ standards established in South Korea. The results of the 8-month observation showed that, the higher the wood coverage ratio, the more the indoor humidity fluctuations were alleviated, and, in the case of the 90% wood coverage ratio condition, the humidity was maintained 5.2% lower in the summer and 10.9% higher in the winter compared to the 0% condition. To further assess the physiological responses induced by the wooden environment, the heart rate variability (HRV) was measured and compared for 26 participants exposed to each environment for two hours. In environments with a 0% and 90% degree of wood coverage, no statistically significant differences were found in the participants’ HRV indicators. But, in the group exposed to the 45% wooden environment, the results showed an increase in HRV indicators, natural logarithm of high frequency power (lnHF): 4.87 → 5.40 (p < 0.05), and standard deviation of normal-to-normal intervals (SDNN): 30.57 → 38.48 (p < 0.05), which are known indicators of parasympathetic nervous system activation. Full article

Infrared thermography is a common approach used in building inspection for identifying building envelope thermal anomalies that cause energy loss and occupant thermal discomfort. Detecting these anomalies is essential to improve the thermal performance of energy-inefficient buildings through energy retrofit design and correspondingly reduce operational energy costs and environmental impacts. A thermal bridge is an unwanted conductive heat transfer. On the other hand, an infiltration/exfiltration anomaly is an uncontrollable convective heat transfer, typically happening around windows and doors, but it can also be due to a defect that comprises a building envelope’s integrity. While the existing literature underscores the significance of automatic thermal anomaly identification and offers insights into automated methodologies, there is a notable gap in addressing an automated workflow that leverages building envelope component segmentation for enhanced detection accuracy. Consequently, an automatic thermal anomaly identification workflow from visible and thermal images was developed to test it, utilizing segmented building envelope information compared to a workflow without any semantic segmentation. Therefore, building envelope images (e.g., walls and windows) were segmented based on a U-Net architecture compared to a more conventional semantic segmentation approach. The results were discussed to better understand the importance of the availability of training data and for scaling the workflow. Then, thermal anomaly thresholds for different target domains were detected using probability distributions. Finally, thermal anomaly masks of those domains were computed. This study conducted a comprehensive examination of a campus building in Syracuse, New York, utilizing a drone-based data collection approach. The case study successfully detected diverse thermal anomalies associated with various envelope components. The proposed approach offers the potential for immediate and accurate in situ thermal anomaly detection in building inspections. Full article