Search Results (85)

This study presents a multi-objective optimization framework for enhancing environmental performance in high-density residential complexes, addressing the critical balance between sunlight access, visual openness, and ground-level privacy. Applied to Helio City Phase 3 in Seoul—a challenging case with 2026 units surrounded by adjacent blocks—the research developed a sequential three-stage optimization strategy using computational design tools. The methodology employs Ladybug simulations for solar analysis, Galapagos genetic algorithms for view optimization, and parametric modeling for privacy assessment. Through grid-based layout reconfiguration, tower form modulation, and strategic conversion of vulnerable ground-floor units to public spaces, the optimized design achieved 100% sunlight standard compliance (improving from 64.31%), increased average visual openness to 66.31% (from 39.48%), and eliminated all privacy conflicts while adding 30 residential units. These results demonstrate that computational optimization can significantly surpass conventional planning approaches in addressing complex environmental trade-offs. The framework provides a replicable methodology for performance-driven residential design, offering quantitative tools for achieving regulatory compliance while enhancing residents’ experiential comfort in dense urban environments. Full article

The present study was motivated by the need to enhance indoor air quality and reduce airborne disease transmission in dense urban environments where high-rise residential buildings face challenges in achieving effective natural ventilation. The problem lies in the lack of scalable and convenient tools to optimize natural ventilation rate, particularly in urban settings with varying building heights. To address this, the scientific technique developed with an innovative metric, the urbanized natural ventilation effectiveness factor (uNVeF), integrates regression analysis of wind direction, velocity, air change rate per hour (ACH), window configurations, and building height to quantify ventilation efficiency. By employing a field measurement methodology, the measurements were conducted across 25 window-opening scenarios in a 13.9 m² residential unit on the 35/F of a Hong Kong public housing building, supplemented by the Hellman Exponential Law with a site-specific friction coefficient (0.2907, R² = 0.9232) to estimate the lower floor natural ventilation rate. The results confirm compliance with Hong Kong’s statutory 1.5 ACH requirement (Practice Note for Authorized Persons, Registered Structural Engineers, and Registered Geotechnical Engineers) and achieving a peak ACH at a uNVeF of 0.953 with 75% window opening. The results also revealed that lower floors can maintain 1.5 ACH with adjusted window configurations. Using the Wells–Riley model, the estimation results indicated significant airborne disease infection risk reductions of 96.1% at 35/F and 93.4% at 1/F compared to the 1.5 ACH baseline which demonstrates a strong correlation between ACH, uNVeF and infection risks. The uNVeF framework offers a practical approach to optimize natural ventilation and provides actionable guidelines, together with future research on the scope of validity to refine this technique for residents and developers. The implications in the building industry include setting up sustainable design standards, enhancing public health resilience, supporting policy frameworks for energy-efficient urban planning, and potentially driving innovation in high-rise residential construction and retrofitting globally. Full article

Assessing energy consumption in existing old residential buildings is key for urban energy conservation and decarbonization. Previous studies on old residential building energy assessment face challenges due to data limitations and inadequate prediction methods. This study develops a novel approach integrating building energy simulation and machine learning to predict large-scale old residential building energy use using multi-source datasets. Using Guangzhou as a case study, open-source building data was collected to identify 31,209 old residential buildings based on age thresholds and areas of interest (AOIs). Key building form parameters (i.e., long side, short side, number of floors) were then classified to identify residential archetypes. Building energy consumption data for each prototype was generated using EnergyPlus (V23.2.0) simulations. Furthermore, XGBoost and Random Forest machine learning algorithms were used to predict city-scale old residential building energy consumption. Results indicated that five representative prototypes exhibited cooling energy use ranging from 17.32 to 21.05 kWh/m², while annual electricity consumption ranged from 60.10 to 66.53 kWh/m². The XGBoost model demonstrated strong predictive performance (R² = 0.667). SHAP (Shapley Additive Explanations) analysis identified the Building Shape Coefficient (BSC) as the most significant positive predictor of energy consumption (SHAP value = 0.79). This framework enables city-level energy assessment for old residential buildings, providing critical support for retrofitting strategies in sustainable urban renewal planning. Full article

The impact of the built environment (BE) on urban vitality (UV) has become a key issue in the field of urban planning. However, few studies have explored the impact of the BE on UV from the perspective of urban function zones (UFZs). Taking the central urban area of Tianjin as an example, this paper explores the nonlinear influences and threshold effects of the BE on UV using machine learning methods. It also reveals the spatiotemporal variations in UV across different UFZs during the daytime and nighttime on weekdays and weekends. The results show the following: (1) Education and culture zones showed the highest UV during weekday daytime, while commercial zones dominated at other times. Industrial zones remained the least active throughout. Residential zones demonstrated higher nighttime UV than daytime UV on weekdays, with the opposite pattern observed on weekends. Public service zones maintained a comparable level of UV between the daytime and nighttime on weekdays. Other function zones generally displayed higher daytime UV. During the daytime on weekends, all function zones except industrial zones demonstrated higher UV compared to other time periods. (2) In commercial zones, the floor area ratio (FAR) exerted the strongest influence, displaying distinct threshold effects. Residential zones showed dual sensitivity to building height (BH) and the FAR. Public service zones were predominantly influenced by Road Density (RD) and Bus Station Density (BSD). RD exhibited higher marginal utility for enhancing UV during the nighttime. Education and culture zones were significantly influenced by the FAR, RD, and POI Density (POID). Full article

In southern Hunan province, a vital element of China’s architectural cultural legacy, the quality of the indoor lighting environment influences physical performance and the transmission of spatial culture. The province encounters minor environmental disparities and diminishing liveability attributed to evolving construction practices and cultural standards. The three varieties of traditional residences in Shanggantang Village are employed to assess the daylight factor (DF), illumination uniformity (U0), daylight autonomy (DA), and useful daylight illumination (UDI). We subsequently integrate field measurements with static and dynamic numerical simulations to create a multi-dimensional analytical framework termed “measured-static-dynamic”. This method enables the examination of the influence of floor plan layout on light, as well as the relationship between window size, building configuration, and natural illumination. The lighting factor (DF) of the core area of the central patio-type residence reaches 27.7% and the illumination uniformity (U0) is 0.62, but the DF of the transition area plummets to 1.6%; the composite patio type enhances the DF of the transition area to 1.2% through the alleyway-assisted lighting, which is a 24-fold improvement over the offset patio type. Parameter optimization showed that the percentage of all-natural daylighting time (DA) in the edge zone of the central patio type increased from 21.4% to 58.3% when the window height was adjusted to 90%. The results of the study provide a quantitative basis for the optimization of the light environment and low-carbon renewal of traditional residential buildings. Full article

Urban regeneration is pivotal to sustainable development, requiring innovative strategies that align social dynamics with spatial configurations. Traditional paradigms increasingly fail to tackle systemic challenges—neighborhood alienation, social fragmentation, and resource inequality—due to their inability to integrate human-centered spatial governance. This study addresses these shortcomings with a novel multidimensional framework that merges social perception (life satisfaction) analytics with spatial quality (GIS-based) assessment. At its core, we utilize geospatial and machine learning models, deploying an ensemble of Gradient Boosted Decision Trees (GBDT), Random Forest (RF), and multiscale geographically weighted regression (MGWR) to decode nonlinear socio-spatial interactions within Suzhou’s community environmental matrix. Our findings reveal critical intersections where residential density thresholds interact with commercial accessibility patterns and transport network configurations. Notably, we highlight the scale-dependent influence of educational proximity and healthcare distribution on community satisfaction, challenging conventional planning doctrines that rely on static buffer-zone models. Through rigorous spatial econometric modeling, this research uncovers three transformative insights: (1) Urban environment exerts a dominant influence on life satisfaction, accounting for 52.61% of the variance. Air quality emerges as a critical determinant, while factors such as proximity to educational institutions, healthcare facilities, and public landmarks exhibit nonlinear effects across spatial scales. (2) Housing price growth in Suzhou displays significant spatial clustering, with a Moran’s I of 0.130. Green space coverage positively correlates with price appreciation (β = 21.6919 ***), whereas floor area ratio exerts a negative impact (β = −4.1197 ***), highlighting the trade-offs between density and property value. (3) The MGWR model outperforms OLS in explaining housing price dynamics, achieving an R² of 0.5564 and an AICc of 11,601.1674. This suggests that MGWR captures 55.64% of pre- and post-pandemic price variations while better reflecting spatial heterogeneity. By merging community-expressed sentiment mapping with morphometric urban analysis, this interdisciplinary research pioneers a protocol for socio-spatial integrated urban transitions—one where algorithmic urbanism meets human-scale needs, not technological determinism. These findings recalibrate urban regeneration paradigms, demonstrating that data-driven socio-spatial integration is not a theoretical aspiration but an achievable governance reality. Full article

In response to the growing importance of AI-driven residential design and the lack of dedicated evaluation metrics, we propose the Residential Floor Plan Assessment (RFP-A), a comprehensive framework tailored to architectural evaluation. RFP-A consists of multiple metrics that assess key aspects of floor plans, including room count compliance, spatial connectivity, room locations, and geometric features. It incorporates both rule-based comparisons and graph-based analysis to ensure design requirements are met. A comparison of RFP-A and existing metrics was conducted both qualitatively and quantitatively, and it was revealed that RFP-A provides more robust, interpretable, and computationally efficient assessments of the accuracy and diversity of generated plans. We evaluated the performance of six existing floor plan generation models using RFP-A, showing that, surprisingly, only HouseDiffusion and FloorplanDiffusion achieved accuracies above 90%, while other models scored below or around 60%. We further conducted a quantitative comparison of diversity, revealing that FloorplanDiffusion, HouseDiffusion, and HouseGAN each demonstrated strengths in different aspects—graph structure, spatial location, and room geometry, respectively—while no model achieved consistently high diversity across all dimensions. In addition, existing metrics can not reflect the quality of generated designs well, and the diversity of the generated designs depends on both the model input and structure. Our study not only enhances the assessment of generated floor plans but also aids architects in utilizing numerous generated designs effectively. Full article

As urbanization accelerates, the increasing density of urban buildings and the prevalence of multi-unit high-rise residential areas have emerged as significant factors affecting residents’ comfort. Effective green space planning within residential areas can mitigate residents’ thermal discomfort. This study utilizes methods including the construction of two-dimensional and three-dimensional landscape indices and meteorological data simulation to examine the relationship between residents’ comfort levels at various heights in residential buildings and the 3D landscape patterns of residential areas, based on semantic three-dimensional grid data from a residential complex in Wuhan. The results indicate that (1) The characteristics of 3D landscape patterns vary across different regions within multi-unit high-rise residential areas. The landscape patches in the central and southern regions are more balanced compared to other areas, while there is minimal height variation in residential buildings in the northeastern region. (2) There are notable differences in comfort levels at varying heights across different areas of the residential district. In summer, residents expressing satisfaction with environmental comfort are primarily located in high-rise buildings in the central-southern region, whereas in winter, satisfaction is concentrated among residents in lower and mid-rise buildings in both the northern center and southern areas. (3) The degree of landscape fragmentation, the dominance of certain patches, and the distribution of buildings and vegetation at different heights significantly influence residents’ comfort. Achieving a balanced distribution of green spaces, reducing building density, and ensuring a uniform arrangement of trees of varied heights can effectively enhance the living environment for residents on lower floors, providing practical strategies for the planning of green spaces and built environments that improve overall resident quality of life. This research provides a theoretical foundation and reference for evaluating thermal comfort in high-rise residential areas and optimizing green space configurations. Full article

Traditional Japanese architecture is known for its open, ambiguous spatial boundaries (“kyokai”), which integrate nature and dwelling through Zen/Shinto philosophies. Yet modern urban housing, driven by high-density minimalism, flattens spatial hierarchies and erodes these rich boundary concepts. This study aims to explore how Japanese architect Yoshiji Takehara reinterprets traditional spatial principles to reconstruct the interior–exterior relationships in modern housing through a mixed-methods approach—including a literature review, case studies, and semi-structured interviews—verifying the hypothesis that he achieves the modern translation of traditional “kyokai” through strategies of boundary expansion and ambiguity. Analyzing 78 independent residential projects by Takehara and incorporating his interview texts, the research employs spatial typology and statistical methods to quantify the characteristics of boundary configurations, such as building contour morphology, opening orientations, and transitional space types, to reveal the internal logic of his design strategies. This study identifies two core strategies through which Takehara redefines spatial boundaries: firstly, clustered building layouts, multi-directional openings, and visual connections between courtyards and private functional spaces extend interface areas, enhancing interactions between nature and daily life; secondly, in-between spaces like corridors and doma (earthen-floored transitional zones), double-layered fixtures, and floor-level variations blur physical and psychological boundaries, creating multilayered permeability. Case studies demonstrate that his designs not only inherit traditional elements such as indented plans and semi-outdoor buffers but also revitalize the essence of “dwelling” through contemporary expressions, achieving dynamic visual experiences and poetic inhabitation within limited sites via complex boundary configurations and fluid thresholds. This research provides reusable boundary design strategies for high-density urban housing, such as multi-directional openings and buffer space typologies, and fills a research gap in the systematic translation of traditional “kyokai” theory into modern architecture, offering new insights for reconstructing the natural connection in residential spaces. Full article

Disentangling the effects of the built environment on urban vitality at the scale of community life circles is crucial for informing precise urban planning and design, particularly in the context of urban renewal. However, studies examining the complex relationships and spatial heterogeneity in these effects remain limited, hindering the identification of built environment characteristics that may generate sustainable benefits. Therefore, this study took Xi’an, a typical high-density city in Northwest China, as an example. The eXtreme Gradient Boosting (XGBoost) model and the SHapley Additive exPlanations (SHAP) method were utilized to reveal threshold effects and spatial correlations between the built environment and community life circles’ vitality across varying buffer zones. The results show that (1) there is a significant spatial correlation between the built environment and the core–periphery structure of community life circles’ vitality. (2) Indicators, such as facility accessibility, the floor area ratio, intersection density, and the residential land use ratio, contribute significantly to community life circles’ vitality. (3) While the micro-built environment and socio-economic factors show limited contributions, their collaboration with the macro-built environment can enhance their individual effects, highlighting the necessity of taking them into account together. These findings provide new insights into supporting community life circles’ vitality through urban planning and design. Full article

The construction of residential buildings and structures is a complex process in which the economic component plays a key role. It is essential to maintain a balance between saving construction materials and the costs of additional engineering solutions while ensuring the functionality and comfort of the building’s operation. To achieve this goal, researchers initially analyze the impact of the climatic environment and spatial planning solutions—i.e., building shapes—that directly affect the building compactness ratio when evaluating the efficiency of the designed building. In this regard, the objective of this study was to analyze the shapes and orientations of buildings in the Republic of Kazakhstan across eight territorial units located in the I, III, and IV climatic zones between latitudes 42°18′ and 52°16′ N. The study identified the most favorable building orientations for each climatic zone: the meridional orientation is preferable for the I and III zones, while the latitudinal orientation is optimal for the IV zone. Four residential building shapes—square, rectangular, cylindrical, and triangular—were analyzed based on a floor area of 1000 m² and a building volume of 3000 m³ during the coldest five-day period and the hottest month. According to the specific thermal characteristic values, it was found that a cylindrical residential building is 1.1, 1.37, and 1.27 times more efficient than square, rectangular, and triangular residential buildings, respectively. Additionally, the compactness ratio was determined for different residential building shapes and heights, ranging from 8 to 16 floors in increments of four floors. The results showed that under these conditions, the compactness ratio increases by an average of 1.3 times due to the increase in the area of external walls. However, if the initial condition is changed to account solely for the floor area, the compactness ratio decreases by up to 2.3 times. The conducted research shows that when solving the problem of the energy efficiency of a building, taking into account shapes and orientations, it is necessary to carry out a full assessment of the specified energy efficiency parameter depending on the expected results, which requires a comprehensive analysis to achieve energy-efficient buildings. At the same time, the results of this study will be used and will positively complement the results of a comprehensive study by the authors on the development of energy-efficient exterior enclosing structures, which, together with general solutions, will significantly affect the thermal balance of the building and complement the research conducted earlier. Full article

Bubble diagrams containing key features and information are used for generative design of floor plans. The lack of reliable methods for automatically generating bubble diagrams significantly affects the smoothness of layout generation systems. To improve the time-consuming and unstable acquisition process, a novel method based on graph neural networks (GNNs) is proposed to generate various residential bubble diagrams. First, a dual-branch graph neural network (DBGNN) is introduced to learn the feature patterns of heterogeneous links, including connectivity and adjacency relations. Then, decentralized node sampling (DNS) and centralized node sampling (CNS) are proposed to enhance the local feature learning of DBGNN. Subsequently, a variational graph autoencoder (VGAE) is used to learn the implicit distribution of topological patterns, enabling the model to generate diverse outputs. Experimental results show that the proposed model performs excellently in two link prediction tasks, achieving 92.39% ACC-Door and 78.84% ACC-Wall, while also generating 50 distinct bubble diagrams, validating the effectiveness of the proposed method and demonstrating its outstanding application value. Full article

Most of Italy’s residential building stock predates contemporary structural safety and energy efficiency regulatory frameworks. Today, policymakers face the challenge of choosing whether to prioritise renovation or opt for demolition and reconstruction; both options carry significant socio-economic and environmental consequences and require extensive knowledge of the built heritage. However, detailed architecture-specific data remain scarce, as existing databases lack granular information. Moreover, traditional urban-level knowledge mapping approaches may be resource-intensive. To address this data gap, this study proposes a semi-automated methodology for generating graph-based digital models representing residential building floor plans. Using graph theory, floor spatial layouts are mapped into connectivity graphs and transformed into topological models. These models are enriched with functional data about spaces by assigning conditional topological rules based on node centrality metrics. The method was tested on 98 buildings in Bologna, Italy, yielding an 89.8% success rate and demonstrating its effectiveness in data-limited contexts. The resulting dataset facilitates the analysis of floor spatial configurations and the extraction of geometric attributes, laying the foundation for future analyses that will integrate machine learning techniques for functional detection and typological clustering. Full article

The automatic identification and three-dimensional reconstruction of house plans has emerged as a significant research direction in intelligent building and smart city applications. Three-dimensional models reconstructed from two-dimensional floor plans provide more intuitive visualization for building safety assessments and spatial suitability evaluations. To address the limitations of existing public datasets—including low quality, inaccurate annotations, and poor alignment with residential architecture characteristics—this study constructs a high-quality vector dataset of raster house plans. We collected and meticulously annotated over 5000 high-quality floor plans representative of urban housing typologies, covering the majority of common residential layouts in the region. For architectural element recognition, we propose a key point-based detection approach for walls, doors, windows, and scale indicators. To improve wall localization accuracy, we introduce CPN-Floor, a method that achieves precise key point detection of house plan primitives. By generating and filtering candidate primitives through axial alignment rules and geometric constraints, followed by post-processing to refine the positions of walls, doors, and windows, our approach achieves over 87% precision and 88% recall, with positional errors within 1% of the floor plan’s dimensions. Scale recognition combines YOLOv8 with Shi–Tomasi corner detection to identify measurement endpoints, while leveraging the pre-trained multimodal OFA-OCR model for digital character recognition. This integrated solution achieves scale calculation accuracy exceeding 95%. We design and implement a house model recognition and 3D reconstruction system based on the WebGL framework and use the front-end MVC design pattern to interact with the data and views of the house model. We also develop a high-performance house model recognition and reconstruction system to support the rendering of reconstructed walls, doors, and windows; user interaction with the reconstructed house model; and the history of the house model operations, such as forward and backward functions. Full article

With the acceleration of urbanisation and the diversification of residents’ travel needs, rail transit plays a critical role in mitigating traffic congestion. However, existing studies predominantly rely on linear models, neglecting the nonlinear effects and spatial heterogeneity of built environment factors on ridership. To address this gap, this study integrates the Multiscale Geographically Weighted Regression (MGWR) model and the Gradient Boosting Decision Tree (GBDT) model to analyse the impact of built environment factors on total, inbound, and outbound ridership in Shenzhen. Utilising Automatic Fare Collection (AFC) data and multiple built environment variables, we identify six key factors (office type, accessibility, road network density, floor area ratio (FAR), public services, and residential type) through SHapley Additive exPlanations (SHAP) value and partial dependency plot (PDP) analysis. Notably, this study constructs a three-dimensional PDP to explore the linkage effects of building volume ratio and accessibility, revealing their joint influence on ridership. The results demonstrate that the GBDT model outperforms MGWR in handling high-dimensional nonlinear data. This paper provides policy recommendations for transport authorities, highlighting the synergies between optimising the planning of the built environment and the development of rail transport to improve the efficiency of short-distance commuting while supporting long-distance cross-city travel. Full article