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Many concerns related to natural ventilation in urban areas have been deduced from experimental or computational fluid dynamics simulations on idealised models. However, it is not definite that the flow through these idealised models presents similar characteristics to actual urban areas. The objective of this research is to suggest an approach to close the gap between idealised models and genuine cities; i.e., predict actual urban flow characteristics from the ready data of idealised models. The flow was simulated by large-eddy simulation through both the actual city model and a group of idealised models of different structures but the same average dimensions and buildingpacking-density as the actual city. The numerical setup was validated by comparison with wind tunnel measurements from the literature. It was found that an equivalent to the average velocity profile throughout an idealised model can be achieved by a mix of the “five-point spatial average” and the “four-point spatial average”. The vertical profiles of mean and turbulent windward velocities of the idealised models manifest a general similarity to those of the actual model. On the other hand, the cross-wind and wall-normal components show large discrepancies. In all cases, the idealised models exhibit very narrow atmospheric surface layer heights compared to the actual model. IM-RAN (which represents a structure of semi-random configuration) displayed the closest results to the actual model but condensed in half the actual model surface layer height. A correction formula was devised to close the gap between the two models. The results confirm the ability to utilise idealised models to deliver recommendations regarding urban environment planning; though, attention should be paid to the selection of the idealised model and corrections may be needed.

\"Every time you wheel a shopping cart through one of Walmart's more than 10,000 stores worldwide, or swipe your credit card or purchase something online, you enter a mind-boggling logistical regime. Even if you've never shopped at Walmart, its logistics have probably affected your life.The Rule of Logistics makes sense of its spatial and architectural ramifications by analyzing the stores,distribution centers, databases, and inventory practices of theworld's largest corporation. The Rule of Logistics tells the story of Walmart's buildings in the context of the corporation's entire operation, itself characterized by an obsession with logistics. Beginning with the company's founding in 1962, Jesse LeCavalier reveals how logistics--as a branch of knowledge, an area of work, and a collection of processes--takes shape and changes our built environment. Weaving together archival material with original drawings, LeCavalier shows how a diverse array of ideas, people, and things--military theory and chewing gum, Howard Dean and satellite networks, Hudson River School painters and real estate software, to name a few--are all connected through Walmart's logistical operations and in turn are transforming how its buildings are conceptualized, located, built, and inhabited. A major new contribution to architectural history and theory, The Rule of Logistics helps us understand how retailing today is changing our bodies, brains, buildings, and cities and predicts what future forms architecture might take when shaped by systems that exceed its current capacities\"-- Provided by publisher.

Adult hippocampal neurogenesis (AHN) is associated with cognitive reserve and mental health, and can be supported by environmental enrichment, such as spatial complexity, as a form of cognitive enrichment. Studies on human subjects show that navigating complex landscapes and higher geospatial complexity at a zipcode level are associated with greater hippocampal volume and lower risk of Alzheimer's disease, while laboratory studies show that spatial complexity can enhance AHN in mice and rats. However, the role of architectural spatial complexity remains unexplored, despite people spending ∼90% of their time indoors, which can pose a risk to AHN. To address this gap, first, this study developed a novel tool, the Architectural Spatial Complexity Index (A-SCI). The A-SCI was calculated based on seven layout variables and seven points of interest to maintain a balanced ratio, and the index scores were normalised to range from 0 to 1. Second, this study theoretically validated the A-SCI's ability to differentiate between enriched housing models that enhance AHN and non-enriched laboratory housing. It showed that enriched environments, using layout complexity, points of interest, or both, scored significantly higher than non-enriched animal housing models, with no difference between enrichment subtypes (layout complexity versus points of interest), demonstrating the theoretical validity of the A-SCI tool. Third, floor area did not independently predict the index scores after controlling for complexity. Fourth, exploratory assessments of human layouts demonstrate that multi-storey houses outscored single-storey apartments. Adding landscape and museum mazes yielded index scores comparable to those of multi-storey houses, both higher than those of apartments. Lastly, using an expanded dataset (n = 63), historical palaces and contemporary shopping malls scored relatively higher, while hotel apartments and student accommodation scored similarly to apartments. Points of interest, which may provide cognitive enrichment independent of layout complexity, were not assessed in this study due to their dependence on social and economic factors that require separate investigation. The A-SCI tool offers a neuroscience-informed approach to architectural enrichment by design, which can support AHN in humans and help predict the risk of cognitive decline and mental health conditions such as depression. This paper encourages the use of the A-SCI tool for future research in architecture and neuroscience. Applications can span public health, space syntax, historical, and socioeconomic perspectives.

Urban flooding has intensified, with the rapid proliferation of urban buildings recognized as a significant contributing factor in recent years. This study aims to assess the impact of building layout on urban flooding under extreme conditions, a factor historically underestimated during the early stages of urban planning. Using an advanced K-Means-XGBoost hybrid model, this research investigates the interaction between building layouts and localized flooding incidents in urban environments. The findings indicate that dense and compact building designs offer superior resistance to water accumulation in areas characterized by gentle topography, with elongated structures demonstrating even greater efficacy. This configuration results in a significant reduction in overall water depth, resulting in a 29.42% decrease. Low-density and larger buildings exhibit enhanced flood resistance in regions with pronounced surface undulations, particularly those with square-like shapes. In these scenarios, overall water depth is also minimized, with an 18.97% decrease. A decrease in water depth in one area may exacerbate flooding in adjacent regions. Future planning should consider the increased risk of urban flooding in neighboring areas due to water depth decline in a single region. This study provides valuable insights for urban planning and presents a strategic framework for other cities to mitigate flooding risks.

The application of machine learning (ML) modelling in daylight prediction has been a promising approach for reliable and effective visual comfort assessment. Although many advancements have been made, no standardized ML modelling framework exists in daylight assessment. In this study, 625 different building layouts were generated to model useful daylight illuminance (UDI). Two state-of-the-art ML algorithms, eXtreme Gradient Boosting (XGBoost) and random forest (RF), were employed to analyze UDI in four categories: UDI- f (fell short), UDI- s (supplementary), UDI- a (autonomous), and UDI- e (exceeded). A feature (internal finish) was introduced to the framework to better reflect real-world representation. The results show that XGBoost models predict UDI with a maximum accuracy of R 2 = 0.992. Compared to RF, the XGBoost ML models can significantly reduce prediction errors. Future research directions have been specified to advance the proposed framework by introducing new features and exploring new ML architectures to standardize ML applications in daylight prediction.

Urban residential building layouts have an impact on air temperature and thermal comfort. Research has shown that poorly designed building layouts can lead to thermal discomfort. Thus, it is crucial to analyze the relationship between residential building layouts and air temperature. We used the ENVI-met 3D microclimate model to simulate six typical residential building layouts and explore the diurnal and seasonal variations in air temperature. In addition, we used the physiological equivalent temperature (PET) as the evaluation index for the thermal comfort of different building layouts. The diurnal results showed that the air temperature of the parallel layout rose faster and fell faster, and these changes were more significant in summer. The results of the air temperature classifications indicated that the frequency of low-air-temperature areas in the parallel layout is approximately 12% smaller than that of the enclosed and semi-enclosed layouts, and the high-air-temperature area frequency is 11% higher than that of the enclosed and semi-enclosed layouts in summer. In winter, the frequency of low-air-temperature areas in the parallel layout is approximately 7% smaller than that of the enclosed and semi-enclosed layouts, and the high-air-temperature area frequency is 5% higher than that of the enclosed and semi-enclosed layouts. In combination with the PET results, we found that the enclosed layout is the optimal configuration. Moreover, in some cases, increased building height and vegetation lead to a reduction in air temperature.

Generating realistic building layouts for automatic building design has been studied in both computer vision and architectural domains. Traditional approaches in the latter, which are based on optimization techniques or heuristic design guidelines, can synthesize desirable layouts, but usually require post-processing and involve human interaction in the design pipeline, making them costly and time-consuming. The advent of deep generative models has significantly improved the fidelity and diversity of the generated architecture layouts, reducing the workload of designers and making the process much more efficient. This paper presents a comprehensive review of three major research topics in architectural layout design and generation: floorplan layout generation, scene layout synthesis, and generation of various other formats of building layouts. For each topic, we overview the leading paradigms, categorized either by research domains (architecture or machine learning) or by user input conditions or constraints. We then introduce commonly-adopted benchmark datasets used to verify the effectiveness of the methods, as well as corresponding evaluation metrics. Finally, we identify the well-solved problems and limitations of existing approaches, and then propose promising directions for future research. This survey has an associated project which aims to maintain the resources, at https://github.com/jcliu0428/awesome-building-layout-generation.

Spatial cognition is a pivotal consideration in hospital buildings. Potential circulation configuration alternatives in hospitals have been filtered to 59 significant alternatives, to be simulated using space syntax analyses for measuring connectivity, visual integration, and visual step depth. Best alternatives have been determined via comparison and correlation analysis. A survey has been conducted, comparing the preferences of professional architects with simulation outcomes. Results show that best and worst alternatives exist in triangular and circular buildings, respectively. Also, cul-de-sac corridors are argued to promote spatial cognition better than looped corridors. Notably, cognition is inversely proportional to the curvature of the circulation spaces, number of nodes, and the number of branches. The survey showed a weak perception of the best alternatives, while respondents have a better perception of highly visible locations in these alternatives.

Asuka village in Nara prefecture has been designated as one of the special preservation areas for historic landscape since 1960s and is well‐known for its richness in historic remains originated from Asuka period. This paper focuses on rural house compounds from six different village sections. Village sections located within the plain area are classified into three groups in terms of village layout, i.e., group 1: clustered village (Okuyama, Kawahara and Noguchi), group 2: street village along the east–west axis (Asuka), group 3: street village along the north–south axis (Oka and Shimasho). The composition of rural house compounds in Asuka village and Nara basin consists of a main building, extensions directly attached to the main building ( tsunoya ) and outbuildings (huzokuya, hanare). We first set up the hypothetical model for the building layout development pattern of house compounds which includes every possible extendable pattern of the main building, extensions and outbuildings. The model was examined by the result of our field research which revealed the fact that dominant development patterns follow the basic rule of traditional Japanese idea of the divination of the auspices of a house regarding its position and orientation.