Building Clusters Research Articles

Solar Species: Energy Optimization of Urban Form Through an Evolutionary Design Process

This paper proposes design guidelines to enhance energy efficiency and energy generation potential in active solar buildings. Additionally, it presents a variety of optimized urban forms characterized by attributes such as shape, layout, and number of buildings on the plot. These urban configurations are classified into solar species, each associated with a distinct range of high passive and active solar potential. These results were achieved by developing and applying a simulation-driven, multi-objective optimization technique for the early-stage design of a residential building cluster in a temperate climate. This method leverages both passive and active energy indicators, employing a genetic algorithm to identify optimal forms that maximize active solar potential while also minimizing operational energy demand. The approach utilizes a parametric modelling routine that relies on vertical cores and horizontal connections to produce design iterations featuring irregular geometry, while ensuring structural continuity and means of egress. The findings reveal a significant variability in onsite energy generation, with optimized solutions differing by a factor of 2.5 solely based on shape, underscoring the critical role of active solar potential. Taken together, these results hint at the descriptive and predictive capabilities of these solar species, making them a promising heuristic model for characterizing urban form in relation to energy performance.

The site-city interaction effect uncertainty on structural responses and its application to fragility analysis of buildings in Shanghai CBD

Seismic fragility analysis is a crucial tool for assessing the seismic performance of buildings. In areas with dense clusters of tall buildings, the significant site-city interaction (SCI) effect alters wave propagation mechanisms, influencing the seismic fragility of structures. However, a significant increase in computational workload results from the need for detailed modeling of sites and building clusters for the SCI analysis. To address this challenge, this work first investigates the minimum number of earthquake waves required to characterize SCI-induced response changes. The Central Business District of Shanghai is analyzed. A table for the recommended minimum number for a given accuracy requirement and prediction reliability is provided. Moreover, a seismic fragility analysis method considering the SCI effect is proposed for low-rise buildings. The case study indicates that, buildings with similar height will exhibit various fragility changes after considering SCI. For the complete damage state, the mean intensity value of the fragility curve can be 14.4 % smaller than that without SCI. In addition, this approach provides significant computational workload reduction. For the case study, the computational workload of the proposed method is roughly 1/50 of that using traditional IDA method.

Research on a Multi-Scale Clustering Method for Buildings Taking into Account Visual Cognition

Building clustering is a key problem that needs to be solved in the realization of the automatic synthesis of large-scale maps. The selection of different feature and spatial distance calculation methods has a great impact on the clustering results, and the need to manually select appropriate feature and distance metrics leads to the problem of not being able to fully consider the complexity and diversity of buildings. In this paper, we propose a multi-scale clustering method for buildings that takes visual perception into account using the Gestalt principle to simulate how humans classify buildings through visual perception. Moreover, by analyzing the spatial features and texture attributes of buildings, a visual distance is designed to be used as a condition for building classification to assess the similarity between buildings, solving the complexity of manually selecting feature vectors and spatial distances and realizing the adaptive selection of features. Through experimental validation at different scales (macro, meso and micro), the present method is able to achieve the accurate clustering of buildings, and a frequency threshold of 91% is found, which is able to determine the optimal clustering results. The experimental results show that the proposed method can not only fully consider the complexity and diversity of buildings but also effectively support the understanding and analysis of urban spatial structure and provide a scientific decision-making basis for urban planning and management.

Analysis of the Impact of Funding Policies for the Energy Refurbishment of Buildings Using Dynamic Simulations

Dynamic simulations can accurately estimate the thermal demands for space heating and cooling in buildings, as well as the energy and economic performance of specific energy refurbishment actions. This study aims to evaluate the energy and economic savings resulting from the adoption of particular energy measures applied to a cluster of residential condominium buildings, also considering some possible Italian funding policies. To this scope, dynamic simulation models of several buildings with different features in terms of geometry, shape, and thermo-physical properties are considered. The selected buildings are representative of the most common buildings in the city of Naples, Southern Italy. Two scenarios regarding the possible penetration of the refurbishment actions are considered: the “25% scenario”, where 25% of buildings in the Naples municipality adopt the selected measures, and the “100% scenario”, where all buildings adopt such energy refurbishment actions. The results of the simulations, reported over different time periods, compare the economic, energy, and environmental benefits of the specific energy measures. This study evaluates the replacement of conventional natural gas-fired boilers with natural gas-fired condensing boilers, as well as the use of thermal insulation on the external walls of the buildings. The primary energy demand for space heating decreased by 28% when the proposed energy measures were implemented in all buildings of the Naples municipality.

Coordinated price-based control of modulating heat pumps for practical demand response and peak shaving in building clusters

With a high share of renewable energy in the power grid, it becomes increasingly difficult to ensure a continuous balance between power generation and consumption, thereby endangering grid stability. A substantial opportunity to address this challenge and align the heating demand with intermittent power production is offered by space heating and domestic hot water, which account for 80% of the energy consumption in buildings. Further research on the control of building clusters is required, where peak load management of multiple buildings can ensure grid stability during peak hours and contribute to avoiding power outages. In this paper, a rule-based controller is presented, called Extended Price Storage Control+ (EPSC+), for the practical and flexible operation of heating systems in a building cluster. Under dynamic pricing, the loads of electric heating devices for the provision of space heating and domestic hot water are shifted by EPSC+ while accounting for peak load constraints. The performance of EPSC+ is evaluated in a nine-week winter simulation study with a building cluster of ten buildings in Germany. For comparison, a hierarchical model predictive controller (MPC) and a hysteresis two-point controller are employed as benchmarks. Results close to those of MPC are achieved by EPSC+by reducing the median peak load by 38.8% and median electricity costs by 15% compared with the hysteresis controller. In contrast to MPC, EPSC+ does not require models, forecasts, or optimization and is computationally inexpensive, rendering it more attainable for real-world implementation.

Earthquake Resilience of Spatially Distributed Building Clusters: Methodology and Application

Earthquake Resilience of Spatially Distributed Building Clusters: Methodology and Application

Effect of urban morphology on microclimate and building cluster energy consumption in cold regions of China

Building energy consumption (BEC) is influenced by the combination of urban morphology and microclimate. This study investigated the influence of urban neighborhood morphology on microclimate and building energy consumption through sixty representative street blocks in cold regions of China. The following conclusions were drawn from the multiple regression analysis, and random forest (RF) in this research: 1) The impact of urban morphology on microclimate and vice versa on energy use intensity (EUI) varies according to the season. 2) Annual temperature (TEMP.), solar radiation (SR), building site cover (BSC), average building height (BH), road area ratio (RAR), and block orientation (BO) were significantly positively correlated with EUI, and the floor area ratio (FAR) was negatively correlated. 3) The FAR is positively correlated with BEC and has a significant negative correlation with relative humidity (RH) which contribution rates are 82.3 % and 7.3 %, respectively. Based on the analysis, the research proposed feasible recommendations to optimize the layout of blocks. The results of this study can be used to predict the energy consumption of new and existing street blocks in cold-climate cities quickly and accurately. Additionally, it provides quantitative strategies for energy-saving and emission reduction in urban planning.

Seismic vulnerability analysis of hospitals and school buildings considering the Gaussian regression algorithm

This study combines the Gaussian regression algorithm to propose a nonlinear regression model that can be used to evaluate the seismic vulnerability of regional hospitals and school buildings. The failure features and disaster mechanisms of hospital and school buildings affected by the Jishishan (JSS) earthquake on December 18, 2023, and the Wenchuan (WC) earthquake on May 12, 2008, were reported. The samples of 252 damaged buildings (37 hospitals and 215 schools) in Dujiangyan city affected by the Wenchuan earthquake for which the author participated in the survey were collected and counted. Hospitals and school buildings were classified according to the types of reinforced concrete (RC) and masonry structures, and earthquake vulnerability probability matrices for hospital and school building clusters were established considering the actual failure probabilities. A total of 2103,213 acceleration records (from the Wenchuan earthquake) monitored by 12 real stations were selected. Using dynamic time history and response spectrum analysis methods, time history and spectrum curves were generated, considering the influence of different seismic directions. Using the developed Gaussian regression model, vulnerability comparison curves and parameter matrices considering multiple regression algorithms were generated. This paper considers the effects of the construction year and number of floors on the seismic vulnerability of hospital and school buildings. Seismic vulnerability curves and failure probability matrices were generated and established on the basis of actual structural failure probabilities. According to regression and vulnerability surface analysis, the evaluation results of the proposed model are highly consistent with actual field observations.

Static analysis-based rapid fire-following earthquake risk assessment method using simple building and GIS information

After the occurrences of large-scale earthquakes, secondary damage (e.g., fire following earthquake) can result in tremendous losses of life, properties, and buildings. To reduce these disaster risks, fire following earthquake assessment methods composed of ignition and fire-burned rate estimation models have been utilized. However, previous methods required for large amounts of building and GIS information, and complex modeling and analysis processes, leading to significant time consumption. This paper proposed a static analysis-based rapid fire following earthquake assessment method using simple information and implemented it in Pohang City, South Korea. Based on previous studies, the best-fit model for the ignition rate estimation was selected, and a cluster-based fire-burned rate estimation model was developed using simple building information (e.g., construction year, building occupancy, story, and total floor area) from the public building database (e.g., building registration data). For the fire-burned rate estimation model, fire-resistant structure types were defined using simple building information, and this was utilized to generate clusters of buildings at a regional level by comparing fire-spread distances for each fire-resistant structure type with adjacent distances among the buildings. This proposed method was applied to Pohang City, South Korea, and validated as follows: (1) the selected ignition rate model predicted similar ignition numbers to the actual reported number (actual number of ignitions = 4 vs. predicted number of ignitions = 3), and (2) the fire-burned rate model estimated fire-burned areas with a marginal difference compared to the fire spread simulation (fire-burned area using the proposed model = 13,703.6 m2 vs. results of fire spread simulation = 16,800.0 m2, with an error of approximately 18%).

Daylighting performance prediction model for linear layouts of teaching building clusters utilizing deep learning

Deep learning (DL) has proven to be an effective tool for predicting the daylighting performance of buildings on individual rooms or standalone buildings by utilizing a few straightforward design parameters as input variables for analysis. In addition to existing studies, exploring methods to characterize larger objects with spatial relationships may contribute to understanding the impact of layout on the overall daylighting performance of buildings. In this study, a DL model based on the framework of “Autoencoder-Based Feature Extraction with Artificial Neural Network (AE-ANN)” has been developed to predict the daylighting performance of the layout of teaching building clusters. In order to efficiently extract the layout characteristics and improve the model's generalization capabilities, an autoencoder (AE) was pre-trained to encode the planar layout images of teaching building clusters into feature vectors, which were then employed for training an ANN model. In the testing dataset, the AE-ANN model demonstrated impressive accuracy, achieving R² values of 0.946 for sDA and 0.853 for ASE, alongside MSE values of 0.312 and 0.656. This research investigated the feasibility of the AE-based model for predicting daylighting performance of large-scale scenarios, highlighting its potential as a fundamental model for the development of more intricate daylighting prediction models.

Carbon Emission Analysis of Low-Carbon Technology Coupled with a Regional Integrated Energy System Considering Carbon-Peaking Targets

Analyzing the carbon emission behavior of a regional integrated energy system (RIES) is crucial for aligning with carbon-peaking development strategies and ensuring compliance with carbon-peaking implementation pathways. This study focuses on a building cluster area in Shanghai, China, aiming to provide a comprehensive analysis from both macro and micro perspectives. From a macro viewpoint, an extended STIRPAT model, incorporating the environmental Kuznets curve, is proposed to predict the carbon-peaking trajectory in Shanghai. This approach yields carbon-peaking implementation pathways for three scenarios: rapid development, stable development, and green development, spanning the period of 2020–2040. At a micro scale, three distinct RIES system configurations—fossil, hybrid, and clean—are formulated based on the renewable energy penetration level. Utilizing a multi-objective optimization model, this study explores the carbon emission behavior of a RIES while adhering to carbon-peaking constraints. Four scenarios of carbon emission reduction policies are implemented, leveraging green certificates and carbon-trading mechanisms. Performance indicators, including carbon emissions, carbon intensity, and marginal emission reduction cost, are employed to scrutinize the carbon emission behavior of the cross-regional integrated energy system within the confines of carbon peaking.

Sustainable Spatial Development of Multifunctional Villages: A Case Study of Eastern Poland

The decline in the role of agriculture as the basis for the livelihood of rural residents has led to the development of new directions for rural transformation. In Poland, the concept of multifunctional development has gained the most popularity. However, it does not have a defined spatial development model. There has also been no research into how the development of non-agricultural functions affects spatial development and to what extent this development is sustainable. Therefore, the aim of this study is to show and compare the changes that have taken place over the last 40–50 years in the spatial arrangements of development in traditional agricultural villages and in villages with different non-agricultural functions (tourist, industrial, and service functions). At the same time, we want to indicate which of these functions have contributed to the development of the most sustainable spatial arrangements. To this end, we selected three indicators of sustainable development of rural space: compactness of buildings, continuation of traditional rural layouts, and availability of services, and then carried out an analysis of changes in these indicators on the basis of archival and current cartographic materials and data on service facilities. We conducted the research for four municipalities in eastern Poland (50 villages). The results indicate the predominance of negative spatial phenomena such as the deterioration of the accessibility of services and spatial development contrary to historical spatial layouts. There is a spillover of development in the form of discontinuous, chaotic clusters of buildings often having the character of suburbia and, consequently, the disappearance of village centres, worsening walkability, and blurring of village boundaries. The only positive change is an increase in the compactness of buildings—mainly in villages that previously had a dispersed character. It is difficult to identify village functions that would unequivocally favour spatial sustainability. The service villages showed slight advantages in terms of social (availability of services) and environmental (compactness of buildings) factors. In contrast, the development of agricultural villages was more favourable in cultural terms (traditional village layouts). In all aspects, negative changes were recorded in industrial villages and (the worst results) in tourist villages. However, the trends were similar in all municipalities, which draws attention primarily to the lack of a rational spatial policy related to multifunctional village development.

Optimized seismic hazard and structural vulnerability model considering macroseismic intensity measures

Macroseismic intensity measures are fundamental quantitative indicators for evaluating the vulnerability and resilience of regional building clusters. The vulnerability model of building portfolios established can provide a noteworthy reference for the development of large-scale regional earthquake risk distributions. However, there are differences in the quantitative scales of the macroseismic intensity standards used in multiple regions. The positive effects of instrument intensity and probabilistic seismic hazard theory on the vulnerability assessment of regional buildings are rarely considered, resulting in significant differences in their assessment results. This paper comprehensively considers the impact of instrument intensity and seismic hazard models on traditional macroseismic intensity. An updated macroseismic intensity measure (UCMS-20) is proposed for evaluating regional buildings, which is combined with the latest version of China's seismic intensity standards. The actual seismic damage sample dataset (31,649 buildings) of four types of building clusters in three typical earthquakes in China (Wenchuan 2008, Yushu 2010, and Jiuzhaigou 2017) has been expanded and updated. Using the UCMS-20, the European Macroseismic Scale (EMS-98), the Modified Mercalli intensity (MMI), and the Medvedev, Sponheuer, and Karnik Scale (MSK-64), vulnerability assessments are conducted on the extended empirical structural seismic damage dataset, and an empirical disaster matrix model considering updating damage states is established. A nonlinear vulnerability prediction model considering updated macrointensity measures is proposed, and the optimized model is validated and compared against the developed multiscale empirical structure dataset. The multiscale vulnerability index calculation model is improved, and novel optimization curves, point clouds, and matrix probability risk models are established, considering the expansion of empirical structural vulnerability databases.

Multi‐view street view image fusion for city‐scale assessment of wind damage to building clusters

Multi‐view street view image fusion for city‐scale assessment of wind damage to building clusters

Hidden structural information reconstruction and seismic response analysis of high‐rise residential shear wall buildings with limited structural data

AbstractThe high‐rise residential shear wall structure is a crucial component of urban building clusters, while the limited availability of detailed structural information becomes a critical bottleneck in improving the accuracy of seismic performance assessment for high‐rise residential shear wall buildings in urban areas. Based on easily obtainable yet limited structural data at the urban scale, this paper proposes a method to address the shortcomings of existing research on reconstructing hidden structural information and enhance the accuracy of structural seismic performance assessment. It includes a physics‐constrained generative adversarial network module and a fuzzy inference system module to reconstruct the spatial arrangement of shear walls, and material strength grades within buildings, respectively. Validated against two actual buildings, the method outperforms the widely used simplified analysis method at the urban scale, achieving 85.9% accuracy in predicting damage states across various floors.

Structural nonlinear seismic time-history response prediction of urban-scale reinforced concrete frames based on deep learning

Efficiently predicting the seismic response of urban building clusters is essential for preemptively identifying potential seismic hazards prior to an earthquake and optimizing resource allocation post-event. However, complete information of buildings at a city scale is generally un-accessible or non-existent. Existing methods struggle to reconcile low information demands, high computational accuracy, and computational efficiency. This paper proposes a fast prediction method for structural seismic time-history responses that combines deep learning methods with easy-getting structural parameters at an urban scale. An end-to-end network with adaptive multilevel fusion output is designed, which incorporates the autoencoder concept for predicting the structural seismic time-history responses based on ground motions records and five easy-getting structural parameters. The models are compared and optimized considering the training hyperparameters and network architecture, resulting in an optimized model with low complexity that provides valuable reference values for structural seismic response. Besides, the proposed model is applied to four actual buildings with different construction time, occupancy types, and floor sizes, demonstrating its good prediction performance and significant computational advantages comparing to the universally used MDOF method.

Modeling the Role of Courtyards with Clusters of Buildings in Enhancing Sustainable Housing Designs

As urbanization increases, buildings require greater amounts of energy for heating and cooling, thereby necessitating the search for effective solutions. The courtyard is often considered a viable option; however, the limited availability and high cost of land resulting from rapid urbanization hinder its widespread use. Consequently, a courtyard with a cluster of buildings is proposed as a feasible solution to address land scarcity. Nonetheless, further investigation is required to effectively integrate this solution into neighborhood urban planning. This study examines the influence of three variables—courtyard orientation, courtyard size, and the arrangement of buildings around the courtyard—on the provision of cooling and heating for buildings. The research focuses on 216 experimental scenarios simulated using Revit software, which excels in its ability to accurately interpret input data and conduct real-time analysis depending on the variables of the building design. The results were recorded for the facades and ground, and the shaded area was computed for each scenario; following these measurements, the shadow areas on both the facade and ground were converted into percentages. The testing involved a group of buildings surrounding courtyards of four different shapes (square, rectangle, triangle, and circle). This approach aimed to identify the most efficient design for implementation in neighborhood planning contexts. The findings indicate that the shape of the courtyard significantly impacts cooling and heating of buildings. Specifically, the square courtyard is unsuitable for countries with Mediterranean climates, such as Jordan, as it can reduce shade coverage by 30%, leading to higher temperatures. Conversely, employing a rectangular courtyard results in a higher proportion of shadows compared to other shapes. The study further demonstrates the influence of the examined variables on the efficacy of the courtyard in cooling and heating of buildings.

Living together but gates apart: aspirations and place caring practices among residents living outside a gated community in Peri-urban Accra.

Global capital investment in the housing sector has transformed major African cities such as Accra into segregated clusters of high-rise buildings and ultramodern residential areas. The development of housing projects, such as Gated Communities (GCs), in peri-urban areas has resulted in a contrasting phenomenon of residents living in traditional housing units alongside modern housing complexes. This paper maps the impacts of Ayi Mensah Park, a privately built gated community in peri-urban Accra, Ghana. Specifically, it explores the perceptions, emotions and attitudes of residents living in the immediate environs of this gated community and how these opinions shape their life aspirations, particularly regarding housing. Using semistructured interviews and drawing on the concepts of ‘peripheral urbanisation’ and ‘care’, we found that residents express varied emotions (happiness, excitement, admiration, pride, disappointment, anger, apathy, and frustration) towards the presence of the gated community. Some of these emotions resulted in action, such as pulling down old buildings and constructing new modern apartments in their places. We conclude that the presence and aesthetic characteristics of gated communities in peri-urban areas invoke contrasting affective elements of both aspirations and despair among residents, resulting in new practices of care for the community and future generations.

Wind-induced interference effect of complex building clusters on long-span roof structures

The presence of complex buildings introduces interference effects and complicates the wind field around the long-span roof structure. The complexity of the problem makes it challenging to predict wind load of long-span roof structures, resulting in a scarcity of design data available to engineers and designers. Thus, the influence of interference effect on mean and peak pressure coefficients of a long-span tri-centered cylindrical roof structure was investigated through wind tunnel tests conducted in an atmospheric boundary layer wind tunnel. The study focused on the interference effects caused by two cooling towers, chimney, and some low-rise buildings, considering all wind directions. The results show that the interference effects are the result of a combination of amplification effects from the adjacent cooling towers and chimney and shielding effects from the surrounding low-rise buildings. Due to the shielding effect of low-rise buildings, the wind suction on the roof is mitigated compared to that of a single building, while at the end of the roof, wind suction is amplified by cooling tower effects. The interference effect of the building clusters, however, amplifies the fluctuating wind pressure coefficient and the peak pressure coefficient on the roof. To accurately estimate the wind load of building components and envelopes, furthermore, this study investigates the influence mechanism of interference effect on non-Gaussian characteristics of wind pressure combining with fluctuating wind pressure spectrum, spatial correlation of fluctuating wind pressure, and probability distribution characteristics of standardized wind pressure coefficient. The spatial correlation of wind pressure in the roof interference area exhibits a strong association, and the probability density distribution characteristics of the standardized wind pressure coefficient significantly deviate from the Gaussian curve, with a peak factor exceeding 3.5. Thus, it can be concluded that the wind pressure within this region demonstrates significant non-Gaussian characteristics. Hence, the zone values of peak pressure coefficients are determined using the peak factor approach to inform the wind resistance design of the envelope structure.

Urban micro-scale street thermal comfort prediction using a ‘graph attention network’ model

Outdoor thermal comfort (OTC) directly affects human behavior and building operations. It is also a key factor in the achievement of smart living. When modeling OTC, existing studies tend to reduce the computational burden by conceptualizing urban spatial elements (e.g., buildings, streets) as static entities without considering the intricate dynamics of their synergistic influence. This research presents an innovative network-based framework for urban micro-scale street OTC prediction. The proposed graph attention network (GAT), in conjunction with building energy modeling (BEM), treating building clusters as graph nodes and streets as edges, capturing the interrelations between urban spatial elements, and modeling the street's universal thermal climate index (UTCI) at different time periods. The GAT model is trained and tested using simulated data from representative high-density residential areas in Hong Kong. Its performance is evaluated against an artificial neural network (ANN) model that disregards interrelations among urban spatial elements. Results demonstrate that, compared to the ANN model, the GAT model achieves an improvement in overall mean absolute error (MAE) of 37.5 %, root mean square error (RMSE) of 36.07 %, and correlation coefficient (r) of 10.58 %. Furthermore, the GAT model can better predict situations with significant amplitude changes and rapid frequency variations.