Urban Building Research Articles

An Integrated Seismic Assessment Method for Urban Buildings and Roads

AbstractSeismic simulation of urban buildings and roads is significant for regional pre-disaster mitigation and post-disaster recovery. To consider the interrelated influences of buildings and roads, an integrated seismic assessment method for urban buildings and roads is proposed. The seismic damages of buildings were assessed using various methods based on structural characteristics and different degrees of available building information. Both physical and topological characteristics of the road network are considered in the proposed method to determine post-earthquake road network traffic capacity. To quantitatively evaluate post-earthquake road network traffic capacity, we comprehensively considered the seismic damage to roads, blockages caused by earthquake-induced debris, and the potential risk of falling debris from damaged buildings. The proposed integrated seismic assessment method was applied to a real earthquake event to demonstrate its feasibility and effectiveness, and also applied to a real city, of which information on buildings and roads was based on open-source data and statistical data, to demonstrate its applicability. The proposed method provides a solid prediction on the seismic performance of urban buildings and road networks, serving as a reference for urban earthquake disaster rescue and relief.

Dynamic Scenario Simulation of Building Carbon Peak from the Perspective of Urbanization

With the rapid development of urbanization in China, the energy consumption and carbon emissions in the building sector will continue to grow. Therefore, the future dynamic evolution of building carbon emissions must be crucially investigated to achieve the "dual carbon" target in China. A system dynamics model of "urbanization-building carbon emissions" was constructed from the perspective of urbanization for revealing the mechanism of urbanization-related factors on building carbon emissions. On this basis, a dynamic simulation of the evolution trend and peak situation of building carbon emissions in the Beijing-Tianjin-Hebei （BTH） Region from 2000 to 2050 was carried out using multi-scenario analysis. The results showed that： ① Under the baseline scenario, the carbon emissions of rural residential buildings, urban residential buildings, and commercial buildings peak in 2027, 2028, and 2037, respectively, with peaks （in CO2） of 0.60×108, 0.99×108, and 1.95×108 tons. ② Under the low-carbon scenario, the peak （in CO2, the same below） time of carbon emissions of urban residential buildings was the most advanced, reaching the peak of 0.88×108 tons in 2024. ③ In the deep low-carbon scenario, the peak time of carbon emissions of the overall buildings occurred five years earlier than that in the low-carbon scenario. It was predicted to peak at 2.61×108 tons in 2028, reaching the 2030 carbon peak target. ④ The sensitivity analysis showed that the carbon emission factor of commercial buildings and the per capita commercial building space floor in the spatial dimension were the major factors influencing the building carbon emissions, followed by the percentage of the urban population in the population dimension. Finally, suggestions are made for the government of the Beijing-Tianjin-Hebei Region to carry out energy conservation and emission reduction planning.

Distributed Low-Carbon Energy Management of Urban Campus for Renewable Energy Consumption

In order to solve the mismatch between renewable energy and load in urban building microgrids, that is, the problem of renewable energy consumption in building microgrid clusters, while preserving the privacy of each user, this paper proposes a distributed low-carbon energy management method for urban building microgrid clusters. First, a low-carbon energy management method for the urban building microgrid is proposed in order to coordinate the power sharing of various subjects to minimize the total economic cost, unleash the consumption potential of low-carbon building clusters for renewable energy, and reduce carbon emissions on the spatial and time scale. Second, an ADMM-based distributed optimal energy management method is proposed to meet user energy needs while preserving local privacy; this includes energy storage systems, renewable energy generation, and the loads within each urban building microgrid. Finally, simulation experiments are conducted based on actual data from a certain area in Hangzhou, China, and the results verify the effectiveness of the model.

Advancing Heat Pump Adoption in Ukraine’s Low-Carbon Energy Transition

The European Union established a legislative framework to facilitate the transition to low-carbon energy sources. As Ukraine aspires to join the EU, it is progressively adopting similar legislation. The extensive damage to Ukraine’s fossil fuel-based heat generation infrastructure necessitates the reconstruction of heating and cooling supply systems, with a focus on low-carbon energy sources, particularly heat pumps. Notably, Poland achieved the highest growth in installed heat pump capacities in Europe, offering valuable insights for Ukraine’s energy transition. This study employs the TIMES-Ukraine model to assess the potential proliferation of heat pumps within the country. The findings suggest that, if capital costs for heat pumps decrease, their adoption could accelerate more rapidly than biomass-fired heating systems, particularly in urban single-family homes and buildings lacking central heating systems, over the next decade. While high investment costs may slightly diminish the attractiveness of this technology for space heating, heat pumps consistently outperform biomass heating appliances and potential biomethane-sourced gas boilers.

A New Methodology for Estimating the Potential for Photovoltaic Electricity Generation on Urban Building Rooftops for Self-Consumption Applications

As the world increasingly embraces renewable energy as a sustainable power source, accurately assessing of solar energy potential becomes paramount. Photovoltaic (PV) systems, especially those integrated into urban rooftops, offer a promising solution to address the challenges posed by aging energy grids and rising fossil fuel prices. However, optimizing the placement of PV panels on rooftops remains a complex task due to factors like building shape, location, and the surrounding environment. This study introduces the Roof-Solar-Max methodology, which aims to maximize the placement of PV panels on urban rooftops while avoiding shading and panel overlap. Leveraging geographic information systems technology and 3D models, this methodology provides precise estimates of PV generation potential. Key contributions of this research include a roof categorization model, identification of PV-ready rooftops, optimal spatial distribution of PV panels, and innovative evaluation technology. Practical implementation in a real urban setting demonstrates the methodology’s utility for decision making in the planning and development of solar energy systems in urban areas. The main findings highlight substantial potential for PV energy generation in the studied urban area, with capacities reaching up to 444.44 kW. Furthermore, implementing PV systems on residential rooftops has proven to be an effective strategy for reducing CO2 emissions and addressing climate change, contributing to a cleaner and more sustainable energy mix in urban environments.

Comparison of Seismic and Structural Parameters of Settlements in the East Anatolian Fault Zone in Light of the 6 February Kahramanmaraş Earthquakes

On 6 February 2023, two very large destructive earthquakes occurred in the East Anatolian Fault Zone (EAFZ), one of Türkiye’s primary tectonic members. The fact that these earthquakes occurred on the same day and in the same region increased the extent of the destruction. Within the scope of this study, twenty different settlements affected by earthquakes and located directly on the EAFZ were taken into consideration. Significant destruction and structural failure at different levels were induced in reinforced concrete (RC) structures, the dominant urban building stock in these regions. To determine whether the earthquake hazard is adequately represented, the PGA values predicted in the last two earthquake hazard maps used in Türkiye for these settlements were compared with the measured PGAs from actual earthquakes. Subsequently, the damage to reinforced concrete structures in these settlements was evaluated within the scope of construction and earthquake engineering. In the final part of the study, static pushover analyses were performed on a selected example of a reinforced concrete building model, and target displacement values for different performance levels were determined separately for each earthquake. For the 20 different settlements considered, the displacements were also derived based on the values predicted in the last two earthquake hazard maps, and comparisons were made. While the target displacements were exceeded in some settlements, there was no exceedance in the other settlements. The realistic presentation of earthquake hazards will enable the mentioned displacements predicted for different performance levels of structures to be determined in a much more realistic manner. As a result, the performance grades predicted for the structures will be estimated more accurately.

CityDPC: A Python library for handling 3D city model datasets

AbstractThis study presents CityDPC, a Python library for geometric computations on CityGML and CityJSON datasets, merging features from tools such as CityATB. It supports loading, analyzing, validating, and manipulating 3D city model datasets, aiming to enhance Python applications for urban building stock analyses. It introduces a shared building class to expedite new data formats integration and improve software development and interoperability among urban‐scope applications. A novel feature is the calculation of party or shared walls, showcased in a UBEM (Urban Building Energy Modeling) context through TEASER+ integration. This demonstrates the library's utility in urban energy modeling, calculating shared walls to advance existing tools’ functionality and foster innovative urban‐scale building analysis applications.

Combining geographic information and climate data to develop urban building energy prediction models in Taichung, Taiwan

Climate change in Taiwan has extended and intensified the summer season, leading to a notable surge in energy demand for cooling systems, especially in densely populated regions. Building energy usage is directly correlated with cooling degree hours (CDHs), representing the hourly temperature differential between indoors and outdoors. This study employed high-resolution Taiwan ReAnalysis Downscaling (TReAD) data to develop an urban energy prediction model focusing on localized cooling demand in central Taiwan's urban areas. Validated against actual electricity consumption data, the model achieved an R2 value of 0.76. The study reveals that urban areas exhibit a high cooling demand during the hot season, exceeding 25,000°C-h and with an annual energy consumption of 44-64 kWh/m2. Conversely, rural areas have a lower cooling demand – that is, below 8,000°C-h, with an annual energy consumption of less than 10 kWh/m2.Considering the IPCC's RCP8.5 warming scenario, October shows a 20-40% increase in cooling demand compared to July and May. This underscores the need to address rising energy consumption especially during the early and late stages of the hot season in response to climate change.

GPT-based data-driven urban building energy modeling (GPT-UBEM): Concept, methodology, and case studies

Achieving carbon neutrality is a critical global goal, with urban building energy modeling (UBEM) playing a pivotal role by providing data-driven insights to optimize energy consumption and reduce emissions. This paper introduces GPT-based urban building energy modeling (GPT-UBEM), a novel approach utilizing GPT’s advanced capabilities to address key UBEM challenges using GPT-4o. The study aimed to demonstrate the effectiveness of GPT-UBEM in performing UBEM tasks and to explore its potential in overcoming traditional limitations. Specifically, (1) basic analytics of urban data, (2) data analysis and energy prediction, (3) building feature engineering and optimization, and (4) energy signature analysis were conducted in four case studies. These analyses were applied to 2,000 buildings in Seoul and 31 buildings in Gangwon-do, South Korea. Through case study, the findings highlighted the ability of GPT-UBEM to integrate diverse data sources, optimize building features for high accuracy in prediction models, and provide valuable insights for urban planners and policymakers through the use of expert domain knowledge and intervention. Additionally, based on the results derived from GPT-UBEM in this study, the current limitations of GPT-UBEM (L1 to L3) and future research directions (F1 to F4) have been outlined.

Mapping structural damage to material damage for analysis of urban buildings considering actual damage states

Mapping structural damage to material damage for analysis of urban buildings considering actual damage states

A novel automated labelling algorithm for deep learning-based built-up areas extraction using nighttime lighting data

The use of remote sensing imagery and cutting-edge deep learning techniques can produce impressive results when it comes to built-up areas extraction (BUAE). However, reducing the manual labelling set production process while ensuring high accuracy is currently the main research topic. This study pioneers the exploitation of nighttime lighting data (NLD) for automatically generating deep learning label sets, assessing the feasibility, and identifying limitations of using varied intensity ranges of lighting data directly for this purpose. We provide a novel method for generating fine-grained labels through an optimisation technique that eliminates the necessity for human involvement. This approach employs deep learning segmentation algorithms and has been tested in eight cities across seven countries. The results indicate that segmentation performs well in most cities, with the combination of iso clustering and NLD allowing for more precise extraction of urban building districts. The overall accuracy exceeds 90% in most cities. The results based on manual and historical data (around 0.7) as labels are significantly lower than those based on NLD. At the same time, the segmentation effect of deep learning has a more significant advantage over traditional machine learning classification algorithms (around 0.8). DeeplabV3 and U-Net exhibit different strengths in segmentation and extraction: DeeplabV3 has a stronger ability to eliminate errors, while U-Net retains the capability to handle less labelled information, making them mutually advantageous depending on the specific requirements of the task. It proposes a Strategy to automatically extract built-up areas with minimal human involvement.

Effects of lift-up building design, building setback, and urban open space on pedestrian danger under the joint effect of floodwater and wind

Urban block layouts affect both the flow patterns of floodwater and wind in urban built environments and ultimately pedestrian danger within the urban building array. Computational fluid dynamics (CFD) is used to simulate both the floodwater inundation process and the wind-blowing process within three kinds of urban block configurations under a floodwater-wind joint effect, including the lift-up building design, horizontal and vertical building setbacks, and urban open space. The obtained floodwater velocity, depth, and wind speed at the pedestrian level are adopted to quantify the level of pedestrian danger by using the instability threshold formula of a human body. A laboratory flume experiment regarding the stability/instability of a dummy that represents a pedestrian in a quasi-natural state is performed to qualitatively validate the numerical simulation results. Compared to the cuboid or triangular pillar that supports the lift-up building structure, the circular pillar yields the minimum resistance against floodwater and increases the wind speed at the pedestrian level, consequently leading to a large danger zone area. Horizontal building setbacks decrease the pedestrian danger, but vertical building setbacks intricately affect it. The existence of open space within the block decreases pedestrian danger to some extent depending on the location and area of open space. The wind plays the dominant role in triggering pedestrian instability for both the lift-up building design and the horizontal setback case. For both the lift-up building design and the open space configuration, the walking and running speeds of pedestrians decrease much more than the conventional urban block. The results of this study could be referred to by administrators and stakeholders in the design and management of urban building arrays to mitigate pedestrian risk in the content of resilient city management.

Integration of Landsat-8 OLI/TIRS and Sentinel-1A PolSAR for analyzing land surface temperature and its anomalies linked to ENSO in Surakarta, Indonesia

The increase in urban buildings leads to degraded vegetated areas, resulting in higher surface radiation and air temperatures. The rise of land surface temperature (LST) is also influenced by land cover changes and global climate related to the ENSO (El Nino-Southern Oscillation) phenomena. This study is the first to combine active and passive sensors to analyze LST anomalies linked to ENSO-related land cover change on a time series approach. Conducted from August 2018 to 2023 in an urban area dominated by buildings, we used Python programming to extract LST from the Landsat-8 OLI/TIRS passive sensor with a Mono-window algorithm. Meanwhile, the land cover classification was performed by Sentinel-1A active sensor imagery using polarimetric decomposition with unsupervised Wishart. The LST and land cover results were equalized to 30 m spatial resolution for regression and anomaly analysis based on reported ENSO phenomena. The results revealed that land cover type significantly affected LST variation during the study period, proven by the significance value of each land cover type being less than 0.05 and showing a positive correlation. However, the correlation is low, meaning that land cover change is not the dominant factor causing LST change. The low correlation caused by El Nino and La Nina, contributed more to the change in LST during the study period. The integrated method can overcome the weakness of passive sensors in penetrating clouds, contribute to a broader knowledge of the factors causing LST changes, and provide effective early mitigation strategies against the threat of future climate change crises.

Research on Natural Gas Leakage and Explosion Mechanisms in a Container House

As unconventional building structures, container houses are now widely used in urban tourism to create characteristic buildings. Nowadays, natural gas accidents occur frequently in cities and towns; however, the development of laws and influencing factors of natural gas accidents in container buildings have rarely been studied. In this paper, a natural gas explosion test was carried out in an ordinary container house, and a numerical simulation was carried out according to the test results. The influence of methane proportion, ignition position, pressure-relief area, and pressure-relief intensity on the explosion load was analyzed. Research shows that natural gas will gather from top to bottom during the process of leakage and diffusion, and vertical stratification will occur. The most unfavorable working condition is 9.5% methane. Using the roof of the container house as a pressure-relief panel can effectively control the influence range of natural gas explosion accidents and help reduce accident losses. It is suggested that the stacking of container buildings should be reduced as much as possible, and the roof strength should be weakened to ensure structural safety. The research results have certain reference values for the disaster prevention and reduction design of urban characteristic buildings.

Multi-branch convolutional neural network in building polygonization using remote sensing images

AbstractBuilding extraction and polygonization is important for urban studies, such as urbanization monitoring, urban planning. Remote sensing images, especially in RGB bands, provide sufficient semantic information which is useful for the task of building extraction and polygonization. Deep learning using Convolutional Neural Networks (CNNs) is proven to be successful in many fields, including building extraction from remote sensing images. In this paper, we propose a two-stage method to solve the task of building polygonization from remote sensing images based on deep learning. Firstly, we decompose a 2‑D building footprint model into three basic geometry primitives. Leveraging stacked Multi-Branch Modules (MBMs), we separate the task of building extraction into tasks of predicting the three geometry primitives using our proposed CNN. At the second stage, we propose an efficient enhanced building polygonization and adjustment algorithm to generate the final building polygons. This algorithm is able to handle both building blocks and individual buildings. We evaluate our model on three open datasets. For building blocks, our model achieved average precision of 62.7% and average recall of 73.6% on the CrowdAI mapping challenge dataset, and 13.9% and 24.4% respectively on the Urban Building Classification (UBC) dataset which contains mainly individual buildings. On the Inria aerial image dataset, the proposed method achieved Intersection over Union (IoU) over 71%.

On account of “double carbon” targeted regional public buildings in Beijing—Research on carbon emission measurement

Energy conservation and carbon reduction in the construction field is an important part of China’s dual-carbon strategy. And construction operation Carbon emissions during the period. It is also the stage where carbon emissions account for the largest proportion of the whole life cycle of buildings, among which. The carbon emissions per unit area of public buildings are twice that of civil buildings. This study takes the sub-center of Beijing City as an example, focusing on “double carbon”. Under the goal, the research on the measurement of carbon emissions of public buildings within a radius of about 812 square kilometers in the sub-center, from the perspective of urban construction development and the overall carbon emissions of regional-level buildings, on the basis of the research on the actual operation data of urban buildings, the bottom-up carbon emission measurement method of the sub-center has been established, combined with research data analysis, during the operation period, the carbon emissions per unit area of public buildings are twice that of civil buildings. This finding highlights the key role of public buildings in the stage with the largest carbon emissions in the entire life cycle of buildings. In addition, our study also puts forward the characteristics and suggestions of carbon emission reduction of public buildings in the sub-center area, which are instructive for formulating targeted work suggestions on the “dual carbon” goals of urban development. This study not only focuses on the carbon emission measurement of individual buildings, but also expands the perspective to urban construction development and regional levels, filling the gap in the overall research on regional building carbon emission measurement in existing research. It also combines the latest building energy-saving design standards and green building ratings to conduct detailed measurement and analysis of the carbon emission levels of buildings with different energy-saving design standards.

Assessment of the Level of Radioactivity in the Soil in Urban Areas and Building Materials of Arlit City (Agadez-NIGER)

Niger's uranium deposits are located in the north, bordering the southern Sahara. Mining activities led to the creation of the town of Arlit in 1969. Uranium mining and uranate production generate large volumes of radioactive solid and liquid tailings, as well as radioactive gases. Through dispersion and transport, these radioactive discharges become a source of contamination to the environment and food chain. The aim of our work is to assess the additional ambient exposure to radioactivity of surrounding populations as a result of mining activities. We assessed the risk of exposure to radionuclides from the uranium-238 decay chain through soil and certain building materials. The methodology used is based on collection of soil, sand, gravel and mud samples, which are analyzed using gamma spectrometry technique. Nine (9) public sites and five (5) building materials quarries were sampled for the work. The radiological parameters calculated are radium equivalent activity (Raeq), absorbed dose rate (D), internal and external risk indices (Hin and Hex) and gamma index (Iγ). For the whole study area, the calculated Raeq values range from 78.67 Bq/kg to 199.32 Bq/kg. These values are below the guideline value of 370 Bq/kg. In terms of air dose rate, however, the average value found was 0.32 mSv/year for the nine public sites considered. This exceeds the threshold value of 0.29 mSv/year corresponding to the selected exposure scenarios. In addition, in the mud (Quarry 5) and the second gravel quarry (Quarry 4), Iγ values greater than unity were found. A comparison is made with the results of similar studies around the world. Interpretation of the data obtained concludes that there is a risk of radiological overexposure at six (6) sites and two (2) quarries. This work is independent research which sheds new light on the issue of uranium mining activities impact on the environment in Arlit.

Multi-Objective Framework for Resilient Post-Disaster Self-Built Residential Buildings

The 2015 seismic events in Nepal highlighted critical challenges in constructing earthquake-resilient, self-built stone masonry houses in rural mountain areas. The Department of Urban Development and Building Construction (DUDBC), however, provided designs that failed to preserve the local architectural landscape and craftsmanship while adding unfamiliar knowledge and technology. To address the shortcomings of DUDBC model houses related to architecture, structure, and socio-economic concerns in designing and implementing post-earthquake houses, a need-based building development framework is required for standardized practice. This study proposes a novel framework to assess, design, and implement a self-built house after a disaster, consisting of five stages: (1) post-disaster problem assessment, (2) need identification, (3) material selection, (4) design development, and (5) validation and implementation. Based on this framework, we proposed a novel gabion building construction technology for two-story stone masonry structures that effectively mitigate post-disaster challenges such as logistic, resilience, and socio-economic aspects while improving disaster resiliency specifically in the high-elevation rural areas of Nepal. The proposed two-story gabion building preserves local architectural values, enhances structural integrity, and provides cost-effectiveness when compared with its DUDBC peer models while providing much-needed relief to the vulnerable community. The proposed resilient house, G-2.1, utilizes locally sourced materials and craftsmanship, and innovative gabion technology that ensures affordability and facilitates knowledge transfer. The contribution of this study includes a multi-objective framework for a two-story gabions house that is most suitable for self-built resilient homes that preserves the local architecture and socio-economic conditions while providing structural integrity and safety.

Influence of building collapse on pluvial and fluvial flood inundation of metro stations in central Shanghai

Abstract. Urban flooding poses a significant threat to vulnerable underground infrastructure systems, such as metro stations. Building collapse induced by earthquakes alters urban building layout and coverage, consequently influencing flood inundation and propagation patterns. This study employs GPU-accelerated hydrodynamic simulation to investigate the mechanisms by which building collapse affects subsequent pluvial or fluvial flooding in the Huangpu District of Shanghai. Massive building collapse layouts are randomly generated, on which hydrodynamic simulations are performed and the inundation process of the metro stations is analyzed. The results reveal that pluvial floods are strongly influenced by localized topography distributed across the city. Consequently, building collapse has a more substantial impact on pluvial flooding when more buildings have collapsed. In contrast, fluvial floods are sensitive to the source location (e.g., location of levee breach) and the long travel route. Building collapse can either positively or negatively influence fluvial flooding by constricting or blocking the flow path. This work highlights the complex mechanism of earthquake–flood multi-hazard processes, emphasizing the importance of performing local-to-local analysis when both the hazard (e.g., individual building collapse, fluvial flood) and the hazard-bearing body (e.g., metro station) are localized. To better serve urban disaster prevention and mitigation, more efforts should be directed to developing physics-based high-resolution urban earthquake–flood simulation methods, as well as to acquiring data to drive such simulations.

Study on the Spatial and Temporal Evolution of Building Carbon Emissions and Influencing Factors in the Urban Agglomeration of the Yangtze River Economic Belt

With the rapid urbanization process, the construction industry has become a significant source of urban carbon emissions in China. The carbon emissions from buildings in the urban clusters of the Yangtze River Economic Belt, a crucial region for China’s economic development, have attracted considerable attention. This study focuses on urban buildings and aims to investigate the primary influencing factors of building carbon emissions in the urban clusters of the Yangtze River Economic Belt. The study highlights the innovative use of nighttime light remote sensing data to analyze urban carbon emissions and provides an in-depth exploration of the spatiotemporal characteristics of building carbon emissions in the urban clusters of the Yangtze River Economic Belt. Utilizing nighttime light remote sensing data similar to DMSP-OLS and provincial-level building carbon emissions, combined with spatial autocorrelation and spatiotemporal geographically weighted regression models, the study estimates and analyzes the building carbon emissions from 2012 to 2021 in 71 prefecture-level and above administrative regions within the three major urban clusters of the Yangtze River Economic Belt. The results indicate a continuous increase in total building carbon emissions in the three major urban clusters of the Yangtze River Economic Belt, with an accelerating growth rate. Spatially, urban building carbon emissions exhibit enhanced convergence but decreasing correlation over time, demonstrating evolving spatiotemporal patterns. Furthermore, the study identifies economic development level, population size, built-up area, and industrial structure as the main factors influencing building carbon emissions, with industrial structure showing significant impact.