Large-scale Building Research Articles

Footprints in an urban array model under multiple wind directions: A wind tunnel experiment investigation

This study investigates the footprint distributions at various measurement positions in an urban array model, considering two incident wind directions, 22.5° and 45°, by a wind tunnel experiment. The airflow velocity and tracer concentration are simultaneously measured by an X-probe hot wire anemometer and a flame ionization detector, respectively, to assess the vertical flux of tracer gas. The experiment findings reveal a unified relationship between footprint distributions and measurement heights, as well as pronounced heterogeneity in footprints across horizontal positions in the urban model. The concentration footprints in both wind directions exhibit local patterns, primarily influenced by the local canopy vortex between buildings, and global patterns, arising from the large-scale building configuration within the urban model. Notably, the local pattern demonstrates a significant spanwise tilt in the 22.5° case and wave-like propagation along the streamwise direction in the 45° case, while the global pattern, characterized by contour lines developing along rows of blocks, remains consistent across both scenarios. Moreover, the flux footprint has constrained source areas compared to the concentration footprint, with its peak coinciding with areas exhibiting local patterns, where turbulent flux contribution exceeds 70%. These findings emphasize the importance of predicting the dispersion driven by local canopy vortex in the numerical modeling of urban footprint applications.

EXPERIMENTAL STUDY ON THE WOODEN MEMBER CONNECTION METHOD IN SEISMIC PANEL DAMPERS FOR STEEL STRUCTURES

Recently, government of Japan has encouraged using forest resources to expand the use of timber. Although moderate-or large-scale buildings are generally steel constructed structures, the authors proposed using cross-laminated timber (CLT) as support members for seismic panel damper systems. The benefits of employing mass timber as support members include the effective utilization of forest resources and a reduction in the building weight. To investigate the shear transfer performance between the shear panel damper and the mass timber, 16 specimens were meticulously prepared. Based on the test results, steel types such as H-shaped steel, circular tube, cruciform steel and steel plate were shown to be effective as the shear connector. In cases where H-shaped steel, circular tubes, and cruciform steel are utilized as shear connectors, significant labor is required for welding to the H-shaped steel and cutting into the mass timber, demanding time and effort from workers. Ensuring the shear connector section has high stiffness is crucial to maintain the energy dissipation capacity of the shear panel. Steel plates offer an advantage in their flexibility to control both strength and stiffness through the size of the plate used.

Construction Technology of Steel Structures in Large Public Buildings

With the continuous development of the Chinese economy and the rapid development of large public buildings, the application of steel structures in large buildings is gradually increasing. The use of steel structures is crucial for meeting the needs of large-span, super high-rise buildings, rapid construction, and achieving sustainable development of buildings. Therefore, studying and analyzing the construction technology of large-scale building steel structures has important practical significance. This study is based on a library construction project, focusing on the installation of embedded bolts, steel columns, steel beams, and high-strength bolts in the construction of large building steel structures, as well as the technical schemes and construction processes of welding, anti-corrosion coatings, and fireproof coatings. I hope to provide reference and suggestions for actual construction.

Fusing multimodal data of nature-economy-society for large-scale urban building height estimation

The building height holds significant importance for comprehensively understanding urban morphology, enhancing urban planning, and fostering sustainable development. Although many methods using optical and SAR images have been presented for building height estimation, these methods fall short in capturing the influences of economic and social attributes on building height. In this study, we introduced a Nature-Economy-Society (NES) feature model to comprehensively represent building height information, and established a multi-scale One-Dimensional (1-D) Convolutional Neural Network for predicting building heights, referred to as NES-CNN. First, we derived the natural attributes of urban buildings from time-series Sentinel-1 SAR images and Sentinel-2 multispectral images, as well as World Settlement Footprint (WSF) data and Digital Elevation Model (DEM), economic attributes from nighttime light and Gross Domestic Product (GDP) data, and social function attributes from Points of Interest (POI) data. Second, an autoencoder is employed to reduce the dimensionality of the high-dimensional natural attribute features, minimizing data redundancy. Finally, the multi-scale 1-D CNN model is presented to explore the correlations between the multi-source and heterogeneous NES features and building height information, facilitating the prediction of building height. In experiments, we applied the proposed method to estimate building heights in Beijing and Shanghai at a spatial resolution of 10 m. The results indicated that for Beijing, the RMSE, MAE, and R values are 6.93 m, 4.41 m, and 0.84, respectively, while for Shanghai, these values are 7.57 m, 5.38 m, and 0.80, respectively. The addition of social and economic attribute information decreases the RMSE by 6 % in both Beijing and Shanghai compared with using only natural attributes. In comparison to existing studies at the same mapping resolution, RMSE decreases by 39 % for Beijing and 51 % for Shanghai. The innovative and inspiring nature of this study lies in its application to large-scale building height estimation.

The effect of interlayer adhesion on stress distribution in 3D printed beam elements

Interlayer adhesion is one of the most pivotal aspects related to mechanical performance and durability of 3D printed concrete. However, due to continuous printing of multi-layered structures, large-scale buildings require long time intervals between layer deposition, with interlayer bond strength decreasing as time intervals between printed layers increase. This study evaluates comprehensively the effects of time intervals between layers: 1 h, 1.5 h, 2 h, 3 h, and 4.5 h as well as two different height to span ratios (hb/s). In order to assess the properties of interlayer regions microstructural examinations using techniques such as X-ray micro-computed tomography (micro-CT), scanning electron microscopy (SEM) were applied. In addition destructive testing of compressive, splitting tensile and flexural strengths along with crack opening analysis using the Digital Image Correlation (DIC) method was performed and supported with FEM analysis. The results show that interlayer adhesion strength decreased significantly for time intervals of up to 2 h, with a linearly decreasing trend. The reduction in strength the interlayer bond for a 2-h interval was 56.6%, compared to the reference specimen. Afterwards, the decrease in strength was minimal (up to 68.5% after 4.5 h time interval). However, microstructural investigations revealed that specimens with printing interval beyond 2 h exhibited noticeable continuous cracking in interfacial zone with widths reaching of up to 100 μm (SEM). At the same time micro-CT analysis confirmed creation of longitudinal pores which in turn was reflected in different cracking phenomena of specimens with longer printing intervals (>2 h). This phenomenon was further explained through FEM modeling. As an outcome it was proven through DIC that that increasing the time interval between the deposit of successive layers not only affects the strength of the entire beam, but also affects its cracking mode. The discontinuities in the interlayer contact zone leads to destruction characteristic of composite structures, thus the structure comes to be destroyed layer by layer, resulting from insufficient interlayer adhesion. The presented study is the first to fully evaluate the failure behavior of 3D printed specimens with long time intervals.

Transformer-based semantic segmentation for large-scale building footprint extraction from very-high resolution satellite images

Extracting building footprints from extensive very-high spatial resolution (VHSR) remote sensing data is crucial for diverse applications, including surveying, urban studies, population estimation, identification of informal settlements, and disaster management. Although convolutional neural networks (CNNs) are commonly utilized for this purpose, their effectiveness is constrained by limitations in capturing long-range relationships and contextual details due to the localized nature of convolution operations. This study introduces the masked-attention mask transformer (Mask2Former), based on the Swin Transformer, for building footprint extraction from large-scale satellite imagery. To enhance the capture of large-scale semantic information and extract multiscale features, a hierarchical vision transformer with shifted windows (Swin Transformer) serves as the backbone network. An extensive analysis compares the efficiency and generalizability of Mask2Former with four CNN models (PSPNet, DeepLabV3+, UpperNet-ConvNext, and SegNeXt) and two transformer-based models (UpperNet-Swin and SegFormer) featuring different complexities. Results reveal superior performance of transformer-based models over CNN-based counterparts, showcasing exceptional generalization across diverse testing areas with varying building structures, heights, and sizes. Specifically, Mask2Former with the Swin transformer backbone achieves a mean intersection over union between 88% and 93%, along with a mean F-score (mF-score) ranging from 91% to 96.35% across various urban landscapes.

Momentum Search Algorithm for Analysis of Fuel Cell Vehicle-to-Grid System with Large-Scale Buildings

Momentum Search Algorithm for Analysis of Fuel Cell Vehicle-to-Grid System with Large-Scale Buildings

Analysis of influence of concrete aggregate in evaluation of form removal time of concrete using ultrasonic pulse velocity methods

Recently, due to the increase in high-rise and large-scale buildings, the use of ordinary aggregates has increased, and research is continuously being conducted on the use of lightweight aggregates to reduce weight. However, because concrete exhibits different compressive strength due to material influences, it is believed that there will be a difference in the form demoulding time for ordinary aggregate and lightweight aggregate in the early age. In order to evaluate the characteristics of concrete in a quantitative manner, an experiment was conducted to predict the time of form demolding through correlation by measuring compressive strength and ultrasonic pulse velocity (UPV). In this study, the target strengths of normal aggregate concrete (NC) and lightweight aggregate concrete (LC) were set at 30 MPa, 45 MPa, and 60 MPa. To reach the target strength of each test specimen, the W/B ratio was set to 41.0%, 33.0%, and 28.0%. In addition, in order to meet the same mixing conditions, the mixing was carried out by setting the fine aggregate excluding aggregate, W/B ratio, and cement amount to the same level. In the case of compressive strength, NC30 was 13.4% higher than LC30 at 24 h, and NC45 and LC45 showed similar compressive strength. NC60 showed 29.9% higher compressive strength than LC60, and it can be seen that NC generally shows higher compressive strength than LC. It can be seen that as the target intensity increases, the difference between NC and LC at the same target intensity increases. At 24 h, UPV showed 21.9% and 11.9% lower rates for NC30 and NC45 compared to NC60. Additionally, compared to LC60, LC30 and LC45 showed 22.3% and 12.2% lower UPV, and it is believed that the timing of form demolding can be evaluated by utilizing the fact that UPV varies depending on aggregate and W/B ratio. The prediction models for NC and LC based on the correlation between compressive strength and UPV showed a coefficient of determination (R2) of 0.90 or higher. Although the cement content significantly impacts the UPV of concrete, UPV may vary depending on the porosity and density of the aggregate, thus making it necessary to consider these factors for strength estimation.

Roman Aspects of Romanesque Architecture in England and Wales

This article investigates late eleventh- and twelfth-century architecture in England and Wales from five interrelated points. First, it considers the appropriation of Roman sites for buildings of the post-1066 Norman administration. Second, it examines the creation of an architectural iconography in which there are associations with Imperial Rome. Third, there is the matter of scale, the desire to build big after many centuries of little or no tradition of constructing large edifices. Fourth, it explores the acquisition of a practical understanding of the building technology required for erecting large buildings. Fifth, it looks into the vocabulary of architectural articulation, the use of marble, and aspects of stone sculpture inherited from antiquity and appropriate to the monumentality of the new architecture. It references specific buildings, such as the Constantinian Basilica of Old St Peter’s in Rome, through the adaptation of triumphal arches down to details such as chip carving, marble, and the reuse and recreation of Roman bricks. Of the many large-scale buildings built by the Normans, a more detailed examination is devoted to the White Tower in London, castles at Colchester and Castle Rising, Winchester and Durham cathedrals, and the former abbey churches of Tewkesbury and Gloucester. It investigates the application of principles of the first-century BCE Roman architect and engineer, Vitruvius, the author of De architectura.

Multi-task deep learning for large-scale buildings energy management

Building energy management acts as the brain of the building, which controls the energy supply based on sensor data and algorithms. However, existing methods only focus on single-task prediction like load forecasting. As more multi-variable data is collected from ubiquitous sensors, building energy management needs to extend functionality from single-task to multi-purpose predictions. This study designs a multi-task learning system to tackle four different tasks: 1. Electricity load forecasting; 2. Air temperature forecasting; 3. Energy anomaly detection; 4. Energy anomaly prediction. A mixture-of-experts framework with the self-attention mechanism is proposed for learning heterogeneous tasks. A new comprehensive dataset has been created with real data to demonstrate the heterogeneous tasks' efficacy of the suggested framework. Extensive experiments are conducted with various deep learning models, which shows our proposed model achieves superior prediction performance overall tasks. Comparative studies are performed to explore the correlations between forecasting and anomaly learning, which reveal the benefits of multi-task learning for heterogeneous tasks. Anomaly detection and prediction both achieve 98% accuracy and 95% F1-score, while the electricity load forecasting single-task error is reduced by almost 60% through the multi-task model. Nonetheless, the tasks' training difficulties and resource consumption are also investigated and the deeper network doesn't ensure better performances. The dataset is open-sourced at: https://github.com/rekingbc/Multi-task-building.

К вопросу о развитии методов измерения вибрации строительных конструкций

Vibration processes contain useful information about the properties of both the signal source and the medium in which they propagate. Control of vibration parameters of structures is an integral part of monitoring. There are various methods and means of measuring vibration processes in building structures, as well as their registration. The development of technology and electronics allows us to steadily improve the parameters of vibration sensors, reduce their dimensions and create fundamentally new sensitive elements. Examples are given of vibration measuring equipment used by the staff of the Institute of Continuous Media Mechanics for testing large-scale building structures over the past 25 years. The results of vibration measurements of large-scale structures are shown, as well as the evolution of the natural frequency of a building structure during its long-term monitoring.

The Last Puzzle of Global Building Footprints—Mapping 280 Million Buildings in East Asia Based on VHR Images

Building, as an integral aspect of human life, is vital in the domains of urban management and urban analysis. To facilitate large-scale urban planning applications, the acquisition of complete and reliable building data becomes imperative. There are a few publicly available products that provide a lot of building data, such as Microsoft and Open Street Map. However, in East Asia, due to the more complex distribution of buildings and the scarcity of auxiliary data, there is a lack of building data in these regions, hindering the large-scale application in East Asia. Some studies attempt to simulate large-scale building distribution information using incomplete local buildings footprints data through regression. However, the reliance on inaccurate buildings data introduces cumulative errors, rendering this simulation data highly unreliable, leading to limitations in achieving precise research in East Asian region. Therefore, we proposed a comprehensive large-scale buildings mapping framework in view of the complexity of buildings in East Asia, and conducted buildings footprints extraction in 2,897 cities across 5 countries in East Asia and yielded a substantial dataset of 281,093,433 buildings. The evaluation shows the validity of our building product, with an average overall accuracy of 89.63% and an F1 score of 82.55%. In addition, a comparison with existing products further shows the high quality and completeness of our building data. Finally, we conduct spatial analysis of our building data, revealing its value in supporting urban-related research. The data for this article can be downloaded from https://doi.org/10.5281/zenodo.8174931 .

Imaging the Middle Ages: Twenty-First Century Revitalizations at Santiago de Compostela

Abstract: Since the Middle Ages, Santiago de Compostela has been home to one of the most famous pilgrimage destinations in Europe. Throughout its history, its success as a pilgrimage site has most often been signaled by large-scale building projects. This essay addresses how Peter Eisenman's Cidade da Cultura de Galicia (CCG), an architectural complex and cultural center overlooking Santiago de Compostela, mediates and responds to contemporary efforts toward the production and revitalization of the pilgrim experience within the city. While not as obvious as the interventions that occur along the route, the CCG is in fact a materialization of the goals of the revitalization initiative, a vision of secularized touristic pilgrimage for the twenty-first century, which is enacted through Compostela's successful image economy. The twentieth-century revitalization efforts are peculiar for the distinctive nature of their engagement with a medieval past, and the strong connections drawn between modern pilgrimage and its medieval counterpart. This article is concerned with the material evidence of competing interests in the revitalization process and specifically with how they relate to institutional interventions initiated by the regional government. The regional government's interests were often pursued in monumental form: in the late twentieth century, the Xunta operationalized a comprehensive cultural policy that was principally enacted in architecture. Examining the CCG's own past, as well as the medieval narrative of Compostela's history that it materializes, offers a new framework for understanding the role of built space in—and its relation to—the politics of pilgrimage revitalization within the twenty-first-century context.

Sustainable regeneration of waste polystyrene foam as cooling coating: Building cooling energy saving, CO2 emission reduction and cost-benefit prospective

Passive radiative cooling technology provides a green and energy-free approach for large-scale building cooling. Developing low-cost raw materials, green production process and entire-process evaluation is keys for the commercialization of cooling materials. This work proposes quickly converting abundant waste polystyrene foam into cooling coating via closed-loop solvent extraction strategy to satisfy scale and clean production requirements of building cooling envelope. Residential building thermodynamic model and outdoor cooling experiment are established to evaluate the application in building energy-saving and CO2 reduction. In addition, this work conducts a full lifecycle assessment to explore the advantages and disadvantages of the coating. Results demonstrate the coating has a randomly porous structure thus reflecting ∼97% solar radiation and emit ∼94% thermal radiation. About 8 °C of net and ∼7.5 °C/1000 cm3 building space cooling capacity can be achieved even under ∼1500 W/m2 of solar radiation. The coating is evaluated to reduce 5.09 kW h·m−2 electrical energy and ∼2 kg/m2 CO2 emission annually in Nanjing. Benefiting from the low-cost raw materials and closed-loop preparation process, the payback time and total environmental impact are reduced 54.96% and 28.29%–54.19% compared to commercial one, respectively. This work will effectively inspire the development of large-scale and low environmental impact building cooling envelopes.

Analysis of the Impact of Layout Mode on the Wind Environment of Dormitories in Coastal Universities in Southern Fujian Province

The building wind environment in coastal areas is highly complex and variable, particularly during winter when strong winds prevail. Understanding the influence of winter winds on human comfort and addressing health issues caused by wind noise in human settlements are very valuable research topics. This study focuses on developing strategies to optimize the wind environment in dormitory buildings located in the southern coastal area, where prolonged periods of strong winds have a significant impact. Specifically, we investigated the current wind in the teacher–student dormitory at Jinjiang Campus of Fuzhou University through questionnaire surveys and field measurements. Additionally, computational fluid dynamics (CFD) simulations were employed to evaluate different dormitory layouts. The research results indicate that it is preferable to position the dormitory areas downwind of large-scale buildings, adopt a staggered layout instead of a regular determinant layout to mitigate the narrow tube effect, avoid designing large-scale squares in the center of the dormitory areas, and enclose the dormitory groups with low-level corridors. These findings provide valuable guidance for the optimizing of winter windproof designs of university buildings located in the same climatic zone along the southeast coast.

Intelligent approach to mining cultural tourism potential areas based on YOLOv4: insights from Macau

ABSTRACT Based on historical background and geopolitical factors, Macau is closely related to Portuguese cities; Macau also currently attaches great importance to the creation of a Sino-Portuguese platform and cultural tourism to drive better economic development. This study explores a method for identifying and mining cultural tourism potential areas developed under the YOLOv4 framework and cosine similarity. The main conclusions of this study are as follows: (1) While the YOLOv4 method shows good performance and can effectively identify the architectural texture of Portuguese cities, it may face certain difficulties if applied to newly built urban areas in Macau. (2) Through the cosine similarity method, it is found that the architectural texture slices of the three island cities of Portugal and Macau have certain similarities. The city with the highest level of similarity with Macau is Lisboa, followed by Porto and Evora, while Guimaraes has the lowest level of similarity with Macau. This finding serves as a certain reference for the subsequent construction of the cultural tourism characteristics of the region. (3) Comparing the characteristics of Macau city obtained through the cosine similarity algorithm with the city slices that have a high level of similarity in Portugal, we find the following: “street networks”, “street blocks”, squares, and open space form in the Macau Peninsula, while “street blocks” and “large-scale building complexes” form in the outlying islands. These types of spaces have different scales and can be developed in the future in conjunction with cultural tourism activities on different scales. This study applies machine learning methods to the development of urban cultural tourism potential areas, which provides new perspectives and methods for urban tourism development planning.

Vibration Analysis Based on Video Data for Buildings and Structures

ODS (Operation Deflection shapes) based on video data holds the possibility to analyze objects with several advantages. The hardware costs are low, as only a camera is required, the measurement is simple and can be performed with little expert knowledge and the post processing is fast and straight forward. It is a contact free method, so the object's vibration is not influenced and by applying methods based on artificial intelligence it is in most cases not necessary to prepare surfaces. The limits of ODS based on video are mainly defined by the used camera and lens. This work will explore the advantages and limitations of the method applied in measurements of buildings and big structures. Results of measurements of large-scale buildings and bridges will be shown and compared to traditional methods and simulations. Advantages and disadvantage as well as limitations in hard- and software of the introduced method will be discussed.

Calibrating subjective data biases and model predictive uncertainties in machine learning-based thermal perception predictions

Heating, Ventilation, and Air Conditioning (HVAC) systems in large-scale buildings often struggle to ensure satisfactory thermal comfort for diverse occupants while minimizing energy waste. Achieving this goal requires developing reliable prediction models that capture the changing and varied occupant thermal perceptions in different spaces. Despite their widespread use, many machine learning (ML) based prediction models suffer from subjective data biases and model predictive uncertainties, causing inaccurate estimation for occupant needs and leading to suboptimal building controls. The authors propose a data-model integration method that identifies and calibrates the inherent uncertainties of existing ML models in both data and model dimensions, ensuring reliable thermal perception predictions. This method introduces the Multidimensional Association Rule Mining (M-ARM) algorithm to identify biased human responses by exploring interrelationships among four perception metrics: thermal sensation, comfort, acceptability, and preference. Our method reveals significant performance enhancements in seven ML models, enhancing the F1-score by up to 5.53%. By leveraging reliability diagrams and Expected Calibration Error (ECE) scores, we also expose the models’ vulnerability to miscalibration and the need for calibrated predictions. We further evaluate six calibration techniques (e.g., Platt Scaling and Isotonic Calibration) on these models and uncover their potential to enhance prediction reliability performance, highlighting a reduction of up to 80.66% in ECE scores. The authors also investigated the impacts of dataset sizes, classifiers, and calibration methods on the proposed method. Our research offers insight into creating robust data-driven strategies for thermal perception predictions, ultimately contributing to optimized occupant comfort and energy efficiency in buildings.

Brick and Mortar, Paint and Metal: Architecture and Craft in Renaissance Florence and Beyond

It is well known that many figures who rose to prominence as architects were initially trained as sculptors, metalworkers, carvers or painters, from Filippo Brunelleschi, who first learned the goldsmith trade, to Andrea Palladio, who started his career as a stonecutter. Yet how exactly their careers evolved and how their training informed their practice as architects remains an underexplored subject in architectural history. This article explores the social, intellectual and practical implications of craftsmanship in 15th- and 16th-century Italy, probing the relationship between painting, sculpture, metalwork and architecture by problematising the theoretical framework of disegno and interrogating measuring practices. In shedding light on the narrative potential of buildings and the broad cultural significance of architectural forms beyond three-dimensional, large-scale buildings, this research underscores how the historiographical gap between art and architectural history has hindered our understanding of craft and the contribution artists made to architectural practice.

Building archetype characterization for mass-housing energy efficiency through a UBEM approach

Building archetypes characterization is one of the main sources of inaccuracy in Urban Building Energy Modelling (UBEM). This study aims to implement an effective approach to developing a building archetype for estimating energy efficiency in social housing districts. The goal is to support researchers and decision-makers analyzing strategies for similar buildings, evaluate energy use in buildings, and design different retrofit scenarios. UBEM is a practical approach in large-scale building energy modeling used by researchers and stakeholders to analyze and develop different design and retrofit scenarios for buildings with similar construction features and occupancy. The archetype approach requires a subset of buildings representing a cluster with similar properties (e.g., building type, construction features, occupancy, and age), which is used to extrapolate the total energy consumption at the urban scale. The research focused on clusters of four in-line multi-story building types in Rome to conceptualize the study, characterized by reinforced concrete structures. The most diffused systems are the traditional building systems using a gas boiler and radiators for domestic hot water and heating. The model was implemented following a UBEM approach, relying on Urban Modeling Interface (UMI), a tool allowing the creation of building templates to evaluate their energy use at neighbourhood and city-scale. The overall results obtained in this study are described to characterize the archetype for the mass-housing building that researchers and administrations can use to evaluate different strategies for buildings with similar characteristics.