Large-scale Building Research Articles

Work-Related Stress and Coping: A Comparative Analysis of On-Site and Office-Based Workers in UK Building Construction.

There are increasing mental health concerns in the construction industry workforce, with stress being a primary concern. This qualitative study investigates stress experiences and the management of stress in office-based and on-site workers in the UK building construction sector. This study can provide key insights for construction, but also potentially other industries which struggle with high stress levels among their employees. A total of 40 semi-structured interviews (20 on-site/20 office-based) were conducted at large-scale building construction projects in the southeast UK. Framework method analysis was used to derive an understanding of stress in the workplace and coping strategies. The study identified two major themes: the negative influence of work stress on personal and professional wellbeing, and the management of stress through proactive and reactive coping strategies. Results indicated that on-site workers frequently cited high workloads, tight deadlines, and safety concerns, whereas office-based workers tended to highlight job complexity and organisational pressures. Both population sub-groups reported stress affecting their work performance, with site-workers having the added concern of physical health and safety. Coping strategies among workers tended to rely on support networks, outside-the-workplace hobbies, and boundary-setting, among others. Stress remains a significant problem in the workplace and affects wellbeing; however, there are ways to mitigate the stress. Our findings could provide a foundation for intervention development.

Applying machine learning algorithms to group the energy performance of existing buildings by different granularity level of energy data

ABSTRACT Recent technology advancement has extended smart-metering capability, enabling measurement of fine-grained building energy consumption. These massive energy data provide options to apply machinelearning in building energy analysis. However, the energy details needed depend on the building feature being analyzed. Unlike system faults and user-related inefficiencies, degradation in building thermal performance results in a steady energy increase that can be reflected even with coarse-grained energy data. This study assesses whether energy data of low temporal resolution can be used to provide insight into building energy efficiency. It evaluates the application of machine learning in clustering buildings based on their thermal characteristics using energy data of different time intervals. Through k-means clustering, buildings are clustered using their hourly, daily, and monthly heating energy. The results indicate that all three granularities can identify buildings with high window transmittances and infiltration rates. These findings present an opportunity for applying machine learning not only for modern buildings with advanced metering systems but also for aging buildings that makes the majority of the existing building stock. The study presents a practical method to identify and prioritize buildings for energy retrofit, an important step toward an effective energy upgrade of a large-scale building using machine learning.

CADefender: Detection of unknown malicious AutoLISP computer-aided design files using designated feature extraction and machine learning methods

Computer-aided design (CAD) files are used to create digital designs for various structures – from the smallest chips in the high-tech industry to large-scale buildings and bridges in the civil engineering space. We found that most exploits and malicious payloads are deployed through Auto List Processing (AutoLISP) source code (LSP) or Fast Load AutoLISP (FAS) files, which are non-executable files (NEFs) containing scripts in the AutoLISP language that are native to AutoCAD; While antivirus software is capable of detecting many malicious CAD files, the potential to improve protection by using a dedicated machine learning (ML) based detection solution remains, especially against unknown and sophisticated CAD malware. In this study, we are the first to propose designated feature extraction methods and a robust framework aimed at the detection of known and unknown AutoLISP malware using ML algorithms. To accomplish this, we examined the structure, functionality, and ecosystems of AutoLISP files and collected the largest known representative collection of LSP files consisting of 6418 malicious and benign files (labeled and verified). We then explored the use of two novel static-analysis-based feature extraction methods (knowledge-based and structural) designated for LSP files to extract a discriminative set of informative features, which can subsequently be used by ML models to detect malicious LSP files. These two feature extraction methods serve as the basis of the proposed detection framework, whose performance we comprehensively compare to both widely used antiviruses and baseline ML models based on existing feature extraction methods, including MinHash, Bidirectional Encoder Representations from Transformers (BERT), and n-gram. Our results highlight our methods' contributions to the detection of unknown AutoLISP malware and demonstrate their ability to outperform existing methods. The best performance in the task of unknown malicious LSP file detection was obtained by the Artificial Neural Networks (ANN) model trained on 100 knowledge-based features, which obtained a true positive rate (TPR) of 99.49% with a false positive rate (FPR) of 0.57%. Our framework's role in explainability is also highlighted, as we also present the prominent features that contribute most to the model's detection capabilities; this information can be used for explainability purposes. We conclude by evaluating the proposed framework's ability to detect a malicious file from an unknown AutoLISP malware family and by evaluating our framework on an additional independent test set that originated from another source, scenarios that are often faced by malware detection solutions.

A Method for Extracting High-Resolution Building Height Information in Rural Areas Using GF-7 Data.

Building height is important information in disaster management and damage assessment. It is also a key parameter in studies such as population modeling and urbanization. Relatively few studies have been conducted on extracting building height in rural areas using imagery from China's Gaofen-7 satellite (GF-7). In this study, we developed a method combining photogrammetry and deep learning to extract building height using GF-7 data in the rural area of Pingquan in northern China. The deep learning model DELaMa was proposed for digital surface model (DSM) editing based on the Large Mask Inpainting (LaMa) architecture. It not only preserves topographic details but also reasonably predicts the topography inside the building mask. The percentile value of the normalized digital surface model (nDSM) in the building footprint was taken as the building height. The extracted building heights in the study area are highly consistent with the reference building heights measured from the ICESat-2 LiDAR point cloud, with an R2 of 0.83, an MAE of 1.81 m and an RMSE of 2.13 m for all validation buildings. Overall, the proposed method in this paper helps to promote the use of satellite data in large-scale building height surveys, especially in rural areas.

The Application of Intelligent Lighting in Thermal Power Plants

With the rapid development of electronic information technology, the Internet of Things (IoT), Internet technology, and modern communication technology, people are demanding higher standards for the building environment. Especially in modern large-scale buildings with high levels of industrialization, lighting systems should also be optimized accordingly. This article explores the application path of intelligent lighting in thermal power plants for reference.

Building detection based on searching of the optimal kernel shapes pruning method on Res2-Unet

<p>In recent years, advances in remote sensing technology have made it feasible to use satellite data for large-scale building detection. Moreover, the building detection from multispectral satellite photography data is necessary; however, it is difficult to recovery the accurate building footprint from the high-resolution pictures. Because the deep learning networks contains high computational cost and over-parameterized. Therefore, network pruning has been used to reduce the storage and computations of convolutional neural network (CNN) models. In this article, we proposed the pruning technique to prune the CNN network from Res2-Unet model for accurately detecting the buildings. Initially, the CNN network is pruned by utilizing the searching of the optimal kernel shapes technique. It is employed to carry out stripe-wise pruning and automatically find the ideal kernel shapes. Then the data quantification is applied to enhance the proposed model and also reduce the complexity. Finally, the enhanced Res2-Unet model is used for the building detection. Moreover, WHU East Asia Satellite and the Massachusetts building dataset are the two available datasets used to access the suggested framework. Compare to the existing models, the proposed model gives better performance.</p>

Large-scale building damage assessment based on recurrent neural networks using SAR coherence time series: A case study of 2023 Turkey–Syria earthquake

The Turkey–Syria earthquakes that occurred on February 6, 2023, have caused significant human casualties and economic damage. Emergency services require quick and accurate assessments of widespread building damage in affected areas. This can be facilitated by using remote sensing methods, specifically all-day and all-weather Synthetic Aperture Radar (SAR). In this study, we aimed to improve the detection of building anomalies in earthquake-affected areas using SAR images. To achieve this, we employed Recurrent Neural Network (RNN) to train coherence time series and predict co-seismic coherence. This approach allowed us to generate a Damage Proxy Map (DPM) for building damage assessment. The results of our study indicated that the estimated proportion of building damage in Kahramanmaras was approximately 24.08%. These findings were consistent with the actual damage observed in the field. Moreover, when utilizing the mean and standard deviation of coherence time series, our method achieved higher accuracy (0.761) and a lower false alarm rate (0.136) compared to directly using coherence with only two views of SAR data. Overall, our study demonstrates that this method provides an accurate and reliable approach for post-earthquake building damage assessment.

Assessment of airborne viral transmission risks in a large-scale building using onsite measurements and CFD method

Screening patients for potential respiratory infections through nucleic acid sampling was a critical non-pharmacologic intervention during a respiratory infectious disease pandemic. Evaluating the cross-infection risk of crowded activities conducted indoors is necessary. We conducted a field study of this event in a large-scale stadium and measured the variation in CO2 concentrations. We conducted simulations on the stadium's flow field, CO2 and N2O concentration distribution using the CFD method, which was validated by the on-site measured data. We simulated the diffusion of virus aerosols exhaled by the sampled individuals under an all-fresh and primary return air system, respectively. It was found that space morphology and airflow pattern are the main factors influencing the concentration distribution of human exhaled contaminants. Primary return air systems can reduce the infection risk for sampled individuals by the filtration system and improve the airflow pattern. With the ratio of fresh air to return air of 1.2, the primary return air systems with 70 %, 85 %, and 95 % filtration levels can reduce the infection risk by an average of 54.5 %, 58.3 %, and 62.1 %, respectively, compared to all-fresh air system. It is necessary to optimize the processes and queue setups for nucleic acid sampling activities in the room based on the room airflow characteristics and the return air system using an efficient filtration system.

Enhancing low-carbon building operations leveraging demand response driven by renewable energy consumption contributions

In the pursuit of sustainable low-carbon energy practices, demand response (DR) has emerged as a pivotal strategy, particularly in the management of building energy consumption. However, current demand management methods often lack a thorough evaluation of building carbon reduction benefits and fall short in promoting grid decarbonization. This paper proposes a low-carbon operation optimization method for buildings, with a novel DR strategy aimed at facilitating local renewable energy consumption and system-level renewables integration. Considering local and systemic renewable shares, an evaluation framework is first introduced for quantitative building energy assessment in each period. The building loads with controllable flexibilities are then grouped and aggregated based on their elasticity characteristics and probability distribution. Finally, a low-carbon optimization model is established, leveraging the DR strategy for sustainable building operations through flexible load regulation. Utilizing measured building data in Shanghai, a case study with extensive analysis validates the effectiveness of the methodology. The results suggest that following DR implementation, the similarity between the building load profile and the systemic renewable share improves by 34.48 %. Moreover, carbon emissions decrease by 5.27 % at the same level of building energy consumption. Further analysis highlights that energy storage can enhance load adjustment capabilities and the potential for reducing carbon emissions. Compared to conventional DR programs, the proposed strategy demonstrates significant advantages of abundant flexibility and low-carbon benefits. The widespread implementation of low-carbon operations in large-scale building groups has profound implications for integrating renewable energy and achieving system decarbonization goals.© 2017 Elsevier Inc. All rights reserved.

Automated building layout generation: Implementation and comparison of STREAMER Early Design Configurator and SDaC Layout Designer

When designing new buildings, various constraints must be taken into account. These constraints encompass factors such as the overall building size, the number, function, and size of required spaces, and even the surrounding neighborhood's existing buildings, infrastructure, and natural environment. Particularly the requirements of interior layout design are usually coupled with each other, making the design process more complex and time-consuming. This paper proposes a novel manner to consider the requirements, aiming to facilitate the generation process of building's interior layout during the early design phase. Besides, this paper aims to generate vectored output, which is more applicable to export BIM model, which also benefits the subsequent design and construct steps. This paper introduces STREAMER Early Design Configurator (EDC) and SDaC Layout Designer, both of which formats the user's requirements, semi-automatically generates interior layouts and exports IFC models of the floor plans. STREAMER EDC is used to discover the potential layouts of large-scale buildings with many rooms such as hospitals, while SDaC Layout Designer focuses on residential houses with comparatively fewer rooms. These drafts are enriched with additional data, including placement information, volume details, and space utilization. Ultimately, the floor plans are exported into three-dimensional objects and exported in the IFC format. This comprehensive step paves the way for more efficient and informed building design, promoting greater flexibility and adaptability in the early stages of the architectural process.

Investigating the Actuation of Sidewall Sprinkler in an Atrium Using CFD Simulation

This study investigates the actuation of sidewall sprinklers in large-scale buildings with high-ceilinged atriums, addressing the challenges of unique architectural configurations. Compliance with NFPA 101 requires automatic sprinkler systems, including atrium areas, in these buildings. To maintain aesthetic considerations, design engineers, particularly in the middle east, often propose sidewall sprinklers as an alternative to traditional ceiling sprinklers. This research assesses whether the sidewall sprinklers would actuate during a fire using Fire Dynamic Simulator (FDS). The findings indicate that sidewall sprinklers will fail to actuate if the fire is located at the centre of the atrium, even if the edge of the fire area is below the sprinklers. Furthermore, the study emphasizes the importance of using an FDS mesh resolution (D*/dx) of 6 or finer resolution when measuring temperatures near the flame or fire plume to ensure accurate evaluations of sprinkler activation. These findings provide valuable insights for design engineers and authorities, assisting in decision-making processes related to fire safety measures, system designs, and regulatory compliance.

Enhanced large-scale building extraction evaluation: developing a two-level framework using proxy data and building matching

ABSTRACT Deep learning-based building extraction methods have widespread applications in diverse fields. However, the evaluation of large-scale extraction results remains challenging, due to traditional evaluation metrics rely on manually created ground-truth samples and the lack of comprehensive reference-building data for developing countries. To address these problems, we proposed a two-level framework for evaluating large-scale footprint extraction. First, we utilised global open-source population and land use data as the proxy data, to assess grid-level completeness for the areas with insufficient reference data. Second, we introduced an improved two-way area-overlapping method to match the extracted footprints with the reference buildings, thereby enabling a comprehensive evaluation of the study region. Tested in Hyogo Prefecture and Numazu City, Japan, the results demonstrated a 2.6-% improvement in grid classification accuracy and an increase of 0.53 in the completeness correlation, compared with the results obtained using a single proxy indicator. Moreover, the optimised matching method achieved an outstanding semantic matching accuracy of 99%, with high efficiency and robustness in multi-scale matching. Therefore, the proposed approach can effectively evaluate large-scale footprint extraction results and interpret their semantic relationship with actual buildings, applicable globally regardless of the availability of reference building datasets.

Self-supporting lamellae: Shape variation methods for the 3D concrete printing of large overhang structures

3D Concrete Printing (3DCP) is considered a solution to confront both labor shortages and material waste in the construction industry. However, to realize non-standard, large-scale buildings, substantial amounts of supporting material and manual assembly are still required to print structures with significant overhangs, including the inclined structure and the cantilever structure. In response, the study draws inspiration from mushroom lamellae and proposes a geometry-based approach to adjusting the large-overhang shape into a self-supported one. This approach imitates lamella morphology by creating folds on the given geometry based on constraints established through empirical studies, thereby reducing overhangs and improving printability. Specifically, the proposed approach includes two shape variation methods. The first method generates lamellae perpendicular to the inclined shape, thereby providing lateral support to the filament. The second method creates lamellae under the cantilever, facilitating a seamless horizontal-to-vertical transition. The effectiveness of these methods is demonstrated through the generation and printing of four column capital samples. Upon evaluating the overhang mechanisms of these geometries, an approachable optimization method is also presented to further minimize the amplitude of lamellae while maintaining self-supporting ability. Compared with control groups that focus on either maximizing printability or minimizing material usage, the optimal result for the inclined structure prototype showcases an over 75 % overhang reduction on the critical layer with just about 15 % use of additional material. As for the cantilever prototype, an over 75 % overhang reduction is achieved on the critical layer with about 35 % additional material usage. Various large-scale structure examples are presented at the end of the research, illustrating the applicability and scalability of the lamella-inspired method in a wider range of building types.

China's first sub-meter building footprints derived by deep learning

The high spatial resolution building footprints are crucial for understanding urban development and its associated applications. However, up to now, the sub-meter-level building footprint data of China is still lacking. The challenges arise from two aspects: 1) the number of training samples is inadequate for large-scale building extraction. 2) the accuracy and efficiency of current models are insufficient to conduct large-scale building extraction. Therefore, we propose a framework for large-scale building extraction in this study, including semi-automated sample generation, building extraction model, model training, and post-processing. Specifically, the main technical contributions include: 1) BldgNet (Building Extraction Network) is proposed, including the Large Window Attention, Edge Attention, and Distribution Alignment Module with consideration of spatial contextual information, to address the challenge of the multi-scale building extraction, building boundary delineation, and class imbalance, respectively; 2) a semi-supervised training approach is proposed for large-scale building extraction, leveraging the incomplete information from OpenStreetMap (OSM) to enhance the diversity of building samples and the robustness of the model. Meanwhile, we created an open-source Global Building Dataset (GBD) comprising approximately 800,000 high-resolution (0.25 m) samples. This dataset incorporates diverse building styles worldwide, offering support for global building extraction. Based on the constructed sample set and the proposed deep net, we generated China's first sub-meter (0.5 m) building footprint dataset (CBF). Through testing on 750,000 buildings from 350 cities, the overall F1 score for CBF reached 83.71%. Finally, we validated that the proposed building extraction model can achieve satisfactory results compared to existing representative deep networks. GBD and CBF datasets can be publicly available and downloadable via https://zenodo.org/doi/10.5281/zenodo.10043351.

Semantic 3D reconstruction-oriented image dataset for building component segmentation

In image-driven 3D building reconstruction, instance segmentation is fundamental to pixel-wise building component detection, which can be fused with 3D data like point clouds and meshes via camera projection for semantic reconstruction. While deep learning-based segmentation has obtained promising results, it relies heavily on large-scale datasets for training. Unfortunately, existing large-scale image datasets often include irrelevant objects that obstruct building components, making them unsuitable for 3D building reconstruction. This paper addresses this gap by introducing a large-scale building image dataset to facilitate building component segmentation for 3D reconstruction. The dataset comprises 3378 images captured from both interiors and exteriors of 36 university buildings, annotated with 49,380 object instances across 11 classes. Rigorous quality control measures were employed during data collection and annotation. Evaluation of five typical deep learning-based instance segmentation models demonstrates the dataset's suitability for training and its value as a benchmark dataset for building component segmentation.

Optimal design of air distribution systems in large indoor spaces during the construction of walls: A case study on liquefied natural gas terminal storage tanks

Efficiently organizing indoor airflow is crucial for optimizing the working environment in large-scale wall construction projects, particularly when constructing liquefied natural gas membrane tanks. This helps prevent membrane corrosion, optimize construction time, and mitigate future safety risks. However, due to their size, these tanks often experience air stratification, posing challenges in meeting simultaneous temperature, humidity, and uniformity requirements during construction. To effectively address this issue, this study developed Computational Fluid Dynamics models that numerically simulated three commonly used ventilation systems: displacement ventilation system, stratified ventilation system, and fabric duct air supply system. Laboratory experiments were conducted to validate the accuracy of numerical simulation. Additionally, response surface methodology (RSM) was used to determine optimal combinations of air supply parameters for superior environmental uniformity control. The findings indicate that the fabric air supply system is more suitable than displacement and stratified ventilation systems for achieving environmental uniformity control in large spaces. It can limit wall temperature nonuniformity coefficient up to 9 % during summer and provide better uniformity in air velocity during winter conditions. Moreover, under summer conditions, the optimal range for air supply speed is between 9.62 m/s - 11.22 m/s with temperatures ranging from 16 °C to 19 °C, while under winter conditions, the optimal range is between 18.3 m/s −19.3 m/s with temperatures ranging from 33.0 °C to 35.0 °C.The optimal design of the ventilation system is advantageous for various building construction scenarios with stringent temperature and humidity requirements, particularly for large-scale buildings.

A deep learning-based super-resolution method for building height estimation at 2.5 m spatial resolution in the Northern Hemisphere

Building height is an important indicator for assessing the level of urban development along the vertical dimension. Existing large-scale building height estimation studies focus on coarse spatial resolution (e.g., 10, 500, and 1000 m), which cannot reveal height variations across buildings in urban areas. High-resolution images (e.g., < 5 m resolution) can support building-scale height estimation, but they usually have small spatial coverage and are not openly accessible. In this context, we proposed a deep learning-based super-resolution method to generate building height maps at a spatial resolution of 2.5 m using Sentinel-1/2 images. The proposed method consisted of two parts: 1) a super-resolution module (SR) for learning high-resolution features; and 2) a height stratification estimation module (HS) for guiding the network to learn different height levels to mitigate the imbalanced distribution of height values. We created an open building height dataset with 45,000 samples covering multiple urban areas in the Northern Hemisphere, including China, the conterminous United States (CONUS), and Europe. Experimental results showed that for height estimation at the pixel level, the proposed method obtained a root mean square error of 10.318 m in China, 5.654 m in CONUS, and 4.113 m in Europe, respectively. Predicted results provided rich spatial details, due to the inclusion of the super-resolution module, which was heavily missed by existing large-scale studies. Moreover, we calculated the mean and standard deviation of building height in 301 urban centers, each having at least a population of 500,000, and found that the buildings in China were the highest (11.353 m ± 9.543 m), followed by CONUS (8.487 m ± 6.202 m) and Europe (8.136 m ± 5.020 m). Ablation studies indicated that the joint use of Sentinel-1/2 images and the proposed modules (SR and HS) can effectively improve the performance of building height estimation. The building dataset we generated provides great potential in high-resolution database updating, urban planning, and natural disaster assessment, and indeed, a new perspective of how we can utilize cutting-edge satellite imaging technology in urban observation, measurement, monitoring, and management. The dataset and code of this study will be available at: https://github.com/lauraset/Super-resolution-building-height-estimation.

Analisis Desain Spasial pada Bangunan Prefabrikasi: Residensial

Prefabricated residential innovation is producing large-scale building components from factory environmental standards and then assembled at the construction site, obtaining production process efficiency benefits with modular methods, adjusting to occupant needs from agreed design configurations. However, this innovation brings the dilemma of ownership being closer to manufacturing than occupants and the existence of standard limits in design improvisation. This study aims to evaluate the production results of prefabricated building components in its innovation to residents in planning occupancy, as it is known that the dilemma of prefabricated occupancy allows the risk of gaps between residents to places or known as placelessness. Qualitative methods with a case study approach have been used in this research through spatial design analysis by Ian Higgins by explaining physical elements through planning strategies, spatial requirements, and spatial compositions. The results of the study concluded that prefabricated buildings achieve occupancy factors; 1) Effective and functional, refers to meeting key needs and supporting activities, 2) Practical and optimal, refers to the design of space arrangements that prioritize comfort and convenience, 3) Compatible, refers to achieving room personalization according to needs and lifestyle.

WYKORZYSTANIE ENERGII WIATRU STREFY PRZYZIEMNEJ W NISKICH OBIEKTACH ZABUDOWY INDYWIDUALNEJ

The issue of obtaining wind energy in individual construction facilities is associated with a change in the scope of design issues undertaken in the case of high and large-scale buildings. This is due to the limited size of the assumptions, the type of wind devices used and the need to maintain their proper relationship to the function and form of the architectural object, and the need to ensure acceptable energy efficiency in turbulent flow conditions. The currently adopted design approaches, treated as patterns and constituting the starting point in the design process, constitute a correct synthesis of the issues undertaken by the designer, but they do not fully exhaust the issues raised. This article presents a study concept of a residential facility located below ground level and equipped with an installation for obtaining wind energy.

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.