Hasil untuk "Building construction"

R. Hoffmann

Junghuei Chen, N. Seeman

C. Gagg

G. Foster

Abstract Circular economy strategies seek to reduce the total resources extracted from the environment and reduce the wastes that human activities generate in pursuit of human wellbeing. Circular Economy concepts are well suited to the building and construction sector in cities. For example, refurbishing and adaptively reusing underutilized or abandoned buildings can revitalize neighborhoods whilst achieving environmental benefits. Cultural heritage buildings hold a unique niche in the urban landscape. In addition to shelter, they embody the local cultural and historic characteristics that define communities. Therefore, extending their useful lifespan has multiple benefits that extend beyond the project itself to the surrounding area, contributing to economic and social development. To explore this complex issue, the research applies systematic literature review and synthesis methods. Decision makers lack knowledge of the environmental benefits of adaptive reuse of cultural heritage buildings and lack tools to implement these projects. A new comprehensive circular economy framework for the adaptive reuse of cultural heritage buildings to reduce environmental impacts intends to meet these needs. The framework integrates methods and techniques from the building and construction literature that aim to reduce lifecycle environmental impact of buildings with a circular product supply chain approach.

Jingchuan Zhang, Jialiang Wang, Sufen Dong et al.

Abstract Additive manufacturing, also known as three-dimension printing (3DP), has the advantages of high building efficient, low labor cost and less construction wastes compared to traditional construction technology. 3D printed concrete is a special type of concrete, which can be deposited through a 3D printer layer by layer without any formwork support and vibration process. Its important performance indexes, including workability, setting and hardening time, and mechanical properties, can be optimized by materials selection and printing parameters. To date, many building structures have been successfully printed using 3D printed concrete technology, some of which have even achieved its real applications. The 3D printed concrete has a great potential on practical applications, such as the affordable housing construction in low-income countries, military bunkers when the soldiers fighting in the wild, and complex constructions where the formwork is difficult to manufacture. In order to comprehensively introduce 3D printed concrete, this paper reviews the progress of 3D printed concrete in terms of workability, mechanical properties and building plan design. In addition, the current applications and further developments of 3D printed concrete are also discussed.

E. Fichter

Vishal Singh, N. Gu, Xiangyu Wang

T. Gieryn

Technology is everywhere, yet a theory of technology and its social dimension remains to be fully developed. Building on the influential book The Social Construction of Technological Systems, this volume carries forward the project of creating a theory of technological development and implementation that is strongly grounded in both sociology and history. The 12 essays address the central question of how technologies become stabilized, how they attain a final form and use that is generally accepted. The essays are tied together by a general introduction, part introductions, and a theoretical conclusion.

E. Corey, A. Guzman-Perez

Zhang Yiqi, Huang Shuoquan, Li Xiuming et al.

Applying data-driven fault-diagnosis models to data center air-conditioning systems can significantly improve operational reliability. However， these models often lack diagnostic interpretability， which limits their application. This study develops a composite fault-diagnosis model based on typical machine-learning algorithms， compares the diagnostic performance of different models， and conducts interpretability research on the diagnostic models using the Shapley additive explanation method. The results demonstrate that the convolutional neural network （CNN）-based fault-diagnosis model achieves optimal performance in both the heat-pipe and vapor-compression modes， with F-1 scores exceeding 0.999 across all classifications. In the heat-pipe mode， the diagnosis of the CNN model primarily relies on the condenser-fan frequency， outdoor temperature， and refrigerant-pump power consumption as key features， whereas in the vapor-compression mode， the dominant features are the outdoor temperature， compressor frequency， and subcooling degree.

Anke Blommaert, Marijke Steeman, Nathan Van Den Bossche

This study examines decay risks in cross-laminated timber (CLT) wall assemblies with built-in moisture, aiming to develop a simulation-based methodology to assess moisture dynamics during the construction phase. Differing from previous research, this study focuses on the central regions of CLT wall panels. Moisture distribution within the panel, especially in the exposed layer, is critical for understanding potential degradation. A series of simulations were conducted to determine the necessary level of detail for moisture profiling, comparing approaches that use a single average value, layer-specific averages, and a refined profile that distinguishes the outer 5 mm from the remaining material. The influence of factors such as wood type, glue type, delivery moisture content, orientation, and rain exposure was systematically evaluated to define realistic moisture profiles at the end of the construction phase. Subsequent degradation assessments incorporated these profiles along with variations in insulation materials to evaluate the time of wetness, dose accumulation, and heat flux increases. Results indicate that a detailed moisture profile is essential for accurately predicting decay risk and that trade-offs exist between moisture management and thermal performance depending on the insulation used. These findings provide a framework for predicting decay risks in CLT assemblies and offer insights for designing more durable and energy-efficient structures.

Jungha Im

The social economy has become increasingly relevant in local regeneration, especially South Korea’s declining inner cities. This study investigates the role of social economy organizations (SEOs) within the network governance framework, focusing on three cases – Suncheon-si, Mokpo-si, and Gwangju Dong-gu – to assess SEOs’ network potential and limitations. Using Social Network Analysis (SNA) and in-depth interviews, this study reveals locality-specific network dynamics within and beyond government-led initiatives. In Suncheon-si, strong local government intervention facilitated the connections between regeneration projects and SEOs, as well as across broader sectors, though SEO solidarity remained limited. Mokpo-si demonstrated open and cohesive networks with significant roles played by new and younger SEOs, despite the comparatively smaller scale of the social economy. Gwangju Dong-gu displayed generally well-connected networks, with SEOs closely linked across regeneration projects, the social economy sector, community groups, and network organizations. These findings highlight that each locality has unique potential for SEOs to support local regeneration by leveraging diverse forms of social capital. SEOs can act as intermediary agents, enhancing network governance and evolving as local ecosystem actors. This study suggests that local regeneration policies should acknowledge the diverse roles of the social economy and tailor local governance structures to incorporate these entities effectively.

Wenjuan Kang, Ni Kang, Pohsun Wang

Urban streetscapes are among the most frequently encountered spatial environments in daily life, and their restorative visual features have a significant impact on well-being. Although existing studies have revealed the relationship between streetscape environments and perceived restorativeness, there remains a lack of scalable, data-driven methods for quantifying such perception at the street level. This study proposes an interpretable and replicable framework for predicting streetscape restorativeness by integrating semantic segmentation, perceptual evaluation, and machine learning techniques. Taking Liwan District of Guangzhou as a case study, street-view images (SVIs) were collected and processed using the Mask2Former model to extract the following five key visual metrics: greenness, openness, enclosure, walkability, and imageability. Based on the Perceived Restorativeness Scale (PRS), an online questionnaire was designed from four dimensions (fascination, being away, compatibility, and extent) to score a random sample of images. A random forest model was then trained to predict the perceptual levels of the full dataset, followed by K-means clustering to identify spatial distribution patterns. The results revealed that there were significant differences in visual characteristics among high, medium, and low restorativeness street types. The proposed framework enables scalable, data-driven evaluation of perceived restorativeness across diverse urban streetscapes. By embedding perceptual metrics into large-scale urban analysis, the framework offers a replicable and efficient approach for identifying streets with low restorative potential—thus providing urban planners and policymakers with a novel tool for prioritizing street-level renewal, improving public well-being, and supporting perception-oriented urban design without the need for labor-intensive fieldwork.

Qian Gao, Mengwei Zhu, Yiyang Lu et al.

TY-type intersecting joints are widely used in ultra-high voltage long-span transmission towers. To improve the ultimate bearing capacity of TY-type intersecting joints, this paper proposes a composite externally stiffened intersecting joint based on the TY-type joint, which involves setting vertical gusset plates and vertical stiffeners on the outer surface of the TY-type joint. In this paper, 3 different TY-type intersecting joints are designed, and experimental studies are carried out to explore the failure modes, load–displacement relationships, and plastic development laws of these different TY-type intersecting joints. The results show that stiffening measures can effectively enhance the ultimate bearing capacity and initial stiffness of the joints. Based on the experimental results, the correctness of the numerical simulation is verified. Taking the composite externally stiffened intersecting joint as the base model, 256 stiffened joint models are established, and numerical simulation is used to investigate the influence of different geometric parameters on the ultimate bearing capacity of the joints. The results indicate that: The use of gusset plates and stiffeners can significantly improve the ultimate bearing capacity and overall stiffness of the unstiffened joints; The failure mechanism of the composite stiffened joints is consistent with that of the unstiffened joints, both characterized by buckling in the core of the main pipe; The ultimate bearing capacity of the composite stiffened joints is positively correlated with the diameter ratio of the branch pipe to the main pipe, the thickness ratio of the external stiffening plate, the thickness ratio of the external stiffener, and the height ratio, while it is negatively correlated with the diameter–thickness ratio of the main pipe. The research results on the new-type intersecting joints in this paper can provide a design reference for their practical engineering applications in transmission towers.

Yu-Hsuan Hsu, Sara Beery, Christopher P. Kempes et al.

Evidence shows that biological organisms tend to be more energetically efficient per unit size. These scaling patterns observed in biological organisms have also been observed in the energetic requirements of cities. However, at lower levels of organization where energetic interventions can be more manageable, such as buildings, this analysis has remained more elusive due to the difficulties in collecting fine-grained data. Here, we use the maintenance energy usage in buildings at the Massachusetts Institute of Technology (MIT) from 2009 to 2024 to analyze energetic trends at the scale of individual buildings and their sensitivity to strong external perturbations. We find that, similar to the baseline metabolism of biological organisms, large buildings are on average $24\%$ more energetically efficient per unit size than smaller buildings. Because it has become debatable how to better measure the efficiency of buildings, this scaling pattern naturally establishes a baseline efficiency for buildings, where deviations from the mean would imply a more or less efficient building than the baseline according to volume. This relative efficiency progressively increased to $34\%$ until 2020. However, the strong activity disruption caused by the COVID-19 pandemic acted as a major shock, removing this trend and leading to a reversal to the expected $24\%$ baseline level. This suggests that energetic adaptations are contingent on relatively stable conditions.

Xiao Xiang Zhu, Sining Chen, Fahong Zhang et al.

We introduce GlobalBuildingAtlas, a publicly available dataset providing global and complete coverage of building polygons, heights and Level of Detail 1 (LoD1) 3D building models. This is the first open dataset to offer high quality, consistent, and complete building data in 2D and 3D form at the individual building level on a global scale. Towards this dataset, we developed machine learning-based pipelines to derive building polygons and heights (called GBA.Height) from global PlanetScope satellite data, respectively. Also a quality-based fusion strategy was employed to generate higher-quality polygons (called GBA.Polygon) based on existing open building polygons, including our own derived one. With more than 2.75 billion buildings worldwide, GBA.Polygon surpasses the most comprehensive database to date by more than 1 billion buildings. GBA.Height offers the most detailed and accurate global 3D building height maps to date, achieving a spatial resolution of 3x3 meters-30 times finer than previous global products (90 m), enabling a high-resolution and reliable analysis of building volumes at both local and global scales. Finally, we generated a global LoD1 building model (called GBA.LoD1) from the resulting GBA.Polygon and GBA.Height. GBA.LoD1 represents the first complete global LoD1 building models, including 2.68 billion building instances with predicted heights, i.e., with a height completeness of more than 97%, achieving RMSEs ranging from 1.5 m to 8.9 m across different continents. With its height accuracy, comprehensive global coverage and rich spatial details, GlobalBuildingAltas offers novel insights on the status quo of global buildings, which unlocks unprecedented geospatial analysis possibilities, as showcased by a better illustration of where people live and a more comprehensive monitoring of the progress on the 11th Sustainable Development Goal of the United Nations.

Ruida Hu, Chao Peng, Xinchen Wang et al.

Scaling up executable code data is significant for improving language models' software engineering capability. The intricate nature of the process makes it labor-intensive, time-consuming and expert-knowledge-dependent to build a large number of executable code repositories, limiting the scalability of existing work based on running tests. The primary bottleneck lies in the automated building of test environments for different repositories, which is an essential yet underexplored task. To mitigate the gap, we introduce Repo2Run, the first LLM-based agent aiming at automating the building of executable test environments for any repositories at scale. Specifically, given a code repository, Repo2Run iteratively builds the Docker image, runs unit tests based on the feedback of the building, and synthesizes the Dockerfile until the entire pipeline is executed successfully. The resulting Dockerfile can then be used to create Docker container environments for running code and tests. We created a benchmark containing 420 Python repositories with unit tests for evaluation. The results illustrate that Repo2Run achieves an 86.0% success rate, outperforming SWE-agent by 77.0%. The resources of Repo2Run are available at https://github.com/bytedance/Repo2Run.

W. Creed, Maureen A. Scully, John R. Austin

Youngsoo Jung, Mihee Joo

Xiaoxiao Duan, Dengke Yang, Xuexu An

In this study, the response mechanism between macro- and microscales of deep hard-rock diorite is investigated under loading and unloading conditions. Moreover, the statistical theory is combined with particle flow code simulations to establish a correlation between unloading rates observed in laboratory experiments and numerical simulations. Subsequent numerical tests under varying confining pressures are conducted to examine the macroscopic mechanical properties and the evolution of particle velocity, displacement, contact force chain failures, and microcracks in both axial and radial directions of the numerical rock samples during the loading and unloading phases. The findings indicate that the confining pressure strength curve displays an instantaneous fluctuation response during unloading, which intensifies with higher initial confining pressures. This suggests that rock sample damage progresses in multiple stages of expansion and penetration. The study also reveals that with increased initial confining pressure, there is a decrease in particle velocity along the unloading direction and an increase in particle displacement and the number of contact force chain failures, indicating more severe radial expansion of the rock sample. Furthermore, microcracks predominantly accumulate near the unloading surface, and their total number escalates with rising confining pressure, suggesting that higher confining pressures promote the development and expansion of internal microcracks.