Search Results (168)

Site selection is the key to carrying out the industrial layout of construction and demolition waste (CDW) resourcing enterprises. The current study needs more data on CDW industry location. The current construction waste resource utilization rate and industrial layout need to be improved. This study uses statistical and visualization methods to analyze key factors affecting the location of CDW recycling enterprises. Additionally, it identifies planning strategies and policy incentives to drive industry development. The study explicitly adopts global and weighted geographic regression (GWR) analysis methods and uses ArcGIS 10.8 to visualize point of interest (POI) data. It was found that (1) the main factors affecting the spatial distribution of the CDW recycling economy, in order of importance, are river network density, financial subsidies, R&D incentives, the number of building material markets, the value added by the secondary industry, the area of industrial land, and the density of the road network. The three main drivers of site selection decisions are government subsidies, market size, land, and transportation resources. (2) Enterprise industry chain and transportation costs are industrial economic decision-making considerations. Enterprises are generally located on flat terrain, around industrial parks, near the center of urban areas, and close to demand and cost reduction. (3) At the city level, there are more resource-based enterprises in cities with high levels of economic development and strong policy support. The spatial distribution of enterprises is consistent with the direction of urban geographic development. There is a positive global correlation between construction waste resourcing enterprises. Ningbo, western Qingdao, and northern Beijing show high aggregation characteristics. Low–low aggregation characteristics exist in regions other than central Chongqing. High–low aggregation characteristics are found in the center of the main city of Chongqing, eastern Shanghai, and central Nanjing. Low–high aggregation is distributed in northeastern Ningbo, northern Guangzhou, and southern Shenzhen. (4) Regarding industrial agglomeration, except for Nanjing, construction waste industrial agglomeration occurs in all 11 pilot cities. Among them, Shanghai, Xiamen, and Hangzhou have industries that are distributed evenly. Xi’an and Chongqing have a centralized distribution of industries. Guangzhou, Shenzhen, Beijing, Ningbo, and Qingdao have multi-center clustering of industries. Nanning’s industry has a belt-shaped distribution. This research explores the micro elements of industry chain integration in the CDW industry. It combines incentive policies and urban planning at the macro level. Together, these efforts promote sustainable city construction. This research provides CDW location data and dates for future digital twin and city model algorithms. It supports industrial planning, transportation, spatial optimization, carbon emission analysis, city operations, and management and aims to enhance the city’s green and low-carbon operations. Full article

This article focuses on the assessment of point clouds obtained by various laser scanning methods as a tool for 3D mapping and Digital Twin concepts. The presented research employed terrestrial and mobile laser scanning methods to obtain high-precision spatial data, enabling efficient spatial documentation of urban structures and infrastructure. As a reference method, static terrestrial laser scanning (TLS) was chosen. Mobile laser scanning (MLS) data obtained by devices such as Lidaretto, the Stonex X120GO laser scanning device, and an iPhone 13 Pro with an Emlid scanning kit and GNSS antenna Reach RX were evaluated. Analyses based on comparing methods of classification, differences in individual objects, detail/density, and noise were performed. The results confirm the high accuracy of the methods and their ability to support the development of digital twins and smart solutions that enhance the efficiency of infrastructure management and planning. Full article

Urban digital twins (UDTs) were first discussed in 2018. Seven years later, we ask: What has been their contribution to urban planning and decision making so far? Here, we systematically review 88 peer-reviewed articles to map and compare UDTs’ ambitions with their realized contributions. Our results indicate that despite the vast technical developments, socio-technical challenges have remained largely unaddressed, causing many of UDTs’ ambitions to remain unrealized. We identify three categories in these socio-technical challenges: interdisciplinary integration (II), consensual contextualization (CC), and procedural operationalization (PO). Accordingly, we consolidate a socio-technical research and development agenda to realize the ambitions of UDTs for urban planning and decision making: Augmented Urban Planning (AUP). Full article

Transportation infrastructure is central to economic development and the daily lives of citizens. However, rapid urbanization, increasing vehicle ownership, and growing concerns about sustainable development have significantly heightened the complexity of managing these systems. Although digital twin (DT) technology holds great promise, most current research focuses on specific areas, lacking a comprehensive framework that spans the entire lifecycle of transportation infrastructure, from planning and construction to operation and maintenance. The technical challenges of integrating different DT systems remain unclear, which to some extent limits the potential of DT technology in the management of transportation infrastructure. To address this gap, this review first summarizes the fundamental concepts and architectures involved in DT systems for transportation infrastructure, such as roads, bridges, tunnels, and hubs. From a lifecycle perspective, DT systems for transportation infrastructure are categorized based on functional scope, data integration methods, and application stages, and their key technologies and basic frameworks are outlined. Subsequently, the potential applications of DT in various lifecycle stages of transportation infrastructure—planning and construction, operation and maintenance, and decommissioning and renewal—are analyzed, and current research progress is reviewed and discussed. Finally, the challenges and future directions for achieving a full lifecycle DT system for transportation infrastructure, encompassing technical, operational, and ethical aspects, are discussed and summarized. The insights gained herein will be valuable for researchers, urban planners, engineers, and policymakers. Full article

The concept of digital twins (DTs) has expanded to encompass buildings and cities, with urban building energy modeling (UBEM) playing a crucial role in predicting urban-scale energy consumption via modeling individual energy use and interactions. As a virtual model within urban digital twins (UDTs), UBEM offers the potential for managing energy in sustainable cities. However, UDTs face challenges with regard to integrating large-scale data and relying on bottom-up UBEM approaches. In this study, we propose an AI agent-based intelligent urban digital twin (I-UDT) to enhance DTs’ technical realization and UBEM’s service functionality. Integrating GPT within the UDT enabled the efficient integration of fragmented city-scale data and the extraction of building features, addressing the limitations of the service realization of traditional UBEM. This framework ensures continuous updates of the virtual urban model and the streamlined provision of updated information to users in future studies. This research establishes the concept of an I-UDT and lays a foundation for future implementations. The case studies include (1) data analysis, (2) prediction, (3) feature engineering, and (4) information services for 3500 buildings in Seoul. Through these case studies, the I-UDT was integrated and analyzed scattered data, predicted energy consumption, derived conditioned areas, and evaluated buildings on benchmark. Full article

In recent years, there appears to be a notable transition towards preserving heritage on a regional scale, aiming to promote the authenticity and integrity of traditional settlements. The complex natural topography of these settlements, along with their spread-out arrangement, poses significant challenges to their protection and preservation. With the acceleration of urbanization and the implementation of the national strategy of building a twin-city economic circle in the Chengdu–Chongqing region, economic, cultural, and ecological exchanges between the twin cities have become the key dimensions of their construction. In this context, the concept of the BaShu Cultural Tourism Corridor has emerged, and the traditional settlement sites in the study area have become an important node of urban–rural integration and the BaShu Cultural Tourism Corridor. The primary objective of this study is to establish a heritage corridor protection network for traditional settlements along the Chengdu–Chongqing Ancient Post Road within the Sichuan Basin of China as an example, using the minimum cost resistance model. This approach comprises several key steps: selecting traditional settlements, analyzing their distribution traits, calculating the resistance value for the spread of traditional settlement culture, identifying potential cultural corridors, identifying cultural spaces, and, thus, constructing a protection network. The results show that the spatial distribution of traditional settlements in the study area shows obvious clustering characteristics, the core ecological space in this study is located in the mountains in the north and the southwest, and the cultural spaces are located along the central and southwestern sections, creating a linear distribution pattern. In the ecological culture corridor network, the Premier Corridor is the most extensive of the three categories, connects important cultural area, and serves as a vital conduit for developing heritage tourism strategies. This research aims to bolster the comprehensive protection and application of the region’s traditional cultural heritage, fostering the integration of urban and rural zones and enhancing regional cultural tourism. Future research directions are to integrate the conservation of traditional settlements and the surrounding environment at different planning scales with local policies and to expand the role of digital technology in the data management and visualization of ecocultural corridors. Full article

The rapid evolution of smart city technologies has expanded digital twin (DT) applications from industrial to urban contexts. However, current urban digital twins (UDTs) remain predominantly focused on the physical aspects of urban environments (“spaces”), often overlooking the interwoven social dimensions that shape the concept of “place”. This limitation restricts their ability to fully represent the complex interplay between physical and social systems in urban settings. To address this gap, this paper introduces the concept of the social digital twin (SDT), which integrates social dimensions into UDTs to bridge the divide between technological systems and the lived urban experience. Drawing on an extensive literature review, the study defines key components for transitioning from UDTs to SDTs, including conceptualization and modeling of human interactions (geo-individuals and geo-socials), social applications, participatory governance, and community engagement. Additionally, it identifies essential technologies and analytical tools for implementing SDTs, outlines research gaps and practical challenges, and proposes a framework for integrating social dynamics within UDTs. This framework emphasizes the importance of active community participation through a governance model and offers a comprehensive methodology to support researchers, technology developers, and policymakers in advancing SDT research and practical applications. Full article

Globalization has profoundly impacted architecture by promoting urban homogenization, where global styles and materials overshadow local character. This shift prioritizes standardized functionality and energy efficiency over cultural identity, erasing regional architectural distinctiveness. In historical urban centers, globalization-driven interventions—such as ventilated facades or external thermal insulation systems (ETISs)—often simplify original compositions and alter building materiality, texture, and color. The Ensanche of San Sebastián serves as a case study highlighting this issue. Despite its architectural richness, which includes neoclassical and modernist buildings primarily constructed with sandstone from the Igeldo quarry, unprotected buildings are at risk of unsympathetic renovations. Such changes can distort the identity of what is considered “everyday heritage”, encompassing the residential buildings and public spaces that shape the collective memory of cities. This study presents a replicable methodology for assessing the vulnerability of buildings to facade interventions. By utilizing tools like digital twins, point cloud modeling, and typological analysis, the research establishes criteria for interventions aimed at preserving architectural values. It emphasizes the importance of collaborative efforts with urban planning authorities and public awareness campaigns to safeguard heritage. Ultimately, protecting architectural identity requires balancing the goals of energy efficiency with cultural preservation. This approach ensures that urban landscapes maintain their historical and social significance amidst globalization pressures. Full article

Traffic management systems play a crucial role in smart cities, especially because increasing urban populations lead to higher traffic volumes on roads. This results in increased congestion at intersections, causing delays and traffic violations. This paper proposes an adaptive traffic control and optimization system that dynamically adjusts signal timings in response to real-time traffic situations and volumes by applying machine learning algorithms to images captured through video surveillance cameras. This system is also able to capture the details of vehicles violating signals, which would be helpful for enforcing traffic rules. Benefiting from advancements in computer vision techniques, we deployed a novel real-time object detection model called YOLOv11 in order to detect vehicles and adjust the duration of green signals. Our system used Tesseract OCR for extracting license plate information, thus ensuring robust traffic monitoring and enforcement. A web-based real-time digital twin complemented the system by visualizing traffic volume and signal timings for the monitoring and optimization of traffic flow. Experimental results demonstrated that YOLOv11 achieved a better overall accuracy, namely 95.1%, and efficiency compared to previous models. The proposed solution reduces congestion and improves traffic flow across intersections while offering a scalable and cost-effective approach for smart traffic and lowering greenhouse gas emissions at the same time. Full article

The Quantum Internet (QI) necessitates a complete revision of the classical protocol stack and the technologies used, whereas its operating principles depend on the physical laws governing quantum mechanics. Recent experiments demonstrate that Optical Fibers (OFs) allow connections only in urban areas. Therefore, a novel Quantum Satellite Backbone (QSB) composed of a considerable number of Quantum Satellite Repeaters (QSRs) deployed in Low Earth Orbit (LEO) would allow for the overcoming of typical OFs’ attenuation problems. Nevertheless, the dynamic nature of the scenario represents a challenge for novel satellite networks, making their design and management complicated. Therefore, we have designed an ad hoc QSB considering the interaction between Digital Twin (DT) and Software-Defined Networking (SDN). In addition to defining the system architecture, we present a DT monitoring protocol that allows efficient status recovery for the creation of multiple End-to-End (E2E) entanglement states. Moreover, we have evaluated the system performance by assessing the path monitoring and configuration time, the time required to establish the E2E entanglement, and the fidelity between a couple of Ground Stations (GSs) interconnected through the QSB, also conducting a deep analysis of the created temporal paths. Full article

Digital twin technology us used to create accurate virtual representations of objects or systems. Digital twins span the object’s life cycle and keep updated with real-time data. Therefore, their simulation capabilities can be combined with deep learning to create a system that simulates scenarios, enabling analysis. As cities continue to grow and the demand for sustainable development increases, digital twin technology, combined with AI-driven analysis, will play a critical role in shaping the future of urban environments. The ability to accurately simulate and manage complex systems in real time opens up new possibilities for optimizing energy usage, reducing costs, and improving the quality of life for urban residents. In this study, a digital twin application is built to visualize a smart area in South Korea, utilizing a deep learning model for personal thermal comfort analysis, which can be useful for managing and saving building and household energy consumption. Using Cesium for Unreal, a powerful tool for integrating 3D geospatial data, and leveraging DataSmith to convert 3D data into Unreal Engine format, this study also contributes a roadmap for smart city application development, which is currently considered to be lacking. By creating a robust framework for smart city applications, this research not only addresses current challenges but also lays the groundwork for future innovations in urban planning and management. Full article

In urban engineering construction, ensuring the stability and safety of subsurface geological structures is as crucial as surface planning and aesthetics. This study proposes a novel multivariate radial basis function (MRBF) interpolant for the three-dimensional (3D) modeling of engineering geological properties, constrained by the stratigraphic structural model. A key innovation is the incorporation of a well-sampled geological stratigraphical potential field (SPF) as an ancillary variable, which enhances the interpolation of geological properties in areas with sparse and uneven sampling points. The proposed MRBF method outperforms traditional interpolation techniques by showing reduced dependency on the distribution of sampling points. Furthermore, the study calculates the bearing capacity of individual pile foundations based on precise stratigraphic thicknesses, yielding more accurate results compared to conventional methods that average these values across the entire site. Additionally, the integration of 3D geological models with urban planning facilitates the development of comprehensive urban digital twins, optimizing resource management, improving decision-making processes, and contributing to the realization of smart cities through more efficient data-driven urban management strategies. Full article

Artificial intelligence (AI) technologies have been widely applied to the automated detection of pipeline leaks. However, traditional AI methods still face significant challenges in effectively detecting the complete leak process. Furthermore, the deployment cost of such models has increased substantially due to the use of GPU-trained neural networks in recent years. In this study, we propose a novel leak detector, which includes a new model and a sequence labeling method that integrates prior knowledge with traditional reconstruction error theory. The proposed model combines the Kolmogorov–Arnold Network (KAN) with an autoencoder (AE). This model combines the Kolmogorov–Arnold Network (KAN) with an autoencoder (AE), forming a hybrid framework that effectively captures complex temporal dependencies in the data while exhibiting strong pattern modeling and reconstruction capabilities. To improve leak detection, we developed a novel unsupervised anomaly sequence labeling method based on traditional reconstruction error theory, which incorporates an in-depth analysis of the reconstruction error curve along with prior knowledge. This method significantly enhances the interpretability and accuracy of the detection process. Field experiments were conducted on real urban water supply pipelines, and a benchmark dataset was established to evaluate the proposed model and method against commonly used models and methods. The experimental results demonstrate that the proposed model and method achieved a high segment-wise precision of 93.1%. Overall, this study presents a transparent and robust solution for automated pipeline leak detection, facilitating the large-scale, cost-effective development of digital twin systems for urban pipeline leak emergency management. Full article

The digital transition is one of the biggest challenges of the new millennium. One of the key drivers of this transition is the need to adapt to the rapidly changing and heterogeneous technological landscape that is continuously evolving. Digital Twin (DT) technology can promote this transition at an urban scale due to its ability to monitor, control, and predict the behaviour of complex systems and processes. As several scientific studies have shown, DTs can be developed for infrastructure and city management, facing the challenges of global changes. DTs are based on sensor-distributed networks and can support urban management and propose intervention strategies based on future forecasts. In the present work, a three-axial operative framework is proposed for developing a DT urban management system using the city of Venice as a case study. The three axes were chosen based on sustainable urban development: energy, mobility, and resiliency. Venice is a fragile city due to its cultural heritage, which needs specific protection strategies. The methodology proposed starts from the analysis of the state-of-the-arts of DT technologies and the definition of key features. Three different axes are proposed, aggregating the key features in a list of fields of intervention for each axis. The Venice open-source database is then analysed to consider the data already available for the city. Finally, a list of DT services for urban management is proposed for each axis. The results show a need to improve the city management system by adopting DT. Full article

Many studies in the literature have assessed the blue–green infrastructure (BGI) characteristics that influence its cooling potential for sustainable urban development. Common assessment methods include satellite remote sensing, numerical simulations, and field measurements, each defining different cooling efficiency indicators. This methodological diversity creates uncertainties in optimizing BGI management. To address this, a literature review was conducted using Google Scholar, Web of Science, and Scopus, examining how the BGI cools urban space, which spatial data and methods are most effective, which methodological differences may affect the results, and what the current research gaps and innovative future directions are. The results suggest that remote sensing is ideal for large-scale BGI comparisons, numerical simulations for local development scenarios, and field measurements for assessing conditions closest to residents. Maximum BGI cooling intensity averages show 4 °C from remote sensing, 3 °C from field measurements, and 2 °C from numerical simulations. Differences in conclusions may arise from differences in the data resolution, model scale, BGI delineation method, and cooling range calculation. The key BGI characteristics include object size, vegetation fraction, foliage density, and spatial connectivity. Future research should prioritize the integration of the different methods, BGI shape complexity effectiveness assessment, and effects of urban morphology on evaluating BGI characteristics’ effectiveness, and explore digital twin technology for BGI management optimization. This study integrates key information on BGI’s cooling capabilities, serving as a useful resource for both practitioners and researchers to support resilient city development. Full article