Search Results (191)

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

Real-scene 3D digital campuses are essential for improving the accuracy and effectiveness of spatial data representation, facilitating informed decision-making for university administrators, optimizing resource management, and enriching user engagement for students and faculty. However, current approaches to constructing these digital environments face several challenges. They often rely on costly commercial platforms, struggle with integrating heterogeneous datasets, and require complex workflows to achieve both high precision and comprehensive campus coverage. This paper addresses these issues by proposing a systematic multi-source data fusion approach that employs open-source technologies to generate a real-scene 3D digital campus. A case study of Lanzhou Jiaotong University is presented to demonstrate the feasibility of this approach. Firstly, oblique photography based on unmanned aerial vehicles (UAVs) is used to capture large-scale, high-resolution images of the campus area, which are then processed using open-source software to generate an initial 3D model. Afterward, a high-resolution model of the campus buildings is then created by integrating the UAV data, while 3D Digital Elevation Model (DEM) and OpenStreetMap (OSM) building data provide a 3D overview of the surrounding campus area, resulting in a comprehensive 3D model for a real-scene digital campus. Finally, the 3D model is visualized on the web using Cesium, which enables functionalities such as real-time data loading, perspective switching, and spatial data querying. Results indicate that the proposed approach can effectively get rid of reliance on expensive proprietary systems, while rapidly and accurately reconstructing a real-scene digital campus. This framework not only streamlines data harmonization but also offers an open-source, practical, cost-effective solution for real-scene 3D digital campus construction, promoting further research and applications in twin city, Virtual Reality (VR), and Geographic Information Systems (GIS). 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

Mobility digital twins (MDTs), which utilize multi-modal microscopic (micro) traffic simulations and an activity-based demand generation, are envisioned as flexible and reliable planning tools for addressing today’s increasingly complex and diverse transport scenarios. Hybrid models may become a resource-efficient solution for building MDTs by creating large-scale, mesoscopic (meso) traffic simulations, using simplified, queue-based network-link models, in combination with more detailed local micro-traffic simulations focused on areas of interest. The overall objective of this paper is to develop an efficient toolchain capable of automatically generating, calibrating, and validating hybrid scenarios, with the following specific goals: (i) an automated and seamless merge of the meso- and micro-networks and demand; (ii) a validation procedure that incorporates real-world data into the hybrid model, enabling the meso- and micro-sub-models to be validated separately and compared to determine which simulation, micro- or meso-, more accurately reflects reality. The developed toolchain is implemented and applied to a case study of Munich, Germany, with the micro-simulation focusing on the city quarter of Schwabing, using real-word traffic flow and floating car data for validation. When validating the simulated flows with the detected flows, the regression curve shows acceptable values. The speed validation with floating car data reveals significant differences; however, it demonstrates that the micro-simulation achieves considerably better agreement with real speeds compared to the meso-model, as expected. Full article

This systematic literature review explores the intersection of AI-driven digital twins and IoT in creating a sustainable building environment. A comprehensive analysis of 125 papers focuses on four major themes. First, digital twins are examined in construction, facility management, and their role in fostering sustainability and smart cities. The integration of IoT and AI with digital twins and energy optimization for zero-energy buildings is discussed. Second, the application of AI and automation in manufacturing, particularly in Industry 4.0 and cyber-physical systems, is evaluated. Third, emerging technologies in urban development, including blockchain, cybersecurity, and EEG-driven systems for sustainable buildings, are highlighted. The study underscores the role of data-driven approaches in flood resilience and urban digital ecosystems. This review contributes to sustainability by identifying how digital technologies and AI can optimize energy use and enhance resilience in both urban and industrial contexts. Full article

Energy management in smart cities has gained particular significance in the context of climate change and the evolving geopolitical landscape. It has become a key element of sustainable urban development. In this context, energy management plays a central role in facilitating the growth of smart and sustainable cities. The aim of this article is to analyse existing scientific research related to energy in smart cities, identify technological trends, and highlight prospective directions for future studies in this field. The research involves a literature review based on the analysis of articles from the Scopus and Web of Science databases to identify and evaluate studies concerning energy in smart cities. The findings suggest that future research should focus on the development of smart energy grids, energy storage, the integration of renewable energy sources, as well as innovative technologies (e.g., Internet of Things, 5G/6G, artificial intelligence, blockchain, digital twins). This article emphasises the significance of technologies that can enhance energy efficiency in cities, contributing to their sustainable development. The recommended practical and policy directions highlight the development of smart grids as a cornerstone for adaptive energy management and the integration of renewable energy sources, underpinned by regulations encouraging collaboration between operators and consumers. Municipal policies should prioritise the adoption of advanced technologies, such as the IoT, AI, blockchain, digital twins, and energy storage systems, to improve forecasting and resource efficiency. Investments in zero-emission buildings, renewable-powered public transport, and green infrastructure are essential for enhancing energy efficiency and reducing emissions. Furthermore, community engagement and awareness campaigns should form an integral part of promoting sustainable energy practices aligned with broader development objectives. Full article

To improve the informatization construction and intelligent decision-making level of river and lake basin management, the water quality of a digital twin basin was considered as the starting point and a water quality evaluation platform for Chuancheng River and Baihe River in Nanyang City, Henan Province was established. Based on digital twin technology, the platform establishes a virtual space city model, uses the long short-term memory algorithm to establish a water quality prediction model, draws the distribution of water pollution factors in two dimensions based on Kriging interpolation, simulates the pollutant diffusion in three dimensions based on numerical simulation, and finally builds a visual platform for evaluation and analysis. The platform combines digital twin with three models: one-dimensional (1D) water quality data processing, two-dimensional pollutant distribution, and three-dimensional (3D) pollutant diffusion simulation to achieve visual and comprehensive management of water quality assessment. Compared with the traditional 1D water quality data management platform, the proposed digital twin 3D urban river water quality evaluation platform system solves the problems of low visualization degree, single management, and incomplete analysis, as well as provides a new technical guarantee for the management of urban river water quality. Full article

This study explores the integration of smart city and resilience strategies in Sydney, focusing on the relationship between technological advancements and urban resilience. By analysing strategic documents and key projects—specifically the NSW Spatial Digital Twin, Land iQ, and SIMPaCT—this research identifies important synergies and gaps in Sydney’s urban planning efforts. The findings indicate that these projects improve urban functionality through real-time data integration, predictive planning, and adaptive infrastructure. However, there are inconsistencies between strategic objectives and actual implementation, particularly concerning stakeholder inclusivity and equity considerations. The study concludes that utilising technologies such as the Internet of Things (IoT) and artificial intelligence (AI), along with equitable stakeholder engagement, has the potential to significantly enhance Sydney’s ability to address environmental, social, and economic challenges. These insights offer practical recommendations for policymakers and urban planners who aim to balance innovation with inclusivity in smart city development. Full article

The working environment of the coal mine boom-type roadheader is harsh with large blind areas and numerous safety hazards for operators. Traditional on-site or remote control methods do not meet the requirements for intelligent tunneling. This paper proposes a digital twin monitoring system of an EBZ-type roadheader based on mixed reality (MR). First, the system integrates a five-dimensional digital twin model to establish the boom-type roadheader digital twin monitoring system. Second, the Unity3D software (v2020.3.25f1c1) and the MR Hololens (v22621.1133 produced by Microsoft) are used to build a digital twin human–machine interaction platform, achieving bidirectional mapping and driving of cutting operation data. Third, a twin data exchange program is designed by employing the Winform framework and the C/S communication architecture, making use of the socket communication protocol to transmit and store the cutting model data within the system. Finally, a physical prototype of the boom-type roadheader is built, and a validation experiment of the monitoring system’s digital twin is conducted. The experimental results show that the average transmission error of the cutting model data of the twin monitoring system is below 0.757%, and the execution accuracy error is below 3.7%. This system can achieve bidirectional real-time mapping and control between the twins, which provides a new monitoring method for actual underground roadheader operations. It effectively eliminates the operator’s blind areas and improves the intelligence level of roadheader monitoring. Beyond mining, this methodology can be extended to the monitoring and control of other mining equipment, predictive maintenance in manufacturing, and infrastructure management in smart cities. Full article

Rapid urbanisation has intensified the need for sustainable solutions to address challenges in urban infrastructure, climate change, and resource constraints. This study reveals that Artificial Intelligence (AI)-enabled metaverse offers transformative potential for developing sustainable smart cities. AI techniques, such as machine learning, deep learning, generative AI (GAI), and large language models (LLMs), enhance the metaverse’s capabilities in data analysis, urban decision making, and personalised user experiences. The study further examines how these advanced AI models facilitate key metaverse technologies such as big data analytics, natural language processing (NLP), computer vision, digital twins, Internet of Things (IoT), Edge AI, and 5G/6G networks. Applications across various smart city domains—environment, mobility, energy, health, governance, and economy, and real-world use cases of virtual cities like Singapore, Seoul, and Lisbon are presented, demonstrating AI’s effectiveness in the metaverse for smart cities. However, AI-enabled metaverse in smart cities presents challenges related to data acquisition and management, privacy, security, interoperability, scalability, and ethical considerations. These challenges’ societal and technological implications are discussed, highlighting the need for robust data governance frameworks and AI ethics guidelines. Future directions emphasise advancing AI model architectures and algorithms, enhancing privacy and security measures, promoting ethical AI practices, addressing performance measures, and fostering stakeholder collaboration. By addressing these challenges, the full potential of AI-enabled metaverse can be harnessed to enhance sustainability, adaptability, and livability in smart cities. Full article

Traditional engineering education cannot effectively respond to the demand for talents in the construction of smart cities. The application of digital twin technology in education is mostly based on case studies and lacks empirical tests. This study takes the practical teaching of a project-based course on smart city parks as an example to explore the action intention of graduate students to use digital twin technology consistently, and to provide a theoretical basis and teaching practice guidance to promote the rational application of digital twin technology in engineering education. This study set up a quasi-experimental design through the digital twin learning system, grouping 24 graduate students with 4 faculty members. The experimental group is digital twin-assisted practical teaching, and the control group is traditional teaching method, the experimental cycle is 12 weeks, and the total lesson time is 24 h. Secondly, combined with UTAUT2 model and TTF theory, the variable factor hypothesis was adopted as the scale design means, and the experimental validity was improved through questionnaire data analysis. Meanwhile, the influencing factors in the use of digital twin platform were recorded in detail through the process of data collection, data processing and modeling, as well as the application practice of digital twin platform. Finally, the results of the comprehensive survey data show that the graduate students in the experimental group are significantly better than the control group in terms of self-confidence, skill enhancement, learning outcomes, and learning experience. All these results provide information for course teaching practice, training professional teaching teams, optimizing innovative teaching paths, and promoting the cultivation and delivery of smart city technology talents. Full article

This paper presents COWIN^E (Community Twin Ecosystem), an ecosystem that harnesses Digital Twin (DT) to elevate and transform community resilience strategies. COWIN^E aims to enhance the disaster resilience of communities by fostering collaborative participation in the use of its DT among the decision-makers, the general public, and other involved stakeholders. COWIN^E leverages Cities:Skylines as its base simulation engine integrated with real-world data for community DT development. It is capable of capturing the dynamic, intricate, and interconnected structures of communities to provide actionable insights into disaster resilience planning. Through demonstrative, simulation-based case studies on Brevard County, Florida, the paper illustrates COWIN^E’s collaborative use with the involved parties in managing tornado scenarios. This study demonstrates how COWIN^E supports the identification of vulnerable areas, the execution of adaptive strategies, and the efficient allocation of resources before, during, and after a disaster. This paper further explores potential research directions using COWIN^E. The findings show COWIN^E’s potential to be utilized as a collaborative tool for community disaster resilience management. Full article

Citizen participation is key in urban planning, but traditional methods are often limited in terms of accessibility and inclusion. This study investigates how the use of emerging technologies such as Virtual and Augmented Reality (VR/AR), Digital Twin (DT), Building Information Modelling (BIM), Artificial Intelligence (AI), and Geographic Information Systems (GIS) can enhance citizen participation in urban planning. Through the review and analysis of existing literature, combined with the study of cases from cities in Eurasia and North America on the implementation of these technologies in urban and environmental planning, the results indicate that the use of multi-reality technologies facilitates immersive visualization of urban projects, allowing citizens to better understand the implications of proposed changes. Furthermore, the integration of real-time monitoring, such as forest and climate surveillance, improves environmental control. Technologies like AI and GIS also enable greater precision and empowerment in participatory decision-making. Nevertheless, the emergence of these technologies presents a challenge that must be addressed, as it is essential to establish a regulatory framework to ensure their responsible use. In conclusion, these platforms not only increase participation and co-creation but also enable more efficient, sustainable, and inclusive urban planning. Greater adoption of these technologies is suggested to optimize the urban decision-making process. Full article