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Digital Twin is becoming an increasingly powerful resource in the field of building production, replacing traditional processes in the Architecture, Engineering, Construction and Operations sector. This study is concerned with the development of a DT, enabled by Building Information Modeling, artificial intelligence, machine learning, and the Internet of Things to implement space management strategies. It proposes an application case for the Lazio Region headquarters, which has partly adopted smart working typology post-COVID-19. The aim is to create an accurate digital replica of the building based on BIM, integrated with real-time data. This will help to improve the use of space, the management of resources, and the quality of services provided to the community. It also improves energy efficiency, reducing consumption by 530.40 MWh per year and reducing greenhouse gas emissions by 641.32 tons of CO2 per year. The research proposes a holistic framework for the implementation of innovative solutions in the context of public infrastructure space management through the use of digital technology, facilitating the promotion of efficiency and sustainability in decision-making and operational processes through the application of a digital methodology. [ABSTRACT FROM AUTHOR]

Current research in nephrology is increasingly focused on elucidating the complexity inherent in tightly interwoven molecular systems and their correlation with pathology and related therapeutics, including dialysis and renal transplantation. Rapid advances in the omics sciences, medical device sensorization, and networked digital medical devices have made such research increasingly data centered. Data-centric science requires the support of computationally powerful and sophisticated tools able to handle the overflow of novel biomarkers and therapeutic targets. This is a context in which artificial intelligence (AI) and, more specifically, machine learning (ML) can provide a clear analytical advantage, given the rapid advances in their ability to harness multimodal data, from genomic information to signal, image and even heterogeneous electronic health records (EHR). However, paradoxically, only a small fraction of ML-based medical decision support systems undergo validation and demonstrate clinical usefulness. To effectively translate all this new knowledge into clinical practice, the development of clinically compliant support systems based on interpretable and explainable ML-based methods and clear analytical strategies for personalized medicine are imperative. Intelligent nephrology, that is, the design and development of AI-based strategies for a data-centric approach to nephrology, is just taking its first steps and is by no means yet close to its coming of age. These first steps are not even homogeneously taken, as a digital divide in access to technology has become evident between developed and developing countries, also affecting underrepresented minorities. With all this in mind, this editorial aim to provide a selective overview of the current use of AI technologies in nephrology and heralds the "Artificial Intelligence in Nephrology" special issue launched by BMC Nephrology. [ABSTRACT FROM AUTHOR]

Beyond 5G networks provide solutions for next-generation communications, especially digital twins networks (DTNs) have gained increasing popularity for bridging physical and digital space. However, current DTNs pose some challenges, especially when applied to scenarios that require efficient and multimodal data processing. Firstly, current DTNs are limited in communication and computational efficiency, since they require to transmit large amounts of raw data collected from physical sensors, as well as to ensure model synchronization through high-frequency computation. Second, current models of DTNs are domain-specific (e.g. E-health), making it difficult to handle DT scenarios with multimodal data processing requirements. Finally, current security schemes for DTNs introduce additional overheads that impair the efficiency. Against the above challenges, we propose a large language model (LLM) empowered DTNs framework, LLM-Twin. First, based on LLM, we propose digital twin semantic networks (DTSNs), which enable more efficient communication and computation. Second, we design a mini-giant model collaboration scheme, which enables efficient deployment of LLM in DTNs and is adapted to handle multimodal data. Then, we designed a native security policy for LLM-twin without compromising efficiency. Numerical experiments and case studies demonstrate the feasibility of LLM-Twin. To our knowledge, this is the first to propose an LLM-based semantic-level DTNs. [ABSTRACT FROM AUTHOR]

The concept of the Human Digital Twin (HDT) has recently emerged as a new research area within the domain of digital twin technology. HDT refers to the replica of a physical-world human in the digital world. Currently, research on HDT is still in its early stages, with a lack of comprehensive and in-depth analysis from the perspectives of universal frameworks, core technologies, and applications. Therefore, this paper conducts an extensive literature review on HDT research, analyzing the underlying technologies and establishing typical frameworks in which the core HDT functions or components are organized. Based on the findings from the aforementioned work, the paper proposes a generic architecture for the HDT system and describes the core function blocks and corresponding technologies. Subsequently, the paper presents the state of the art of HDT technologies and their applications in the healthcare, industry, and daily life domains. Finally, the paper discusses various issues related to the development of HDT and points out the trends and challenges of future HDT research and development. [ABSTRACT FROM AUTHOR]

In recent years, the detection performance of SAR-GMTI (synthetic aperture radar-ground moving target indication) algorithm based on deep learning has always been limited by insufficient measured data due to the heavy operation complexity and high cost of real SAR systems. To solve this problem, this paper proposes an overall DT-based implementation framework for SAR ground moving target intelligent detection tasks. In particular, by virtue of a SAR imaging algorithm, a high-fidelity twin replica of SAR moving targets is established in digital space through parameter traversal based on the prior target characteristics of the obtained measured datasets. Then, the constructed SAR twin datasets is fed into the neural network model to train an intelligent detector by fully learning features of the moving targets and preset the SAR scene in the twin space, which can realize the robust detection of ground moving targets in related practical scenarios with no need for multiple and complex field experiments. Moreover, the effectiveness of the proposed framework is verified on the MiniSAR measured system, and a comparison with traditional CFAR detection method is given simultaneously. [ABSTRACT FROM AUTHOR]

The current building industry is facing challenges of labor shortages and labor-intensive practices. Effectively collaborating with robots will be crucial for industry upgrading. This research introduces a MR iterative design and robot-assisted construction mode based on human–robot collaboration, facilitating an integrated process innovation from design to construction. The development of the ROCOS (Robot Collaboration System) comprises three key aspects: (1) Layout Stage: using MR technology to layout the site, forming a full-scale integrated virtual and physical digital twin design environment. (2) Design Stage: conducting virtual iterative design in the digital twin environment and automatically simulating assembly processes. (3) Assembly Stage: translating simulated results into assembly path commands and driving a robotic arm to perform actual assembly. In the end, this research setup two experiments to examine the feasibility of this iterative design–construction loop script. The results showed that although the presence of obstacles reduced the designer's freedom and increased the number of steps, the designer could still finish both tasks. This means that the ROCOS has value in the prototype of human–robot collaboration. In addition, some valuable findings from users' feedback showed that potential improvements can be addressed in operability, customization, and real construction scenarios. [ABSTRACT FROM AUTHOR]

This research examines the potential of digital twin (DT) technology for reformation within China's traditional solid-wood-panel processing industry, which currently suffers from production inefficiencies and the slow adoption of digital technology. The research centers around developing a digital twin system, elucidating improvements in manufacturing efficiency, waste management, process simulation, and real-time monitoring. These capabilities facilitate immediate problem solving and offer transparency in the process. The digital twin system is comprised of physical, transport, virtual, and application layers, employing a MySQL database and using the Open Platform Communications Unified Architecture (OPC UA) protocol for communication. The application of this system has led to heightened production efficiency and better material use in the solid-wood-panel manufacturing line. Integrating the dynamic selection adaptive genetic algorithm (DSAGA) into the virtual layer drives the system's efficiency forward. This evolved approach has allowed for an enhancement of 8.93% in the scheduling efficiency of DSAGA compared to traditional genetic algorithms (GAs), thereby contributing to increased system productivity. Real-time mapping and an advanced simulation interface have strengthened the system's monitoring aspect. These additions enrich data visualization, leading to better comprehension and a holistic process view. This research has ignited improvements in solid-wood-panel production, illustrating the tangible benefits and representing progress in incorporating digital technology into traditional industries. This research sets a path for transforming these industries into smart manufacturing by effectively bridging the gap between physical production and digital monitoring. Furthermore, the adjustability of this approach extends beyond solid-wood-panel production, indicating the capability to expedite movement towards intelligent production in various other manufacturing sectors. [ABSTRACT FROM AUTHOR]

Aiming at the problems of data integration and information sharing in the calibration workshop of smoke alarm, combining OPC Unified Architecture (OPC UA) and digital twin technology, the OPC UA based digital twin smoke alarm calibration workshop information network framework is proposed. Using OPC UA information modeling technology, a digital workshop information model for smoke alarm calibration was constructed, and the static attribute set, process attribute set, and functional component set of the workshop were defined in detail. Taking the smoke box as an example, the information model construction was instantiated, and data collection and transmission were carried out with the digital twin model of the smoke box, achieving the implementation mapping between the twin model and physical entities. The results show that based on the proposed OPC UA information model, the interconnection and intercommunication of data at all levels of the digital twin smoke alarm calibration workshop can be achieved, and the workshop can be visualized, digitized, and flexible. [ABSTRACT FROM AUTHOR]

A healthcare-focused version of the Internet of Things (IoT), the Internet of Medical Things (IoMT) enables real-time monitoring and remote medical support via integrated medical devices, programs, and support solutions. However, patients' safety and anonymity are in jeopardy by the open access networks employed in IoMT, which makes the systems susceptible to several threats and security lapses. By harnessing the synergies of blockchain, cloud computing, and digital twins, this study presents a comprehensive architecture and a secure lightweight authentication mechanism (which integrates the benefits offered by Yu and Park, Yu et al., and Amintoshi et al.) that addresses these concerns. The suggested method entails using session keys for secure communication while authenticating medical professionals and patients through a gateway. Cloud computing offers a flexible and robust framework for managing and storing medical data. Additionally, it simulates digital twins to enable data-driven decision-making and predictive analysis, and the incorporation of blockchain offers a decentralized and immutable ledger for recording and validating patient data and transaction logs enhancing data integrity, transparency, and traceability. Healthcare systems may confidently embrace the potential of IoMT by implementing this framework since it offers promising solutions to enhance the security and confidentiality of patient data in IoMT while supporting the provision of the best healthcare services, especially in emergency scenarios like the COVID-19 pandemic. The suggested approach is subjected to a thorough security evaluation using AVISPA, demonstrating its resistance to various attacks. A comparative analysis has also been carried out to assess the performance and computational cost of IoMT-BADT in comparison with other authentication schemes. [ABSTRACT FROM AUTHOR]

Energy performance-based contracts (EPCs) offer a promising solution for enhancing the energy performance of buildings, which is an overarching step towards achieving Net Zero Carbon Buildings, addressing climate change and improving occupants' comfort. Despite their potential, their execution is constrained by difficulties that hinder their diffusion in the architecture, engineering, construction, and operation industry. Notably, the Measurement and Verification process is considered a significant impediment due to data sharing, storage, and security challenges. Nevertheless, there have been minimal efforts to analyze research conducted in this field systematically. A systematic analysis of 113 identified journal articles was conducted to fill this gap. A paucity of research tackling the utilization of digital technologies to enhance the implementation of EPCs was found. Consequently, this article proposes a framework integrating Digital Twin and Blockchain technologies to provide an enhanced EPC execution environment. Digital Twin technology leverages the system by monitoring and evaluating energy performance in real-time, predicting future performance, and facilitating informed decisions. Blockchain technology ensures the integrity, transparency, and accountability of information. Moreover, a private Blockchain infrastructure was originally introduced in the framework to eliminate high transaction costs related to on-chain storage and potential concerns regarding the confidentiality of information in open distributed ledgers. [ABSTRACT FROM AUTHOR]

Digital twin technology, a new type of digital technology emerging in recent years, realizes real-time simulation, prediction and optimization by digitally modeling the physical world, providing a new idea and method for the design, operation and management of water conservancy projects, which is of great significance for the realization of the transformation of water conservancy informatization to intelligent water conservancy. In view of this, this paper systematically discusses the concept and development history of digital twin smart water conservancy, compares its differences with traditional water conservancy models, and further proposes the digital twin smart water conservancy five-dimensional model. Based on the five-dimensional model of digital twin water conservancy, the research progress of digital twin smart water conservancy is summarized by focusing on six aspects, namely digital twin water conservancy data perception, data transmission, data analysis and processing, digital twin water conservancy model construction, digital twin water conservancy interaction and collaboration and digital twin water conservancy service application, and the challenges and problems of digital twin technology in the application of smart water conservancy. Finally, the development trend of digital twin technology and the direction of technological breakthroughs are envisioned, aiming to provide reference and guidance for the research on digital twin technology in the field of smart water conservancy and to promote the further development of the field. [ABSTRACT FROM AUTHOR]

This research paper presents the development and analysis of a multifaceted smart grid prototype. It combines various technologies for the smart grid operation. The first technology is environmental analysis of smart grid and solar panel cleaning. Secondly, radio-frequency identification (RFID)-based security and access control system has been integrated for smart grid. The third component is internet of things (IoT)-based energy monitoring and load management. For environmental analysis sensors such as temperature, humidity, light-dependent resistor, and flame sensors are connected to a NodeMCU controller for real time monitoring. Moreover, IoT based solar cleaning system is developed in the form of prototype with the help of Blynk and servo motor. The second component of prototype is smart security system which is developed with the help of Arduino and RFID module to facilitate secure access control. The third part of prototype employs voltage and current sensors with an ESP32 microcontroller and the Blynk application for real-time energy consumption analysis. This setup enables remote monitoring of voltage, power dynamics, and consumption patterns in a smart grid. It also offers an IoT based solution for load management and load shedding within the smart grid. The complete prototype overall demonstrates a comprehensive approach to 1) smart grid management, 2) environmental analysis, 3) security, and 4) energy monitoring. [ABSTRACT FROM AUTHOR]

Remotely operated vehicles (ROVs) provide practical solutions for a wide range of activities in a particularly challenging domain, despite their dependence on support ships and operators. Recent advancements in AI, machine learning, predictive analytics, control theories, and sensor technologies offer opportunities to make ROVs (semi) autonomous in their operations and to remotely test and monitor their dynamics. This study moves towards that goal by formulating a complete navigation, guidance, and control (NGC) system for a six DoF BlueROV2, offering a solution to the current challenges in the field of marine robotics, particularly in the areas of power supply, communication, stability, operational autonomy, localization, and trajectory planning. The vehicle can operate (semi) autonomously, relying on a sensor acoustic USBL localization system, tethered communication with the surface vessel for power, and a line of sight (LOS) guidance system. This strategy transforms the path control problem into a heading control problem, aligning the vehicle's movement with a dynamically calculated reference point along the desired path. The control system uses PID controllers implemented in the navigator flight controller board. Additionally, an infrastructure has been developed that synchronizes and communicates between the real ROV and its digital twin within the Unity environment. The digital twin acts as a visual representation of the ROV's movements and considers hydrodynamic behaviors. This approach combines the physical properties of the ROV with the advanced simulation and analysis capabilities of its digital counterpart. All findings were validated at the Point Rouge port located in Marseille and at the port of Ancona. The NGC implemented has proven positive vehicle stability and trajectory tracking in time despite external interferences. Additionally, the digital part has proven to be a reliable infrastructure for a future bidirectional communication system. [ABSTRACT FROM AUTHOR]

Recently, advancements in digital twin and extended reality (XR) technologies, along with industrial control systems (ICSs), have driven the transition to Industry 5.0. Digital twins mimic and simulate real-world systems and play a crucial role in various industries. XR provides innovative user experiences through virtual reality (VR), augmented reality (AR), and mixed reality (MR). By integrating digital twin simulations into XR devices, these technologies are utilized in various industrial fields. However, the prevalence of XR devices has increased the exposure to cybersecurity threats in ICS and digital twin environments. Because XR devices are connected to networks, the control and production data they process are at risk of being exposed to cyberattackers. Attackers can infiltrate XR devices through malicious code or hacking attacks to take control of the ICS or digital twin or paralyze the system. Therefore, this study emphasizes the cybersecurity threats in the ecosystem of XR devices used in ICSs and conducts research based on digital forensics. It identifies potentially sensitive data and artifacts in XR devices and proposes secure and reliable security response measures in the Industry 5.0 environment. [ABSTRACT FROM AUTHOR]

In recent years, the integration of digital technologies has grown rapidly in the field of thermal comfort and energy efficiency for buildings. The concept of a digital twin, incorporating multiple digital technologies, has gained increasing attention. The literature lacks a review of the digital twin concept in thermal comfort and energy consumption for existing buildings. This paper conducts a review of the current state-of-the-art in digital twin (DT) technology for thermal comfort/ energy consumption in buildings. The review employs a scientometric approach and examines various technologies used in creating DTs and a systematic analysis of the methods, technologies, algorithms, and approaches used in digital twin experiments. The results show a growing number of studies in this area, with a focus on thermal comfort monitoring, visualization, tracking, energy management, prediction, and optimization for existing buildings. Furthermore, the prediction of energy consumption using algorithms such as Artificial Neural Networks (ANN), Artificial Intelligence (AI), deep neural networks, and YOLOv4 have been used in buildings. However, the wider adoption of a DT that can facilitate occupants, and thermal sensations, enhance human-centered solutions, and improve energy prediction levels are necessary. There is a need for further international collaboration to expand the studies on digital twins for thermal comfort and energy efficiency. The review highlights the limitations and areas of improvement, such as the limited adoption of sensors for environmental measures, the need for more focus on the subjective perception of occupants, and the need for more comparative studies of algorithms for predicting energy consumption. Further studies can be conducted in areas such as understanding occupant psychological responses/behaviors to comfort in the digital world. This will enhance a more consolidated and robust validation for building performance. [ABSTRACT FROM AUTHOR]

Applying digital technologies in education has been proven to have several positive impacts on teaching and learning. Digital twins have the potential to revolutionize education by offering immersive and interactive learning experiences. They can simulate virtual environments, enabling students to conduct experiments, practice techniques, and observe outcomes in a safe and cost-effective setting, particularly when access to physical laboratories is limited or expensive. This work proposes a learning management model that applies cognitive apprenticeship learning theory as a framework for learning digital twins to enhance the programming of a smart agriculture system and computational thinking skills. The model consists of three parts: input, learning process, and output with feedback. The input phase includes setting learning objectives, analyzing learners, determining learning content, planning learning activities, and utilizing a digital twin virtual laboratory space for experimenting in the development of a smart agriculture system. The learning process involves engaging students in programming and problem-solving tasks by offering expert guidance and support in computationally solving problems. The achievement of learning outcomes and practices is included in the output and feedback sections. After evaluation, five experts in this subject agreed that the learning management model was at the most appropriate level. [ABSTRACT FROM AUTHOR]

One of the goals of developing and implementing Industry 4.0 solutions is to significantly increase the level of flexibility and autonomy of production systems. It is intended to provide the possibility of self-reconfiguration of systems to create more efficient and adaptive manufacturing processes. Achieving such goals requires the comprehensive integration of digital technologies with real production processes towards the creation of the so-called Cyber–Physical Production Systems (CPPSs). Their architecture is based on physical and cybernetic elements, with a digital twin as the central element of the "cyber" layer. However, for the responses obtained from the cyber layer, to allow for a quick response to changes in the environment of the production system, its virtual counterpart must be supplemented with advanced analytical modules. This paper proposes the method of creating a digital twin production system based on discrete simulation models integrated with deep reinforcement learning (DRL) techniques for CPPSs. Here, the digital twin is the environment with which the reinforcement learning agent communicates to find a strategy for allocating processes to production resources. Asynchronous Advantage Actor–Critic and Proximal Policy Optimization algorithms were selected for this research. [ABSTRACT FROM AUTHOR]

To address the challenges posed by global climate change, developing green energy systems characterized by informatization, digitalization, and intelligence is crucial for achieving carbon neutrality. This article is a research report type paper on water source heat pump (WSHP) energy stations, aiming to use digital twin technology and other information technologies to resolve conflicts between clean energy development and efficient energy utilization. The primary objective of this study is to identify and analyze issues in traditional energy station operations and management systems. Based on this analysis, specific technical solutions are proposed, including pathways for technological research, methodologies, and content. The results provide a comprehensive theoretical framework for the intelligent transformation of energy station systems and essential technical support for the WSHP energy station project in the Hankou Binjiang International Business District. The findings have significant implications for the widespread adoption of WSHP energy stations and the achievement of national carbon neutrality goals. [ABSTRACT FROM AUTHOR]

Musical cultural heritage, as an important component of cultural heritage, possesses significant cultural value and inheritance significance. With the development of society and the passage of time, these precious traditional musical cultural heritages inevitably face the dilemma of gradual depletion or even disappearance. In the digital age, effectively protecting and inheriting these musical cultural heritages has become an urgent problem to be addressed. Therefore, this paper proposes an application method based on digital twin technology, exploring how to protect and inherit musical cultural heritages through digital twin technology. By leveraging digital twin technology, a virtual museum dedicated to showcasing the richness and historical connotations of music cultures is created, preserving and simulating the soundscapes of historical music eras. Through the integration of audio archives, 3D modeling, and interactive displays, users can immerse themselves in the experience of historical music in the digital space. This paper evaluates the feasibility and cultural preservation value of this digital music history museum through the creation of music digital twin technology instances and user survey feedback and discusses the prospects of digital twins in the field of musical cultural heritage. [ABSTRACT FROM AUTHOR]

Over the last decade, the digital twin (DT) concept has effectively revolutionized conventional bridge monitoring and management. Despite their overall success, current bridge DTs encounter conceptual ambiguities, hindering their inherent potential for practical implementation. Moreover, intelligent decision support models have not been properly considered as a component of the bridge DTs framework to enhance the reliability of decisions for asset maintenance. Therefore, this paper conducts a scientometric analysis and a comprehensive state-of-the-art review, exploring current bridge DT research trends and architectures and introducing an enhanced conceptual framework for bridge DTs. To this end, more than 480 research publications have been reviewed, compared, and analyzed. The research result encompasses the redevelopment of a multilayer DT framework, fostering its implementation in the full lifecycle of bridge infrastructure while exploring the potential integration of decision support systems and data fusion from advanced technologies to improve the overall efficiency of implementing DT technology in bridges. [ABSTRACT FROM AUTHOR]

With the rapid development of underground space, the issue of safety in construction processes is becoming more and more significant. The purpose of this paper is to solve the problem of the existing underground space monitoring technology not being centralized and unified. In view of the problems related to large underground spaces in the process of constructing complex structures, with the introduction of Internet of Things technology and digital twins, we put forward an application of an intelligent safety-monitoring digital twin system in the construction of a large underground space structure, and at the same time, explore the Internet and digital integration mechanism of the twin system. The twin system uses BIM technology to establish the corresponding physical construction model, and collects multi-source heterogeneous monitoring data in real time through Internet of Things technology to achieve the exchange of information between the virtual construction model and the physical construction model. The twin system uses the multi-source heterogeneous data for real-time security analysis, and obtains the security status of the structure and feeds it back to the application service layer. The effectiveness and practicability of the twin system in large underground spaces are verified by an example project. Aiming at the safe performance of the orthogonal arch, the mapping relationship of various parameter indexes is obtained, and reasonable control measures are given. This study provides a new solution for improving the safety of construction projects and risk prevention and control, and has important theoretical and practical value for the safety management of underground space construction processes. [ABSTRACT FROM AUTHOR]

This research aims to develop an integrated approach to construction project management by integrating digital technology into monitoring and surveillance operations. Through the use of drones and image processing software, data can be updated regularly and accurately about the progress at the construction site, allowing managers and decision makers to have a clear view of the current situation and make effective decisions based on accurate. In addition, this approach contributes to improving communication and coordination among project team members, as data and images can be easily and effectively shared, reducing opportunities for error and enhancing effective interaction among different parties. Using digital twin technologies, planning and forecasting processes can also be improved, as comprehensive analysis of digital data provides a deeper understanding of project dynamics, identifies potential risks, and enables appropriate preventive measures to be taken. In conclusion, the integration of digital twins and the use of drones in construction projects represent a significant step towards achieving smarter and more efficient management, and successfully achieving the defined goals with greater effectiveness. [ABSTRACT FROM AUTHOR]

The recent rise in the applications of advanced technologies in the sustainable design and construction of transportation infrastructure demands an appropriate medium for their integration and utilization. The relatively new concept of Civil Integrated Management (CIM) is such a medium; it enhances the development of digital twins for infrastructure and also embodies various practices and tools, including the collection, organization, and data-management techniques of digital data for transportation infrastructure projects. This paper presents a comprehensive analysis of advanced CIM tools and technologies and categorizes its findings into the following research topics: application of advanced surveying methods (Advanced Surveying); geospatial analysis tools for project planning (Geospatial Analysis); multidimensional virtual design models (nD Modeling); Integrated Geospatial and Building Information Modeling (GeoBIM); and transportation infrastructure maintenance and rehabilitation planning (Asset Management). Despite challenges such as modeling complexity, technology investment, and data security, the integration of GIS, BIM, and artificial intelligence within asset-management systems hold the potential to improve infrastructure's structural integrity and long-term performance through automated monitoring, analysis, and predictive maintenance during its lifetime. [ABSTRACT FROM AUTHOR]

This paper delves into the integration of Factories of the Future (FoF) and digital twin technologies within urban contexts, marking a significant leap in Smart Cities development. We present a thorough exploration of the principles and a scientifically grounded framework designed for seamlessly blending advanced manufacturing systems with the urban environment's digital and physical aspects. Our detailed analysis has identified core principles crucial for this integration, focusing on interoperability, sustainability, adaptability, stakeholder collaboration, and strong data governance. We propose a structured framework that puts these principles into action, outlining strategic routes for incorporating digital twin and Building Information Modeling (BIM) technologies into FoF, establishing public-private partnerships, enhancing education and workforce development, and setting up mechanisms for ongoing evaluation and enhancement. The potential of this integration to transform urban development is vast, providing a model for boosting operational efficiency, driving economic growth, and enhancing urban livability. Although challenges exist in realizing this vision, our research offers practical insights and strategies for cities and industries to effectively navigate the complexities of the digital era. This contribution enriches the growing field of urban science, advocating for a harmonious integration of industrial production with urban development in the Smart Cities framework. [ABSTRACT FROM AUTHOR]

Implementing circular economy principles in the built environment significantly reduces raw material extraction and generated waste. A transition is essential, especially as it accounts for 40-60% of global raw material extraction and 33% of total waste in the European Union. This paper shows the methodological approach to generating a digital Building model to enable circular economy in the early design stage by coupling Generative Design methods, enabling automated variant generation of Digital building models, Building Information Modelling objects, and assessment algorithms to evaluate the environmental impact and circularity. By visualising the assessments of the generated variants of (so-called) Circular Twins in a Virtual Reality environment, including user interaction with a voice-controlled Virtual Agent, the digital Circular Twin ecosystem enables the early implementation of the goals of the circular economy and sustainability in Architecture, Engineering and Construction. [ABSTRACT FROM AUTHOR]

Digital twin technology is expected to transform agriculture. By creating the virtual representation of a physical entity, it assists food producers in monitoring, predicting, and optimizing the production process remotely and even autonomously. However, the progress in this area is relatively slower than in industries like manufacturing. A systematic investigation of agricultural digital twins' current status and progress is imperative. With seventy published papers, this work elaborated on the studies targeting agricultural digital twins from overall trends, focused areas (including domains, processes, and topics), reference architectures, and open questions, which could help scholars examine their research agenda and support the further development of digital twins in agriculture. [ABSTRACT FROM AUTHOR]

Digital twins aim to optimize practices implemented in various sectors by bridging the gap between the physical and digital worlds. Focusing on open-field agriculture, livestock farming, and forestry and reviewing the current applications in these domains, this paper reveals the multifaceted roles of digital twins. Diverse key aspects are examined, including digital twin integration and maturity level, means of data acquisition, technological capabilities, and commonly used input and output features. Through the prism of four primary research questions, the state of the art of digital twins, the extent of their achieved integration, and an overview of the critical issues and potential advancements are provided in the landscape of the sectors under consideration. The paper concludes that in spite of the remarkable progress, there is a long way towards achieving full digital twin. Challenges still persist, while the key factor seems to be the integration of expert knowledge from different stakeholders. In light of the constraints identified in the review analysis, a new sector-specific definition for digital twins is also suggested to align with the distinctive characteristics of intricate biotic and abiotic systems. This research is anticipated to serve as a useful reference for stakeholders, enhancing awareness of the considerable benefits associated with digital twins and promoting a more systematic and comprehensive exploration of this transformative topic. [ABSTRACT FROM AUTHOR]

There currently exists a need for a platform that provides users with the ability to perform extensive, repeatable and meaningful simulation and testing for the hardware and software which compose vehicle/autonomous vehicle systems whilst being broadly accessible, widely supported and provides robust features and development tools. The contemporary implementations of similar systems are either financially exorbitant or highly contained. The system reflected in this paper aims to fill a gap in the industry of vehicle/autonomous vehicle development by extending on currently existing open-source software to provide a highly streamlined platform to support the production of general road vehicle and autonomous vehicle driving systems. The software tools and hardware components chosen for the system will be discussed, followed by the features constructed throughout the development process. The end result of the system is a platform that allows for quick, repeatable, accurate, and nearly endless testing of a digital twin of real life vehicles. This system will allow users to gain valuable simulation and testing data of hardware and software components in a manner which is not always feasible using the traditional methods of autonomous vehicle testing. [ABSTRACT FROM AUTHOR]

Gear factories are most typical discrete manufacturing factories. Many gear factories are striving to explore how to achieve intelligent manufacturing in order to improve efficiency and reduce costs. Digital twin technology is currently one of the most reliable ways to achieve intelligent manufacturing. This article aims to establish a real-time control system in order to promote intelligent manufacturing for discrete manufacturing factories. Firstly, a model for a digital twin gear factory is put forward based on the characteristics of gear factories, and the composition of a real-time control system for gear factories is clarified. Then, a human–computer interaction architecture for the real-time control system is proposed. The real-time control system consists of three parts as follows: a monitoring module, a virtualizing module, and a controlling module. At work, it appears as a kind of human–machine interaction form with the three following interfaces: a monitoring window, a virtualizing window, and a controlling window. Finally, a gear factory, which is specialized in manufacturing the intermediate shaft dual gear of a new energy vehicle gearbox, develops a set of software for the real-time control system. The prototype software is obtained through some development activities such as 3D MAX and WebGL virtualization modeling and OPC UA and REST communication design. [ABSTRACT FROM AUTHOR]

This paper explores the integration of intelligent digital twin technology with PID regulators in industrial process control utilizing smart meter data. It presents a novel approach involving the creation of mathematical models to simulate real-time system behavior, thereby enhancing the PID control loop. The focus is on the development of specialized IT infrastructure to support this integration, which includes data acquisition, processing, and control optimization. This integration aims to not only improve control system efficiency but also introduce a robust predictive maintenance framework, offering significant benefits across a wide range of industrial applications. [ABSTRACT FROM AUTHOR]

Developing guidelines for designing robot-inclusive spaces has been challenging and resource-intensive, primarily relying on physical experiments and observations of robot interactions within the built environment. These conventional methods are often costly, time-consuming, and labour-intensive, demanding manual intervention. To address these limitations, this study explores the potential of using digital twins as a promising solution to offer detailed insights, reducing the dependence on physical experiments for studying robot-built environment interactions.Although the concept of digital twins is popular in many domains, the use of digital twins for this specific problem has not been explored yet. A novel methodology for assessing existing built environment guidelines by incorporating them as an architectural digital twin asset within robot simulation software is proposed in this regard. By analysing the digital interactions between robots and the architectural digital twin assets in simulations, the compatibility of the environment with robots is evaluated, ultimately contributing to enhancing these guidelines to be robot-inclusive. The ultimate goal is to create environments that are not only inclusive but also readily accessible to Autonomous Mobile Robots (AMRs). With this objective, the proposed methodology is tested on robots of different specifications to understand the robots' interactions with different architectural digital twin environments and obstacles. The digital twin effectively demonstrates the capability of the proposed approach in assessing the robots' suitability for deployment in the simulated environments. The gained insights contribute to improved comprehension and strengthen the existing design guidelines. [ABSTRACT FROM AUTHOR]

This comprehensive review explores the application and impact of digital twin (DT) technology in bolstering the reliability of Floating Offshore Wind Turbines (FOWTs) and their supporting platforms. Within the burgeoning domain of offshore wind energy, this study contextualises the need for heightened reliability measures in FOWTs and elucidates how DT technology serves as a transformative tool to address these concerns. Analysing the existing scholarly literature, the review encompasses insights into the historical reliability landscape, DT deployment methodologies, and their influence on FOWT structures. Findings underscore the pivotal role of DT technology in enhancing FOWT reliability through real-time monitoring and predictive maintenance strategies, resulting in improved operational efficiency and reduced downtime. Highlighting the significance of DT technology as a potent mechanism for fortifying FOWT reliability, the review emphasises its potential to foster a robust operational framework while acknowledging the necessity for continued research to address technical intricacies and regulatory considerations in its integration within offshore wind energy systems. Challenges and opportunities related to the integration of DT technology in FOWTs are thoroughly analysed, providing valuable insights into the role of DTs in optimising FOWT reliability and performance, thereby offering a foundation for future research and industry implementation. [ABSTRACT FROM AUTHOR]

This study uses a wind turbine case study as a subdomain of Industrial Internet of Things (IIoT) to showcase an architecture for implementing a distributed digital twin in which all important aspects of a predictive maintenance solution in a DT use a fog computing paradigm, and the typical predictive maintenance DT is improved to offer better asset utilization and management through real-time condition monitoring, predictive analytics, and health management of selected components of wind turbines in a wind farm. Digital twin (DT) is a technology that sits at the intersection of Internet of Things, Cloud Computing, and Software Engineering to provide a suitable tool for replicating physical objects in the digital space. This can facilitate the implementation of asset management in manufacturing systems through predictive maintenance solutions leveraged by machine learning (ML). With DTs, a solution architecture can easily use data and software to implement asset management solutions such as condition monitoring and predictive maintenance using acquired sensor data from physical objects and computing capabilities in the digital space. While DT offers a good solution, it is an emerging technology that could be improved with better standards, architectural framework, and implementation methodologies. Researchers in both academia and industry have showcased DT implementations with different levels of success. However, DTs remain limited in standards and architectures that offer efficient predictive maintenance solutions with real-time sensor data and intelligent DT capabilities. An appropriate feedback mechanism is also needed to improve asset management operations. [ABSTRACT FROM AUTHOR]

In the era of intelligence and automation, robots play a significant role in the field of automated welding, enhancing efficiency and precision. However, challenges persist in scenarios demanding complexity and higher precision, such as low welding planning efficiency and inaccurate weld seam defect detection. Therefore, based on digital twin technology and kernel correlation filtering algorithm, a welding tracking model is proposed. Firstly, the kernel correlation filtering algorithm is used to train the filter on the first frame of the collected image, determine the position of image features in the region, extract histogram features of image blocks, and then train the filter using ridge regression to achieve welding trajectory tracking. Additionally, an intelligent weld seam detection model is introduced, employing a backbone feature network for feature extraction, feature fusion through a feature pyramid, and quality detection of weld seams through head classification. During testing of the tracking model, the maximum tracking error is -0.232 mm, with an average absolute tracking error of 0.08 mm, outperforming other models. Comparatively, in tracking accuracy, the proposed model exhibits the fastest convergence with a precision rate of 0.845, surpassing other models. In weld seam detection, the proposed model excels with a detection accuracy of 97.35% and minimal performance loss at 0.023. In weld seam quality and melt depth error detection, the proposed model achieves errors within the range of -0.06 mm, outperforming the other two models. These results highlight the outstanding detection capabilities of the proposed model. The research findings will serve as technical references for the development of automated welding robots and welding quality inspection. [ABSTRACT FROM AUTHOR]

Automated disassembly is increasingly in focus for Recycling, Re-use, and Remanufacturing (Re-X) activities. Trends in digitalization, in particular digital twin (DT) technologies and the digital product passport, as well as recently proposed European legislation such as the Net Zero and the Critical materials Acts will accelerate digitalization of product documentation and factory processes. In this contribution we look beyond these activities by discussing digital information for stakeholders at the Re-X segment of the value-chain. Furthermore, we present an approach to automated product disassembly based on different levels of available product information. The challenges for automated disassembly and the subsequent requirements on modeling of disassembly processes and product states for electronic waste are examined. The authors use a top-down (e.g., review of existing standards and process definitions) methodology to define an initial data model for disassembly processes. An additional bottom-up approach, whereby 5 exemplary electronics products were manually disassembled, was employed to analyze the efficacy of the initial data model and to offer improvements. This paper reports on our suggested informal data models for automatic electronics disassembly and the associated robotic skills. [ABSTRACT FROM AUTHOR]

This research leverages the potential of digital twin technology to present a novel method of predictive maintenance for 6G communication networks. With the increasing need for 6G networks to be consistently reliable and always available, this essay emphasises the need for more advanced maintenance techniques. The seamless integration of digital twin technology makes it possible to monitor, analyse, and simulate the 6G network in real time. This innovation makes it easier to use artificial intelligence to fine-tune maintenance schedules and anticipate maintenance requirements in advance. The paper fervently promotes the use of digital twin technology as a vital instrument for raising the bar for maintenance requirements in the context of 6G networks. [ABSTRACT FROM AUTHOR]

Digital Twins allow for designing, operating, and optimizing running systems by replicating the operations of the physical components in a digital environment. Interconnectivity between Digital Twin models and corresponding real-world counterparts facilitates continuous examining of the actual system, resulting in nearly real-time analysis and decision-making support. Validation is an integral part of Digital Twins as underlying models must accurately reflect the corresponding physical systems according to predefined objectives. The near real-time nature of Digital Twins demands a continuous validation process to ensure models' accuracy. Labor-intensive manufacturing, where humans are at the heart of manufacturing processes, is a sector that encompasses a wide range of industries, from toys and apparel to medical devices and automotive components. This sector continues to play a vital role in emerging economies, offering employment opportunities that mitigate poverty and enhance social stability. Enabling Digital Twins for labor-intensive manufacturing systems opens many opportunities towards humancentricity and improvement of well-being of human operators. In these systems, however, human data must also be considered for Digital Twin development and the corresponding validation processes. Handling human data further complicates the creation and validation of Digital Twins. To the best of our knowledge, there has not been a comprehensive study on the validation of Digital Twins in labor-intensive manufacturing. In this paper, we review Digital Twin validation in manufacturing, focusing on systems that feature data from human operations. As a result, we outline the current challenges of validation of Digital Twins in labor-intensive manufacturing environments and suggest future research directions. [ABSTRACT FROM AUTHOR]

This paper describes the use of Internet of Things (IoT) technologies, digital twins (DT), and augmented reality (AR) to raise awareness and disseminate the use of digital services within the INICTEL-UNI institutional project financed by the Inter-American Development Bank to strengthen technological services and satisfy the technological needs of companies, promoting digital transformation in Peru. Within various fields, such as technical education, construction, and manufacturing, challenges are faced related to the adoption of advanced technologies and the need to improve efficiency. The main objective of this paper is to implement an IoT control and visualization system with DT and AR in a digital transformation space. A system is shown to create a technological demonstrator environment that visualizes and monitors sensor data on physical IoT devices in real time, allowing users to interact and operate them through an ESP32 module with data transmission with the MQTT protocol and an AR application developed in Unity and Vuforia. The study results successfully demonstrated the efficiency of real-time communication between the IoT device and the AR application, as well as the efficient ability to perform tasks, validated by users with no prior experience. [ABSTRACT FROM AUTHOR]

Owing to global warming and pollution concerns, reducing the environmental footprint of the textile and fashion industry has received considerable attention. Within this industry, the dyeing and finishing processes contribute significantly to greenhouse gas emissions and water pollution. This study introduces an innovative approach to address these challenges by leveraging digital twin technology to optimize the textile dyeing process. A smart analysis module was developed to continuously monitor and analyze the dyeing parameters in real time to implement control actions to automatically reduce the process duration. Integrated with this module, a digital twin of the dyeing machine enabled the real-time monitoring of energy consumption and process parameters. A case study comparing the traditional dyeing process with the optimized process was conducted. The results showed that dyeing time was reduced by ~ 17.5% without compromising dyeing quality. Energy consumption and greenhouse gas emissions were also reduced by ~ 12.1% when using the optimized process. This study offers a practical and sustainable option for textile dyeing, particularly for small and medium-sized enterprises. [ABSTRACT FROM AUTHOR]

The rapid development of digitalization, the Internet of Things (IoT), and Industry 4.0 has led to the emergence of the digital twin concept. IoT is an important pillar of the digital twin. The digital twin serves as a crucial link, merging the physical and digital territories of Industry 4.0. Digital twins are beneficial to numerous industries, providing the capability to perform advanced analytics, create detailed simulations, and facilitate informed decision-making that IoT supports. This paper presents a review of the literature on digital twins, discussing its concepts, definitions, frameworks, application methods, and challenges. The review spans various domains, including manufacturing, energy, agriculture, maintenance, construction, transportation, and smart cities in Industry 4.0. The present study suggests that the terminology "3 dimensional (3D) digital twin" is a more fitting descriptor for digital twin technology assisted by IoT. The aforementioned statement serves as the central argument of the study. This article advocates for a shift in terminology, replacing "digital twin" with "3D digital twin" to more accurately depict the technology's innate potential and capabilities in Industry 4.0. We aim to establish that "3D digital twin" offers a more precise and holistic representation of the technology. By doing so, we underline the digital twin's analytical ability and capacity to offer an intuitive understanding of systems, which can significantly streamline decision-making processes using the digital twin. [ABSTRACT FROM AUTHOR]

Carbon emissions present a pressing challenge to the traditional construction industry, urging a fundamental shift towards more sustainable practices and materials. Recent advances in sensors, data fusion techniques, and artificial intelligence have enabled integrated digital technologies (e.g., digital twins) as a promising trend to achieve emission reduction and net-zero. While digital twins in the construction sector have shown rapid growth in recent years, most applications focus on the improvement of productivity, safety and management. There is a lack of critical review and discussion of state-of-the-art digital twins to improve sustainability in this sector, particularly in reducing carbon emissions. This paper reviews the existing research where digital twins have been directly used to enhance sustainability throughout the entire life cycle of a building (including design, construction, operation and maintenance, renovation, and demolition). Additionally, we introduce a conceptual framework for this industry, which involves the elements of the entire digital twin implementation process, and discuss the challenges faced during deployment, along with potential research opportunities. A proof-of-concept example is also presented to demonstrate the validity of the proposed conceptual framework and potential of digital twins for enhanced sustainability. This study aims to inspire more forward-thinking research and innovation to fully exploit digital twin technologies and transform the traditional construction industry into a more sustainable sector. [ABSTRACT FROM AUTHOR]

Historical sites are incomplete relics left by human activities and are also valuable resources for human society, with most of them buried deep underground. Because the protection of original historical sites is difficult, very few sites choose this method. Many funerary objects that have been in historical sites are scattered in museums, losing their original context and complicating their utilization. Thus, using digital technology to protect and utilize historical sites and to explore their outstanding value from historical, aesthetic, and anthropological perspectives is a foremost concern. Therefore, this study aims to develop a comprehensive method for the protection and utilization of historical sites, that is, digital protection and utilization based on a digital twin. We constructed a historical site digital twin model using qualitative and vertical methods, including a physical entity, virtual entity, twin data center, digital twin service, and connection. We also established a technical framework of data acquisition and processing, digital protection, and digital utilization, forming a layered management and application of digital resources. In digital protection, information in the real world and the virtual world are connected to monitor risks, collect data, create simulations, and propose protection strategies, quickly and accurately. In digital utilization, the knowledge graph is constructed to associate seemingly unrelated information, explore potential knowledge, and improve information sharing. In addition, the method is validated by means of case studies of historical sites in China. In this paper, the historical sites of the Northern Qi Dynasty in Taiyuan, Shanxi, especially the Xuxianxiu Tomb and Lourui Tomb, are discussed in detail. The results indicate that this method is effective for the protection and utilization of historical sites. [ABSTRACT FROM AUTHOR]

For many researchers the task of assessing the socio-economic security of a region involves using a system of security criteria based on comparng the values of indicators of the regional system with fixed threshold levels. This approach, at best, provides an assessment of the current state of the security of the regional system, but does not allow predicting the reaction of the state of the security system to the negative impact of external factors. For this it is necessary to additionally take into account sustainability - an ability of the regional security system to resist the impact of external threats. Therefore, diagnosing the ecosystem of the region should include steps to assess sustainability. From this point of view, resilience-diagnostics can be considered as a superstructure on the system of diagnostics of the socioeconomic security of the region, and for the integration of diagnostic processes, general computational algorithms should be used based on the digital model of the regional security system - a digital twin. The article presents an algorithm for resilience-diagnostics of the ecosystem of the region based on the digital twin model of the ecosystem security, the results of its testing, including graphic visualizations in the form of heat maps, digital portraits of the security and sustainability of the ecosystem of the region. [ABSTRACT FROM AUTHOR]

Background: Maintaining machines effectively continues to be a challenge for industrial organisations, which frequently employ reactive or premeditated methods. Recent research has begun to shift its attention towards the application of Predictive Maintenance (PdM) and Digital Twins (DT) principles in order to improve maintenance processes. PdM technologies have the capacity to significantly improve profitability, safety, and sustainability in various industries. Significantly, precise equipment estimation, enabled by robust supervised learning techniques, is critical to the efficacy of PdM in conjunction with DT development. This study underscores the application of PdM and DT, exploring its transformative potential across domains demanding real-time monitoring. Specifically, it delves into emerging fields in healthcare, utilities (smart water management), and agriculture (smart farm), aligning with the latest research frontiers in these areas. Methodology: Employing the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) criteria, this study highlights diverse modeling techniques shaping asset lifetime evaluation within the PdM context from 34 scholarly articles. Results: The study revealed four important findings: various PdM and DT modelling techniques, their diverse approaches, predictive outcomes, and implementation of maintenance management. These findings align with the ongoing exploration of emerging applications in healthcare, utilities (smart water management), and agriculture (smart farm). In addition, it sheds light on the critical functions of PdM and DT, emphasising their extraordinary ability to drive revolutionary change in dynamic industrial challenges. The results highlight these methodologies' flexibility and application across many industries, providing vital insights into their potential to revolutionise asset management and maintenance practice for real-time monitoring. Conclusions: Therefore, this systematic review provides a current and essential resource for academics, practitioners, and policymakers to refine PdM strategies and expand the applicability of DT in diverse industrial sectors. [ABSTRACT FROM AUTHOR]

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As the "blood vessels" and "nerves" of the power grid, cables are important carriers for transmitting electric energy, and their failures will affect the safe and stable operation of the power grid. In this paper, a digital twin model of thermal characteristics is constructed for 10 kV YJVx400 cluster cable and its joint model. Based on the multi-physical field coupling analysis method, the fluid field, temperature field and current-carrying capacity of the cluster cable are analyzed. The temperature rise and airflow distribution characteristics of thejoint and its external insulation when the defect of joint false connection occurs are analyzed. Based on the fuzzy analytic hierarchy process (FAHP), weights are assigned to the locations of multiple measurement points of the cable external insulation, and the temperature distribution of each measurement point is combined with the digital twin technology to calculate the hot spot temperature of the cable, so as to assess the defects of the joints of the cables. The digital twin platform is used for real-time monitoring of cable status and assessment of defects. The temperature rise experiments of cables under different carrying capacity and joint defects show that this method can accurately and efficiently calculate hot spot temperatures and assess defects of joints in cluster cables. [ABSTRACT FROM AUTHOR]

The effects of climate change and the rapid growth of societies often lead to water scarcity and inadequate water quality, resulting in a significant number of diseases. The digitalization of infrastructure and the use of Digital Twins are presented as alternatives for optimizing resources and the necessary infrastructure in the water cycle. This paper presents a framework for the development of a Digital Twin platform for a wastewater treatment plant, based on a microservices architecture which optimized its design for edge computing implementation. The platform aims to optimize the operation and maintenance processes of the plant's systems, by employing machine learning techniques, process modeling and simulation, as well as leveraging the information contained in BIM models to support decision-making. [ABSTRACT FROM AUTHOR]

Digital twins reflect the state of the environment and the activities of enterprises affected by the hydrometeorological conditions. It is proposed to use models to calculate indicators for assessing impacts of natural hazards or of climate change; of forecasts of these impacts; damage estimates; of calculating the cost of actions to protect enterprises; of assessing the feasibility of carrying out preventive actions in order to optimize them. Requirements for impact assessment models working with a digital twin are given. The difficulties in using such models are presented. Proposals for the development of impact models are being considered. A diagram of the use of digital twins in modeling impacts of environmental on enterprises is shown. [ABSTRACT FROM AUTHOR]

In an era where countries worldwide are emphasizing the process of educational informatization, traditional experimental teaching methods encounter inherent limitations, such as constraints related to the availability of experimental sites and the amount of experimental equipment. Consequently, it becomes a challenge to provide students with an unconstrained, open, and collaborative experimental environment. The digital twin, as a pivotal technology for achieving interactive mapping of the physical world and the information world, possesses attributes characterized by real time interactivity and the seamless fusion of virtual and tangible elements. As a result, various virtual simulation experimental teaching platforms grounded in digital twin technology are now extensively integrated into talent development initiatives. In the context of the "Production Planning and Control" course at Harbin Institute of Technology (HIT), the virtual simulation experimental teaching method is examined, leading to the creation of a virtual simulation instructional platform that blends the virtual and tangible realms, thanks to the foundation of digital twin technology. The virtual simulation experimental teaching platform detailed in this paper, specifically designed for optimizing scheduling within assembly line workshops, represents a pioneering model practice in the integration of digital twin technology into workshop-based experimental instruction and assumes an influential role in enhancing students' grasp of theoretical knowledge and their capacity for practical innovation. [ABSTRACT FROM AUTHOR]

The construction industry has been trying to enhance the level of digitalization and autonomy by adopting various communication and information technologies (ICT), e.g., augmented reality (AR), virtual reality (VR), robotics, drones, or building information modeling (BIM). However, improvement of the safety and productivity in their domains is still a struggle. One of the main reasons for failing to accelerate their digital transformation is ignoring the deep understanding of the concept of digital twin, its usage, and the potential benefits of digital twins in the construction industry. Therefore, this paper investigated the impacts and potentials of digital twins on the construction industry through a quantitative systematic review assisted by the text mining method. The study presented the potential usability of digital twins, leading and core technologies, and applications, revealing their benefits and potential for optimizing project planning, execution, and management process. Through this comprehensive literature review, this study elucidated the distinctive features, advantages, and immense potential that digital twins bring to the construction field. The findings highlight the transformative impact of digital twins, providing critical insights for their broader adoption and groundbreaking applications in the industry. By addressing the challenges of adopting this technology, the article provided valuable insights for advancing research and the broad implementation of digital twins in the sector. [ABSTRACT FROM AUTHOR]