Search

This article delves into the intersection of generative AI and digital twins within drug discovery, exploring their synergistic potential to revolutionize pharmaceutical research and development. Through various instances and examples, we illuminate how generative AI algorithms, capable of simulating vast chemical spaces and predicting molecular properties, are increasingly integrated with digital twins of biological systems to expedite drug discovery. By harnessing the power of computational models and machine learning, researchers can design novel compounds tailored to specific targets, optimize drug candidates, and simulate their behavior within virtual biological environments. This paradigm shift offers unprecedented opportunities for accelerating drug development, reducing costs, and, ultimately, improving patient outcomes. As we navigate this rapidly evolving landscape, collaboration between interdisciplinary teams and continued innovation will be paramount in realizing the promise of generative AI and digital twins in advancing drug discovery.

This paper will discuss Data, Information, Knowledge, and Wisdom, which is commonly referred to as DIKW. The DIKW Pyramid Model is a hierarchical model that is often referenced in both academic and practitioner circles. This model will be discussed and shown to be faulty on several levels, including a lack of definitional agreement. A new DIKW framework with systems orientation will be proposed that focuses on what the DIKW elements do in the way humans think, not what they are by definition. Information as a replacement for wasted physical resources in goal-oriented tasks will be a central organizing point. The paper will move the DIKW discussion to the computer-based concept of Digital Twins (DTs) and its augmentation of how we can use DIKW to be more effective and efficient. This will especially be the case as we move toward Intelligent Digital Twins (IDTs) with Artificial Intelligence (AI).

Conventional patient monitoring methods require skin-to-skin contact, continuous observation, and long working shifts, causing physical and mental stress for medical professionals. Remote patient monitoring (RPM) assists healthcare workers in monitoring patients distantly using various wearable sensors, reducing stress and infection risk. RPM can be enabled by using the Digital Twins (DTs)-based Internet of Robotic Things (IoRT) that merges robotics with the Internet of Things (IoT) and creates a virtual twin (VT) that acquires sensor data from the physical twin (PT) during operation to reflect its behavior. However, manual navigation of PT causes cognitive fatigue for the operator, affecting trust dynamics, satisfaction, and task performance. Also, operating manual systems requires proper training and long-term experience. This research implements autonomous control in the DTs-based IoRT to remotely monitor patients with chronic or contagious diseases. This work extends our previous paper that required the user to manually operate the PT using its VT to collect patient data for medical inspection. The proposed decision-making algorithm enables the PT to autonomously navigate towards the patient’s room, collect and transmit health data, and return to the base station while avoiding various obstacles. Rather than manually navigating, the medical personnel direct the PT to a specific target position using the Menu buttons. The medical staff can monitor the PT and the received sensor information in the pre-built virtual environment (VE). Based on the operator’s preference, manual control of the PT is also achievable. The experimental outcomes and comparative analysis verify the efficiency of the proposed system.

A digital twin (DT) is a virtual/digital model of any physical object (physical twin), interconnected through data exchange. In the context of Industry 4.0, DTs are integral to intelligent automation driving innovation at scale by providing significant improvements in precision, flexibility, and real-time responsiveness. A critical challenge in developing DTs is achieving a model that reflects real-time conditions with precision and flexibility. This paper focuses on evaluating latency and accuracy, key metrics for assessing the efficacy of a DT, which often hinder scalability and adaptability in robotic applications. This article presents a comprehensive framework for developing DTs using Unity and Robot Operating System (ROS) as the main layers of digitalization and communication. The MoveIt package was used for motion planning and execution for the robotic arm, showcasing the framework’s versatility independent of proprietary constraints. Leveraging the versatility and open-source nature of these tools, the framework ensures interoperability, adaptability, and scalability, crucial for modern smart manufacturing applications. Our approach was validated by conducting extensive accuracy and latency tests. We measured latency by timestamping messages exchanged between the physical and digital twin, achieving a latency of 77.67 ms. Accuracy was assessed by comparing the joint positions of the DT and the physical robotic arm over multiple cycles, resulting in an accuracy rate of 99.99%. The results highlight the potential of DTs in enhancing operational efficiency and decision-making in manufacturing environments.

This study aims to examine the use of digital twins in critical infrastructure through a literature review as well as a bibliometric and scientific mapping analysis. A total of 3414 documents from Scopus and Web of Science (WoS) are examined. According to the findings, digital twins play an important role in critical infrastructure as they can improve the security, resilience, reliability, maintenance, continuity, and functioning of critical infrastructure in all sectors. Intelligent and autonomous decision-making, process optimization, advanced traceability, interactive visualization, and real-time monitoring, analysis, and prediction emerged as some of the benefits that digital twins can yield. Finally, the findings revealed the ability of digital twins to bridge the gap between physical and virtual environments, to be used in conjunction with other technologies, and to be integrated into various settings and domains.

The rapid development of artificial intelligence (AI) and 5G paradigm brings infinite possibilities for data annotation for new applications in the industrial Internet of Things (IIoT). However, the problem of data annotation consistency under distributed architectures and growing concerns about issues such as data privacy and cybersecurity are major obstacles to improving the quality of distributed data annotation. In this paper, we propose a reputation-based asynchronous federated learning approach for digital twins. First, this paper integrates digital twins into an asynchronous federated learning framework, and utilizes a smart contract-based reputation mechanism to enhance the interconnection and internal interaction of asynchronous mobile terminals. In addition, in order to enhance security and privacy protection in the distributed smart annotation system, this paper introduces blockchain technology to optimize the data exchange, storage, and sharing process to improve system security and reliability. The data results show that the consistency of our proposed FedDTrep distributed intelligent labeling system reaches 99%.

Efficient healthcare facility design is crucial for providing high-quality healthcare services. This study introduces an innovative approach that integrates artificial intelligence (AI) algorithms, specifically particle swarm optimization (PSO), with building information modeling (BIM) and digital twin technologies to enhance facility layout optimization. The methodology seamlessly integrates AI-driven layout optimization with the robust visualization, analysis, and real-time capabilities of BIM and digital twins. Through the convergence of AI algorithms, BIM, and digital twins, this framework empowers stakeholders to establish a virtual environment for the streamlined exploration and evaluation of diverse design options, significantly reducing the time and manual effort required for layout design. The PSO algorithm generates optimized 2D layouts, which are seamlessly transformed into 3D BIM models through visual programming in Dynamo. This transition enables stakeholders to visualize, analyze, and monitor designs comprehensively, facilitating well-informed decision-making and collaborative discussions. The study presents a comprehensive methodology that underscores the potential of AI, BIM, and digital twin integration, offering a path toward more efficient and effective facility design.

This article examines the incorporation of the Shopping Assistance Automatic Suggestion (SAAS) model into Virtual Reality (VR) environments in order to improve the online shopping experience. The SAAS model employs sophisticated deep learning methods to offer customized product recommendations, which are conveyed by non-player characters (NPCs) via voice-based interactions. Our goal is to develop an interactive shopping experience that replicates real-life interactions by integrating AI-powered recommendations with immersive VR technology. We gather and standardize data from several open commerce databases, such as Amazon Product and Customer Reviews. The SAAS model, in conjunction with GPT-3, BERT, and T5, undergoes training and testing to evaluate its effectiveness across multiple criteria. The results demonstrate that the SAAS model surpasses other models in delivering contextually aware and pertinent recommendations. The integration process outlines the specific steps involved in capturing, processing, and transforming user interactions in virtual reality (VR) into vocal suggestions provided by non-player characters (NPCs). This strategy improves customization and utilizes the immersive features of virtual reality to effectively engage people. The results of our research establish a higher standard for e-commerce, with the goal of enhancing the user experience of online purchasing by making it more instinctive, engaging, and pleasurable.

This paper presents computer and color vision research focusing on human color perception in VR environments. A VR art gallery with digital twins of original artworks is created for this experiment. In this research, the field of colorimetry and the application of the L*a*b* and RGB color models are applied. The inter-relationships of the two color models are applied to create a color modification of the VR art gallery environment using C# Script procedures. This color-edited VR environment works with a smooth change in color tone in a given time interval. At the same time, a sudden change in the color of the RGB environment is defined in this interval. This experiment aims to record a user’s reaction embedded in a VR environment and the effect of color changes on human perception in a VR environment. This research uses lie detector sensors that record the physiological changes of the user embedded in VR. Five sensors are used to record the signal. An experiment on the influence of the user’s color perception in a VR environment using lie detector sensors has never been conducted. This research defines the basic methodology for analyzing and evaluating the recorded signals from the lie detector. The presented text thus provides a basis for further research in the field of colors and human color vision in a VR environment and lays an objective basis for use in many scientific and commercial areas.

Current railway infrastructure maintenance work, which is mostly carried out by visual inspection, has a reactive approach, dissociated information, and limited follow-up. On the other hand, railway bridges, being critical infrastructures, require effective monitoring and maintenance to guarantee their safety and operation over time. The designed tool links a parametric BIM model in Revit® with an automated spreadsheet in MS Excel® through visual programming in Dynamo, generating BIM/data automation as an initial step towards a digital twin. This achieves a bidirectional flow to exchange data on the structural condition of elements. The procedure was applied to a railway bridge in use for over 100 years, representing its geometry and damage information according to technical standards. The value lies in laying the foundations for adopting preventive approaches for this key infrastructure. The BIM/data automation allows the BIM model to visually reflect the condition of the elements, depending on their damage, consolidate the inspection information, and generate a visual management tool. In conclusion, the designed BIM/data automation improves the monitoring of railway bridges compared to traditional methods, facilitating the interaction and relationship between the damage records and the actual bridge elements, laying the foundations for the construction of digital twins.

Buildings significantly contribute to global energy consumption and greenhouse gas emissions. This systematic literature review explores the potential of artificial intelegence (AI) to enhance sustainability throughout a building’s lifecycle. The review identifies AI technologies applicable to sustainable building practices, examines their influence, and analyses implementation challenges. The findings reveal AI’s capabilities in optimising energy efficiency, enabling predictive maintenance, and aiding in design simulation. Advanced machine learning algorithms facilitate data-driven analysis, while digital twins provide real-time insights for decision-making. The review also identifies barriers to AI adoption, including cost concerns, data security risks, and implementation challenges. While AI offers innovative solutions for energy optimisation and environmentally conscious practices, addressing technical and practical challenges is crucial for its successful integration in sustainable building practices.

Although digital twins have been established in manufacturing for a long time, they are only more recently making their way into the urban environment and present a relatively new concept for the construction industry. The concept of a digital twin—a model of the physical environment that has a real-time two-way link between the physical and the digital, with the virtual model changing over time to reflect changes in the real world—lends itself well to the continually changing environment of a construction project. Predictive capabilities built into a twin also have great potential for construction planning—including in supply chain management and waste disposal as well as in the construction process itself. Underpinning this opportunity is location data, which model where something is happening and when and can be used to solve a wide range of problems. In particular, location (the power of where) can integrate diverse data sources and types into a single system, overcoming interoperability challenges that are known to be a barrier to twin implementation. This paper demonstrates the power of location-enabled digital twins in the context of a highway construction project, documenting and addressing data engineering tasks and functionality development to explore the potential of digital twins in the context of two case studies—health and safety and construction monitoring. We develop two demonstrators using data from an existing construction project (building on data and requirements from industry partner Skanska) to build twins that make use of the powers of 4D data presentation offered by the Unreal Gaming Engine and CesiumJS mapping, while software development expertise is sometimes available to construction firms, we specifically explore to what extent the no-code approach available within Unreal can be deployed in this context. Our findings provide evidence to construction companies as to the benefits of digital twins, as well as an understanding of the data engineering and technical skills required to achieve these benefits. The overall results demonstrate the potential for digital twins to unlock and democratise construction data, taking them beyond the niche use of experts and into the boardroom.

The construction industry’s reliance on traditional methods and fragmented workflows results in significant information loss, inefficiencies, increased costs, and errors. This study addresses these issues by integrating comprehensive urban planning with building information modeling (BIM) to create a seamless information flow throughout the building lifecycle. We propose a holistic framework that synchronizes data from planning to demolition, incorporating national and municipal digital twins. An imperative literature review and analysis of international best practices were conducted to develop a conceptual framework aimed at improving data accuracy and interoperability. Our findings underscore the importance of adopting open standards such as Industry Foundation Classes (IFC) and CityGML for effective information exchange. By implementing an information model (IM)-based approach in urban planning and public sector permit processes, project timelines can be streamlined, and regulatory compliance enhanced. This study concludes that continuous, integrated information flow facilitates more efficient, cost-effective construction practices and improved decision-making. Furthermore, this research illustrates the potential of digital twin technology to revolutionize the construction industry by enabling real-time data integration and fostering stakeholder collaboration, ultimately offering a robust framework for practitioners, and significantly enhancing the efficiency and accuracy of construction processes.

This paper examines the integration of interactive 3D applications into the teaching process in mechanical engineering education. An innovative interactive 3D application has been developed as a teaching aid for engineering students. The main advantage is its easy availability through a web browser on mobile devices or desktop computers. It includes four explorable 3D gearbox models with assembly animations, linked technical information, and immersive virtual and augmented reality (AR) experiences. The benefits of using this application in the teaching process were monitored on a group of students at the end of the semester. Assessments conducted before and after the use of the interactive 3D application measured learning outcomes. Qualitative feedback from students was also collected. The results demonstrated significant improvements in engagement, spatial awareness, and understanding of gearbox principles compared to traditional methods. The versatility and accessibility of the application also facilitated self-directed learning, reducing the need for external resources. These findings indicate that interactive 3D tools have the potential to enhance student learning and engagement and to promote sustainable practices in engineering education. Future research could explore the scalability and applicability of these tools across different engineering disciplines and educational contexts.

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.

In the rapidly evolving landscape of scientific semiconductor laboratories (commonly known as, cleanrooms), integrated with Internet of Things (IoT) technology and Cyber-Physical Systems (CPSs), several factors including operational changes, sensor aging, software updates and the introduction of new processes or equipment can lead to dynamic and non-stationary data distributions in evolving data streams. This phenomenon, known as concept drift, poses a substantial challenge for traditional data-driven digital twin static machine learning (ML) models for anomaly detection and classification. Subsequently, the drift in normal and anomalous data distributions over time causes the model performance to decay, resulting in high false alarm rates and missed anomalies. To address this issue, we present TWIN-ADAPT, a continuous learning model within a digital twin framework designed to dynamically update and optimize its anomaly classification algorithm in response to changing data conditions. This model is evaluated against state-of-the-art concept drift adaptation models and tested under simulated drift scenarios using diverse noise distributions to mimic real-world distribution shift in anomalies. TWIN-ADAPT is applied to three critical CPS datasets of Smart Manufacturing Labs (also known as “Cleanrooms”): Fumehood, Lithography Unit and Vacuum Pump. The evaluation results demonstrate that TWIN-ADAPT’s continual learning model for optimized and adaptive anomaly classification achieves a high accuracy and F1 score of 96.97% and 0.97, respectively, on the Fumehood CPS dataset, showing an average performance improvement of 0.57% over the offline model. For the Lithography and Vacuum Pump datasets, TWIN-ADAPT achieves an average accuracy of 69.26% and 71.92%, respectively, with performance improvements of 75.60% and 10.42% over the offline model. These significant improvements highlight the efficacy of TWIN-ADAPT’s adaptive capabilities. Additionally, TWIN-ADAPT shows a very competitive performance when compared with other benchmark drift adaptation algorithms. This performance demonstrates TWIN-ADAPT’s robustness across different modalities and datasets, confirming its suitability for any IoT-driven CPS framework managing diverse data distributions in real time streams. Its adaptability and effectiveness make it a versatile tool for dynamic industrial settings.

The advancement of digital twins (DTws) has been instrumental in various scientific and industrial domains, facilitating real-time monitoring, analysis, and optimisation of complex systems. However, it remains difficult to describe precisely the architectural models and their characteristics of DTws and the aggregation of lower-level DTws to higher-level DTws. This article introduces two contributions with the goal of addressing challenges in describing DTws architectures and aggregating DTws. Firstly, it presents the development of “WoTDT” (WoT digital twin) ontology, an extension of the W3C Web of Things descriptions ontology, designed to semantically describe the five-dimensional model architecture of DTws. This ontology enhances data interoperability and accessibility across dimensions, promoting a deeper understanding of DTws. Secondly, it introduces the “DTAG” (digital twin aggregation) methodology for aggregating multiple DTws into an unified DTw aggregate (DTwA). This methodology considers whether the DTws contain semantics or not and employs the WoTDT ontology to conceptualise the architecture and features of the resulting DTwA. Finally, an example of WoTDT ontology together with the DTAG methodology is shown in the context of the European H2020 construction-related project COGITO.

Nuclear power plants (NPPs) require continuous monitoring of various systems, structures, and components to ensure safe and efficient operations. The critical safety testing of new fuel compositions and the analysis of the effects of power transients on core temperatures can be achieved through modeling and simulations. They capture the dynamics of the physical phenomenon associated with failure modes and facilitate the creation of digital twins (DTs). Accurate reconstruction of fields of interest (e.g., temperature, pressure, velocity) from sensor measurements is crucial to establish a two-way communication between physical experiments and models. Sensor placement is highly constrained in most nuclear subsystems due to challenging operating conditions and inherent spatial limitations. This study develops optimized data-driven sensor placements for full-field reconstruction within reactor and steam generator subsystems of NPPs. Optimized constrained sensors reconstruct field of interest within a tri-structural isotropic (TRISO) fuel irradiation experiment, a lumped parameter model of a nuclear fuel test rod and a steam generator. The optimization procedure leverages reduced-order models of flow physics to provide a highly accurate full-field reconstruction of responses of interest, noise-induced uncertainty quantification and physically feasible sensor locations. Accurate sensor-based reconstructions establish a foundation for the digital twinning of subsystems, culminating in a comprehensive DT aggregate of an NPP.

In recent years, researchers in the field of natural heritage have intensified their efforts to develop new ways to enhance the promotion and accessibility of natural content in order to attract more audiences using virtual representations of physical objects (digital twins). Therefore, they are increasingly incorporating new technologies and digital tools in their operations since their usage by the general public and in the natural heritage (NH) museums is considered particularly effective. Simultaneously, the increasing quality of the produced digitizations has opened up new opportunities for the exploitation of the outcomes of digitization beyond the initial anticipations. Responding to the growing demand of museum visitors for a personalized digital tour experience, especially amidst the recent COVID-19 pandemic, the v-PalM project aims to develop a digital platform to offer virtual guidance and education services at the Museum of Paleontology and Geology, which is hosted at the National Kapodistrian University of Athens. The development of the platform will be based on collecting data through various methods, including crowdsourcing, innovative information, and communication technologies, taking advantage of content digitization using 3D scanning devices. This paper demonstrates an enhanced methodology for the digitization of paleontological exhibits. The methodology uses photogrammetry and laser scanning methods from various devices, such as drones, laser scanners, and smartphones. These methods create digital twins that are suitable for various scenarios, including research, education, and entertainment. The proposed methodology has been applied to over fifty paleontological museum exhibits of varying sizes and complexities, and the resulting 3D models exhibit high accuracy in both their material and geometric aspects, while they also feature crucial details that assist researchers and the scientific community.

Digital twins are a relatively new form of digital modeling that has been gaining popularity in recent years. This is in large part due to their ability to update in real time to their physical counterparts and connect across multiple devices. As a result, much interest has been directed towards using digital twins in the healthcare industry. Recent advancements in smart wearable technologies have allowed for the utilization of human digital twins in healthcare. Human digital twins can be generated using biometric data from the patient gathered from wearables. These data can then be used to enhance patient care through a variety of means, such as simulated clinical trials, disease prediction, and monitoring treatment progression remotely. This revolutionary method of patient care is still in its infancy, and as such, there is limited research on using wearables to generate human digital twins for healthcare applications. This paper reviews the literature pertaining to human digital twins, including methods, applications, and challenges. The paper also presents a conceptual method for creating human body digital twins using wearable sensors.

This paper concerns the extension of the Heritage Digital Twin Ontology introduced in previous research to describe the reactivity of digital twins used for cultural heritage documentation by including the semantic description of sensors and activators and all of the process of interacting with the real world. After analysing previous work on the use of digital twins in cultural heritage, a summary description of the Heritage Digital Twin Ontology is provided, and the existing applications of digital twins to cultural heritage are overviewed, with references to reviews summarising the large production of scientific contributions on the topic. Then, a novel ontology named the Reactive Digital Twin Ontology is described, in which sensors, activators, and the decision processes are also semantically described, turning the previous synchronic approach to cultural heritage documentation into a diachronic one. Some case studies exemplify this theory.

Construction 4.0 integrates digital technologies that increase vulnerability to cyber threats. A dedicated cyber risk assessment framework is essential for proactive risk mitigation. However, existing studies on this subject within the construction sector are scarce, with most discussions still in the preliminary stages. This study introduces a cyber risk assessment framework that integrates machine learning techniques, pioneering a data-driven approach to quantitatively assess cyber risks while considering industry-specific vulnerabilities. The framework builds on over 20 literature reviews related to construction cybersecurity and semi-structured interviews with two industry experts, ensuring both rigor and alignment with practical industrial needs. This study also addresses the challenges of data collection and proposes potential solutions, such as a standardized data collection format with preset fields that computers can automatically populate using data from construction companies. Additionally, the framework proposes dynamic machine learning models that adjust based on new data, facilitating continuous risk monitoring tailored to industry needs. Furthermore, this study explores the potential of advanced language models in cybersecurity management, positioning them as intelligent cybersecurity consultants that provide answers to security inquiries. Overall, this study develops a conceptual machine learning framework aimed at creating a robust, off-the-shelf cyber risk management system for industry practitioners.

Digital twins (DTs) are highly valuable tools for urban planning as they provide a virtual replica of the physical city, integrating real-time data and simulations to enhance the decision-making and management processes. The use of DTs expands the possibilities for data integration and visualization in urban contexts. This includes real-time data measurements from multiple sources, such as sensors and IoT devices, facilitating comprehensive insights. DT’s virtual representation helps authorities and planners visualize urban dynamics and improve their understanding of urban ecosystems, energy efficiency, traffic management, emergency response, and more. DT supports the simulation and modeling of different scenarios in an urban built environment, enabling the predictive analysis of transformation decisions and the anticipation of future trends and challenges. This paper highlights the assumptions and ongoing progress in the development of a DT for the city of Turin (Italy), focusing on a range of applications, such as the extraction of built and natural environment features, land use data, road network and pavement quality, and signage, along with continuous model updates over time.

Digital twins (DTs) have become a widely discussed subject, believed to have the potential to solve various problems across different industries, including Engineering & Construction (E&C). However, there is still significant misconception concerning the definition of DTs and their purpose within E&C. This study dives deep into identifying DT applications within E&C and the other prominent industries, i.e., Aerospace & Aviation, Manufacturing, Energy & Utilities, Automotive, Healthcare, Smart Cities, Oil & Gas, and Retail. The main challenges to the evolution of DT practical applications are also analyzed. A combination of a literature review, multi-case study analysis, and comparative analysis compose the deployed methodology. Standardization and a maturity level classification are proposed to drive progress of the adoption of DTs. The distinct aspects of the different industries and their assets are evaluated to the conclusion that DTs are better employed for maintenance of structures within E&C. DTs have become a well-worn topic, but the abundance of complex theoretical frameworks is met with simple or inexistent practical applications. Therefore, the novelty of this study lays in its comprehensive analysis of DT applications and real-world implementations—a departure from the often-theoretical discussions surrounding DTs.

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.

In the highly competitive field of material manufacturing, stakeholders strive for the increased quality of the end products, reduced cost of operation, and the timely completion of their business processes. Digital twin (DT) technologies are considered major enablers that can be deployed to assist the development and effective provision of manufacturing processes. Additionally, knowledge graphs (KG) have emerged as efficient tools in the industrial domain and are able to efficiently represent data from various disciplines in a structured manner while also supporting advanced analytics. This paper proposes a solution that integrates a KG and DTs. Through this synergy, we aimed to develop highly autonomous and flexible DTs that utilize the semantic knowledge stored in the KG to better support advanced functionalities. The developed KG stores information about materials and their properties and details about the processes in which they are involved, following a flexible schema that is not domain specific. The DT comprises smaller Virtual Objects (VOs), each one acting as an abstraction of a single step of the Industrial Business Process (IBP), providing the necessary functionalities that simulate the corresponding real-world process. By executing appropriate queries to the KG, the DT can orchestrate the operation of the VOs and their physical counterparts and configure their parameters accordingly, in this way increasing its self-awareness. In this article, the architecture of such a solution is presented and its application in a real laser glass bending process is showcased.

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.

Traditional capacity forecasting algorithms lack effective data interaction, leading to a disconnection between the actual plan and production. This paper discusses the multi-factor model based on a discrete manufacturing workshop and proposes a digital twin-driven discrete manufacturing workshop capacity prediction method. Firstly, this paper gives a system framework for production capacity prediction in discrete manufacturing workshops based on digital twins. Then, a mathematical model is described for discrete manufacturing workshop production capacity under multiple disturbance factors. Furthermore, an innovative production capacity prediction method, using the “digital twin + Long-Short-Term Memory Network (LSTM) algorithm”, is presented. Finally, a discrete manufacturing workshop twin platform is deployed using a commemorative disk custom production line as the prototype platform. The verification shows that the proposed method can achieve a prediction accuracy rate of 91.8% for production line capacity. By integrating the optimization feedback function of the digital twin system into the production process control, this paper enables an accurate perception of the current state and future changes in the production system, effectively evaluating the production capacity and delivery date of discrete manufacturing workshops.

Transient car emission tests generate huge amount of test data, but their results are usually evaluated only using their “accumulated” cycle values according to the homologation limits. In this work, two machine learning models were developed and applied to a truck RDE test and two light-duty vehicle chassis emission tests. Different from the conventional approach, the engine parameters and fuel consumption were acquired from the Engine Control Unit, not from the test measurement equipment. Instantaneous engine values were used as input in machine-learning-based digital twins. This novel approach allows for much less costly vehicle tests and optimizations. The paper’s novel approach and developed digital twins model were able to predict both instantaneous and accumulated fuel consumption with good accuracy, and also for tests cycles different to the one used to train the model.

Numerical modeling tools are essential in aircraft structural design, yet they face challenges in accurately reflecting real-world behavior due to factors like material properties scatter and manufacturing-induced deviations. This article addresses the potential impact of digital twins on overcoming these limitations and enhancing model reliability through advanced updating techniques based on machine learning. Digital twins, which are virtual replicas of physical systems, offer a promising solution by integrating sensor data, operational inputs, and historical records. Machine learning techniques enable the calibration and validation of models, combining experimental inputs with simulations through continuous updating processes that refine digital twins, improving their accuracy in predicting structural behavior and performance throughout an aircraft’s life cycle. These refined models enable real-time monitoring and precise damage assessment, supporting decision making in diverse contexts. By integrating sensor data and updating techniques, digital twins contribute to improved design and maintenance operations by providing valuable insights into structural health, safety, and reliability. Ultimately, this approach leads to more efficient and safer aviation operations, demonstrating the potential of digital twins to revolutionize aircraft structural analysis and design. This article explores various advancements and methodologies applicable to structural assessment, leveraging machine learning tools. These include the utilization of physics-informed neural networks, which enable the handling of diverse uncertainties. Such approaches empower a more informed and adaptive strategy, contributing to the assurance of structural integrity and safety in aircraft structures throughout their operational life.

Diabetes management requires constant monitoring and individualized adjustments. This study proposes a novel approach that leverages digital twins and personal health knowledge graphs (PHKGs) to revolutionize diabetes care. Our key contribution lies in developing a real-time, patient-centric digital twin framework built on PHKGs. This framework integrates data from diverse sources, adhering to HL7 standards and enabling seamless information access and exchange while ensuring high levels of accuracy in data representation and health insights. PHKGs offer a flexible and efficient format that supports various applications. As new knowledge about the patient becomes available, the PHKG can be easily extended to incorporate it, enhancing the precision and accuracy of the care provided. This dynamic approach fosters continuous improvement and facilitates the development of new applications. As a proof of concept, we have demonstrated the versatility of our digital twins by applying it to different use cases in diabetes management. These include predicting glucose levels, optimizing insulin dosage, providing personalized lifestyle recommendations, and visualizing health data. By enabling real-time, patient-specific care, this research paves the way for more precise and personalized healthcare interventions, potentially improving long-term diabetes management outcomes.

This paper presents a comparative analysis of six prominent registration techniques for solving CAD model alignment problems. Unlike the typical approach of assessing registration algorithms with synthetic datasets, our study utilizes point clouds generated from the Cranfield benchmark. Point clouds are sampled from existing CAD models and 3D scans of physical objects, introducing real-world complexities such as noise and outliers. The acquired point cloud scans, including ground-truth transformations, are made publicly available. This dataset includes several cleaned-up scans of nine 3D-printed objects. Our main contribution lies in assessing the performance of three classical (GO-ICP, RANSAC, FGR) and three learning-based (PointNetLK, RPMNet, ROPNet) methods on real-world scans, using a wide range of metrics. These include recall, accuracy and computation time. Our comparison shows a high accuracy for GO-ICP, as well as PointNetLK, RANSAC and RPMNet combined with ICP refinement. However, apart from GO-ICP, all methods show a significant number of failure cases when applied to scans containing more noise or requiring larger transformations. FGR and RANSAC are among the quickest methods, while GO-ICP takes several seconds to solve. Finally, while learning-based methods demonstrate good performance and low computation times, they have difficulties in training and generalizing. Our results can aid novice researchers in the field in selecting a suitable registration method for their application, based on quantitative metrics. Furthermore, our code can be used by others to evaluate novel methods.

Advancing digitalization is reaching the realm of lightweight construction and structural–mechanical components. Through the synergistic combination of distributed sensors and intelligent evaluation algorithms, traditional structures evolve into smart sensing systems. In this context, Structural Health Monitoring (SHM) plays a key role in managing potential risks to human safety and environmental integrity due to structural failures by providing analysis, localization, and records of the structure’s loading and damaging conditions. The establishment of networks between sensors and data-processing units via Internet of Things (IoT) technologies is an elementary prerequisite for the integration of SHM into smart sensing systems. However, this integrating of SHM faces significant restrictions due to scalability challenges of smart sensing systems and IoT-specific issues, including communication security and interoperability. To address the issue, this paper presents a comprehensive methodological framework aimed at facilitating the scalable integration of objects ranging from components via systems to clusters into SHM systems. Furthermore, we detail a prototypical implementation of the conceptually developed framework, demonstrating a structural component and its corresponding Digital Twin. Here, real-time capable deformation and strain-based monitoring of the structure are achieved, showcasing the practical applicability of the proposed framework.

Underwater cultural heritage (UCH) is an irreplaceable resource with intrinsic value that requires preservation, documentation, and safeguarding. Documentation is fundamental to increasing UCH resilience, providing a basis for monitoring, conservation, and management. Advanced UCH documentation and virtualization technologies are increasingly important for dissemination and visualization purposes, domain expert study, replica reproduction, degradation monitoring, and all other outcomes after a metric survey of cultural heritage (CH). Among the different metric documentation techniques, underwater photogrammetry is the most widely used for UCH documentation. It is a non-destructive and relatively inexpensive method that can produce high-resolution 3D models and 2D orthomosaics of underwater sites and artifacts. However, underwater photogrammetry is challenged by the different optical properties of water, light penetration, visibility and suspension, radiometric issues, and environmental drawbacks that make underwater documentation difficult. This paper introduces some of the recent applications of photogrammetric techniques and methods for UCH documentation, as well as the needs and shortcomings of the current state of the art.

Resistance spot welding is a process with high variability regarding the quality of the produced joints. This means that key performance indicators (KPIs) such as geometrical and mechanical features as well as failure modes can deviate from the initial design even if the same process parameters are used. The industry has developed quality assurance programs and quality control methods for tracking these KPIs; however, most of them are based on offline or/and destructive practices. On the other hand, state-of-the-art approaches have made online quality assessment feasible and proved its necessity if a 100% quality rate is required. However, limited attention has been given to “closing the loop” and providing feedback for preventing and correcting process anomalies that cause quality variations in real time. In this study, the main gaps between the state of practice and the state of the art are discussed in the context of quality assurance for resistance spot welding. Finally, the role and importance of digital twins by taking into consideration the entire welding ecosystem in quality assurance are discussed in order to form the prospects for the road ahead.

The construction of digital twin cities is a current research hotspot; GIS technology and BIM technology are widely used in the field of digital twin cities. However, BIM is still subject to major limitations in its applications, mainly due to huge amounts of model data, low query efficiency and accuracy, non-uniform marking systems, etc. The reason is that the BIM model itself focuses more on the expression of visual effects and lacks spatial calculation ability and the utilization of spatial location information. Secondly, the current lightweight processing methods for BIM models are mostly based on geometric transformation and rendering optimization, focusing more on the data compression and visual quality of the model, which essentially does not change the data structure of the BIM model, and it is difficult to establish the mapping relationship between spatial location and spatial data, information, and resources. In addition, current coding methods proposed for BIM models are mostly based on the line classification method, which realizes the identification of components based on the classification of their attributes, and the location information is stored according to the attributes or natural language descriptions, which need to be parsed and translated when they are used, and this procedure ignores the importance of spatial location in daily management and emergency management. The importance of spatial location in daily management and emergency management is also ignored. Based on this kind of identification code, it is impossible to directly analyze and apply spatial location data. Therefore, this paper takes the combination of GIS technology and BIM technology as the starting point and proposes a BIM data modeling method based on the BeiDou grid code, based on the efficiency of its underlying data organization and the accuracy of its real geographic location expression on the one hand and the completeness of the information expression by BIM and fine three-dimensional visualization on the other hand. Finally, a series of experiments are carried out based on the method. Through visualization modeling and efficiency experiments, different feature models are meshed to verify the feasibility and efficiency of the model. Through coding and information query experiments, the model′s data organization capability, data dynamic carrying capability, and efficient spatial computation capability and practical application capability are verified.

The integration of heterogeneous hardware and software components to construct human-centered systems for Industry 5.0, particularly human digital twins, presents considerable complexity. Our research addresses this challenge by pioneering a novel approach that harmonizes the techno-centered focus of the Robot Operating System (ROS) with the cross-platform advantages inherent in NEP+ (a human-centered development framework intended to assist users and developers with diverse backgrounds and resources in constructing interactive human–machine systems). We introduce the nep2ros ROS package, aiming to bridge these frameworks and foster a more interconnected and adaptable approach. This initiative can be used to facilitate diverse development scenarios beyond conventional robotics, underpinning a transformative shift in Industry 5.0 applications. Our assessment of NEP+ capabilities includes an evaluation of communication performance utilizing serialization formats like JavaScript Object Notation (JSON) and MessagePack. Additionally, we present a comparative analysis between the nep2ros package and existing solutions, illustrating its efficacy in linking the simulation environment (Unity) and ROS. Moreover, our research demonstrates NEP+’s applicability through an immersive human-in-the-loop collaborative assembly. These findings offer promising prospects for innovative integration possibilities across a broad spectrum of applications, transcending specific platforms or disciplines.

The current decarbonization transition to be achieved by 2050 according to the European Council has given great prominence to the use of Digital Twins as tools for energy management. For their correct operation, it is essential to control the uncertainties of the energy models, which lead to differences between the measured and predicted data. One of the key parameters that is most difficult to assess numerically is air leakage. The existent infiltration models available in EnergyPlus were developed to be applied in low-rise residential buildings with fewer than three stories. Therefore, it is common to rely on air leakage equations employing predefined coefficients. This research presents an empirical assessment of the performance of two EnergyPlus air leakage models, the “Effective Leakage Area” and the “Flow Coefficient”, in predicting dynamic infiltration within the attic of a seven-story building. Blower door tests, along with the application of CO2 tracer gas, were conducted to establish coefficients for the models. Then, they were evaluated in three independent periods according to the criteria established in the American Society for Testing Material D5157 Standard. Those models that only used in situ coefficients consistently met the standard across all three periods, demonstrating for both equations their accurate performance and reliability. For the best model derived from tracer gas data, the R2 and NMSE values are 0.94 and 0.019, respectively. In contrast, the model developed using blower door test data and EnergyPlus default values presented a 64% reduction in accuracy compared to the best one. This discrepancy could potentially lead to misleading energy estimates. Although other software options exist for estimating infiltration, this study specifically targets EnergyPlus users. Therefore, these findings offer valuable insights to make more informed decisions when implementing the infiltration models into energy simulations for high-rise buildings using EnergyPlus.

At present, increased modes of transport have facilitated daily life. Building information modeling (BIM) integration has become a key strategy to foster efficiency, collaboration, and sustainability in the fields of buildings, transport, and facilities. Currently, there is a scarcity of comprehensive examinations of the performance of integrated BIM with transport facilities and applications to help identify potential prospects for sustainable development. Hence, this research paper attempts to scrutinize the integration of BIM, transportation, and facilities (T and Fs) by highlighting future directions and trends, revealing the existing status and hotspots of research in the field, and clarifying the developmental pulse of research as well as emerging areas in the future. A quantitative research technique is utilized in this study, using VOSviewer and CiteSpace software, and the Web of Science Core Collection (WoSCC) database. The study findings suggest that the hot keywords for the integration of BIM and T and Fs are predominantly concentrated on construction, framework, system, design, and management. In addition, over the previous 34 years (years 1989 to 2023), the applications of point cloud, digital twin, and life cycle assessment have been the current hot topics, and these emerging technologies can offer more innovative breakthroughs for the future. Furthermore, the integration of BIM and T and Fs represents an important trend that is essential for improving the efficiency, sustainability, and intelligence of buildings and infrastructure, from which the three domains can create synergies that contribute to the better planning, construction, and management of building and infrastructure projects.

The effective development of digital twins of real-world objects requires sophisticated data collection techniques and algorithms for the automated modeling of individual objects. In City Information Modeling (CIM) systems, individual buildings can be modeled automatically at the second Level of Detail or LOD2. Similarly, for Tree Information Modeling (TIM) and building Forest Digital Twins (FDT), automated solutions for the 3D modeling of individual trees at different levels of detail are required. The existing algorithms support the automated modeling of trees by generating models of the canopy and the lower part of the trunk. Our argument for this work is that the structure of tree trunk and branches is as important as canopy shape. As such, the aim of the research is to develop an algorithm for automatically modeling tree trunks based on data from point clouds obtained through laser scanning. Aiming to generate 3D models of tree trunks, the suggested approach starts with extracting the trunk point cloud, which is then segmented into single stems. Subsets of point clouds, representing individual branches, are measured using Airborne Laser Scanning (ALS) and Terrestrial Laser Scanning (TLS). Trunks and branches are generated by fitting cylinders to the layered subsets of the point cloud. The individual stems are modeled by a structure of slices. The accuracy of the model is calculated by determining the fitness of cylinders to the point cloud. Despite the huge variation in trunk geometric forms, the proposed modeling approach can gain an accuracy of better than 4 cm in the constructed tree trunk models. As the developed tree models are represented in a matrix format, the solution enables automatic comparisons of tree elements over time, which is necessary for monitoring changes in forest stands. Due to the existence of large variations in tree trunk geometry, the performance of the proposed modeling approach deserves further investigation on its generality to other types of trees in multiple areas.

A Digital Twin (DT) refers to a virtual representation or digital replica of a physical object, system, process, or entity. This concept involves creating a detailed, real-time digital counterpart that mimics the behavior, characteristics, and attributes of its physical counterpart. DTs have the potential to improve efficiency, reduce costs, and enhance decision-making by providing a detailed, real-time understanding of the physical systems they represent. While this technology is finding application in numerous fields, such as energy, healthcare, and transportation, it appears to be a key component of the digital transformation of industries fostered by the fourth Industrial revolution (Industry 4.0). In this paper, we present the research results achieved by IoTwins, a European research project aimed at investigating opportunities and issues of adopting DTs in the fields of industrial manufacturing and facility management. Particularly, we discuss a DT model and a reference architecture for use by the research community to implement a platform for the development and deployment of industrial DTs in the cloud continuum. Guided by the devised architectures’ principles, we implemented an open platform and a development methodology to help companies build DT-based industrial applications and deploy them in the so-called Edge/Cloud continuum. To prove the research value and the usability of the implemented platform, we discuss a simple yet practical development use case.

Computer-aided engineering (CAE) is an essential tool in a digital twin not only to verify and validate a virtual twin before it is transformed into a physical twin, but also to monitor the use of the physical twin for enhanced sustainability. This paper aims to develop a CAE model for a digital twin to predict the fatigue life of materials. Fatigue damage is represented by the size of a macro-crack that grows with a cluster of micro-cracks subjected to three different loads. The growth angle is related to the maximum circumferential tensile stress, and the growth rate is determined by the stress intensity factor (SIF) at the crack tip. The prediction model takes into consideration the main factors, including micro-cracks, crack closures, and initial configurations. Simulations are developed for the growth of macro-cracks with radially distributed micro-cracks and randomly distributed micro-cracks, and we find that (1) the macro-crack in the second case grows faster than that in the first case; (2) a pure shear load affects the macro-crack propagation more than a combined shear and tensile load or a tensional load; (3) the external stresses required to propagate are reduced when the inclination angle of the micro-crack is small and within (−25° < β < 25°); (4) micro-cracks affect the propagating path of the macro-crack and generally guide the direction of propagation. The developed model has been verified and validated experimentally for its effectiveness in predicting the fracture or fatigue damage of a structure.

Smart cities, leveraging advanced data analytics, predictive models, and digital twin techniques, offer a transformative model for sustainable urban development. Predictive analytics is critical to proactive planning, enabling cities to adapt to evolving challenges. Concurrently, digital twin techniques provide a virtual replica of the urban environment, fostering real-time monitoring, simulation, and analysis of urban systems. This study underscores the significance of real-time monitoring, simulation, and analysis of urban systems to support test scenarios that identify bottlenecks and enhance smart city efficiency. This paper delves into the crucial roles of citizen report analytics, prediction, and digital twin technologies at the neighborhood level. The study integrates extract, transform, load (ETL) processes, artificial intelligence (AI) techniques, and a digital twin methodology to process and interpret urban data streams derived from citizen interactions with the city’s coordinate-based problem mapping platform. Using an interactive GeoDataFrame within the digital twin methodology, dynamic entities facilitate simulations based on various scenarios, allowing users to visualize, analyze, and predict the response of the urban system at the neighborhood level. This approach reveals antecedent and predictive patterns, trends, and correlations at the physical level of each city area, leading to improvements in urban functionality, resilience, and resident quality of life.

The use of renewable energy sources is becoming increasingly widespread around the world due to various factors, the most relevant of which is the high environmental friendliness of these types of energy resources. However, the large-scale involvement of green energy leads to the creation of distributed energy networks that combine several different generation methods, each of which has its own specific features, and as a result, the data collection and processing necessary to optimize the operation of such energy systems become more relevant. Development of new technologies for the more optimal use of RES is one of the main tasks of modern research in the field of energy, where an important place is assigned to the use of technologies based on artificial intelligence, allowing researchers to significantly increase the efficiency of the use of all types of RES within energy systems. This paper proposes to consider the methodology of application of modern approaches to the assessment of the amount of energy obtained from renewable energy sources based on artificial intelligence technologies, approaches used for data processing and for optimization of the control processes for operating energy systems with the integration of renewable energy sources. The relevance of the work lies in the formation of a general approach applied to the evaluation of renewable energy sources such as solar and wind energy based on the use of artificial intelligence technologies. As a verification of the approach considered by the authors, a number of models for predicting the amount of solar power generation using photovoltaic panels have been implemented, for which modern machine-learning methods have been used. As a result of testing for quality and accuracy, the best results were obtained using a hybrid forecasting model, which combines the joint use of a random forest model applied at the stage of the normalization of the input data, exponential smoothing model, and LSTM model.

The spin theory of fullerenes is taken as a basis concept to virtually exhibit a peculiar role of C60 fullerene in the free radical polymerization of vinyl monomers. Virtual reaction solutions are filled with the initial ingredients (monomers, free radicals, and C60 fullerene) as well as with the final products of a set of elementary reactions, which occurred in the course of the polymerization. The above objects, converted to the rank of digital twins, are considered simultaneously under the same conditions and at the same level of the theory. In terms of the polymerization passports of the reaction solutions, a complete virtual picture of the processes considered is presented.

This paper uses virtual simulations to examine the interaction between autonomous vehicles (AVs) and their surrounding environment. A framework was developed to estimate the environment’s complexity by calculating the real-time data processing requirements for AVs to navigate effectively. The VISTA simulator was used to synthesize viewpoints to replicate the captured environment accurately. With an emphasis on static physical features, roadways were dissected into relevant road features (RRFs) and full environment (FE) to study the impact of roadside features on the scene complexity and demonstrate the gravity of wildlife–vehicle collisions (WVCs) on AVs. The results indicate that roadside features substantially increase environmental complexity by up to 400%. Increasing a single lane to the road was observed to increase the processing requirements by 12.3–16.5%. Crest vertical curves decrease data rates due to occlusion challenges, with a reported average of 4.2% data loss, while sag curves can increase the complexity by 7%. In horizontal curves, roadside occlusion contributed to severe loss in road information, leading to a decrease in data rate requirements by as much as 19%. As for weather conditions, heavy rain increased the AV’s processing demands by a staggering 240% when compared to normal weather conditions. AV developers and government agencies can exploit the findings of this study to better tailor AV designs and meet the necessary infrastructure requirements.

The potential application of the digital twins concept and its principles to all industries, including agriculture, has recently been the subject of considerable research interest. Since livestock farming is one of the mainstays of the agricultural industry, contributing to food security as well as employment and income generation, digital twins appear to be a promising technology to address issues related to the intensive nature of livestock production systems, which are typically characterized by high levels of input and output. The objective of this study is to identify and analyze the latest scientific and technological contributions in the field of digital twins technology, with a view to its adoption and assimilation in livestock production systems in the context of Agriculture 5.0. In particular, the aim of this study is to synthesize the existing research and identify the opportunities and open issues related to the implementation of digital twins in livestock production systems. The results derived in the literature review indicate that digital twins is still in the early stages of development in livestock farming, but it has the potential to transform the industry. As Agriculture 5.0 evolves and the digital twins technology matures, it is expected to play an increasingly important role in the livestock farming industry.

This study introduces a pioneering structural equation modeling (SEM)-based framework to assess BIM-DT adoption readiness in sustainable construction. The research’s approach delves into the intricate correlation between key success factors (KSFs) and sustainable parameters (SPs), fostering BIM-DT deployment. This interdisciplinary perspective provides a holistic understanding of the impact of KSFs on BIM-DT adoption. The research aims to identify KSFs and parameters, prioritize their relative weights for BIM-DT implementation, analyze KSFs and SPs correlations, and offer practical insights into the findings. The results revealed the importance of sustainability considerations in BIM-DT adoption, with cost optimization and resource management playing pivotal roles. The findings also revealed that the readiness of the organization to adopt the technology, availability of technology operators, availability of standards and codes for the new technology, availability of knowledge, skills, and competencies to adopt the technology, and availability of funds and financial investment are the top-ranked KSFs contributing to the successful adoption of BIM-DT.

Multi-unmanned systems are primarily composed of unmanned vehicles, drones, and multi-legged robots, among other unmanned robotic devices. By integrating and coordinating the operation of these robotic devices, it is possible to achieve collaborative multitasking and autonomous operations in various environments. In the field of surveying and mapping, the traditional single-type unmanned device data collection mode is no longer sufficient to meet the data acquisition tasks in complex spatial scenarios (such as low-altitude, surface, indoor, underground, etc.). Faced with the data collection requirements in complex spaces, employing different types of robots for collaborative operations is an important means to improve operational efficiency. Additionally, the limited computational and storage capabilities of unmanned systems themselves pose significant challenges to multi-unmanned systems. Therefore, this paper designs an edge–end–cloud integrated multi-unmanned system payload management and computing platform (IMUC) that combines edge, end, and cloud computing. By utilizing the immense computational power and storage resources of the cloud, the platform enables cloud-based online task management and data acquisition visualization for multi-unmanned systems. The platform addresses the high complexity of task execution in various scenarios by considering factors such as space, time, and task completion. It performs data collection tasks at the end terminal, optimizes processing at the edge, and finally transmits the data to the cloud for visualization. The platform seamlessly integrates edge computing, terminal devices, and cloud resources, achieving efficient resource utilization and distributed execution of computing tasks. Test results demonstrate that the platform can successfully complete the entire process of payload management and computation for multi-unmanned systems in complex scenarios. The platform exhibits low response time and produces normal routing results, greatly enhancing operational efficiency in the field. These test results validate the practicality and reliability of the platform, providing a new approach for efficient operations of multi-unmanned systems in surveying and mapping requirements, combining cloud computing with the construction of smart cities.

Data infrastructures, economic processes, and governance models of digital platforms are increasingly pervading urban sectors and spheres of urban life. This phenomenon is known as platformization, which has in turn given rise to the phenomena of platform society, where platforms have permeated the core of urban societies. A recent manifestation of platformization is the Metaverse, a global platform project launched by Meta (formerly Facebook) as a globally operating platform company. The Metaverse represents an idea of a hypothetical “parallel virtual world” that incarnate ways of living and working in virtual cities as an alternative to smart cities of the future. Indeed, with emerging innovative technologies—such as Artificial Intelligence, Big Data, the IoT, and Digital Twins—providing rich datasets and advanced computational understandings of human behavior, the Metaverse has the potential to redefine city designing activities and service provisioning towards increasing urban efficiencies, accountabilities, and quality performance. However, there still remain ethical, human, social, and cultural concerns as to the Metaverse’s influence upon the quality of human social interactions and its prospective scope in reconstructing the quality of urban life. This paper undertakes an upper-level literature review of the area of the Metaverse from a broader perspective. Further, it maps the emerging products and services of the Metaverse, and explores their potential contributions to smart cities with respect to their virtual incarnation, with a particular focus on the environmental, economic, and social goals of sustainability. This study may help urban policy makers to better understand the opportunities and implications of the Metaverse upon tech-mediated practices and applied urban agendas, as well as assess the positives and negatives of this techno-urban vision. This paper also offers thoughts regarding the argument that the Metaverse has disruptive and substantive effects on forms of reconstructing reality in an increasingly platformized urban society. This will hopefully stimulate prospective research and further critical perspectives on the topic.