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Digital twin (DT) technology has garnered increasing attention across various sectors, particularly in the construction and road infrastructure domains. To fully realize its potential and systematically apply it in practice, adherence to a formalized approach is necessary. However, numerous DT-related standards and models currently exist, creating uncertainty in the selection of appropriate frameworks. Moreover, no widely accepted standard or reference model has yet been developed in the field of road infrastructure management. Therefore, this study examined the current standards and models employed in the adoption and implementation of DTs in road infrastructure management, focusing on their dimensions (layers) and functional components. A bottom-up approach was adopted by comprehensively reviewing the existing literature on road networks, bridges, tunnels, and other civil infrastructures and urban DTs. Ultimately, a DT framework was developed, comprising five core layers with their respective components and functionalities, to facilitate network-level integrated road infrastructure management. Moreover, the proposed framework’s implementation scenario enhances its applicability in the field. Overall, this study provides valuable insights for researchers and practitioners involved in DT implementation in infrastructure management and supports future standardization efforts in this domain.

The concept of digital twins is proposed as a new technology-led advancement to support the processes of the design, construction, and operation of built assets. Commonalities between the emerging definitions of digital twins describe them as digital or cyber environments that are bidirectionally-linked to their physical or real-life replica to enable simulation and data-centric decision making. Studies have started to investigate their role in the digitalization of asset delivery, including the management of built assets at different levels within the building and infrastructure sectors. However, questions persist regarding their actual applications and implementation challenges, including their integration with other digital technologies (i.e., building information modeling, virtual and augmented reality, Internet of Things, artificial intelligence, and cloud computing). Within the built environment context, this study seeks to analyze the definitions and characteristics of a digital twin, its interactions with other digital technologies used in built asset delivery and operation, and its applications and challenges. To achieve this aim, the research utilizes a thorough literature review and semi-structured interviews with ten industry experts. The literature review explores the merits and the relevance of digital twins relative to existing digital technologies and highlights potential applications and challenges for their implementation. The data from the semi-structured interviews are classified into five themes: definitions and enablers of digital twins, applications and benefits, implementation challenges, existing practical applications, and future development. The findings provide a point of departure for future research aimed at clarifying the relationship between digital twins and other digital technologies and their key implementation challenges.

The use of open-source software is crucial for the digitalization of manufacturing, including the implementation of Digital Twins as envisioned in Industry 4.0. This research paper provides a comprehensive comparison of free and open-source implementations of the reactive Asset Administration Shell (AAS) for creating Digital Twins. A structured search on GitHub and Google Scholar was conducted, leading to the selection of four implementations for detailed analysis. Objective evaluation criteria were defined, and a testing framework was created to test support for the most common AAS model elements and API calls. The results show that all implementations support at least a minimal set of required features while none implement the specification in all details, which highlights the challenges of implementing the AAS specification and the incompatibility between different implementations. This paper is therefore the first attempt at a comprehensive comparison of AAS implementations and identifies potential areas for improvement in future implementations. It also provides valuable insights for software developers and researchers in the field of AAS-based Digital Twins.

Being a cornerstone of Industry 4.0, Asset Administration Shell (AAS) enables seamless integration and interaction among the physical and digital worlds. There are multiple different tools and technologies available for implementing AAS. The purpose of this study is to support the tool and technology selection decision of AAS modelers and implementers. For that purpose, we conducted a literature survey and identified four active tools, and in the study, we included all of them: AASX server, Eclipse BaSyx, FA3ST service, and NOVAAS. Using a comprehensive criteria list, we conducted a thorough comparison of the selected technologies. The comparison was made in two steps: first for initial learning exercises and second for a real case study where digital twins belong to real assets in a facility belonging to the petrochemical industry. Among the evaluated tools, Eclipse BaSyx demonstrated superior performance compared to the other three tools investigated in this study. Future research will focus on incorporating machine learning (ML) and deep learning (DL) models associated with the assets, leveraging datasets generated by the sensors installed on the system.

The demand for individualized products drives modern manufacturing systems towards greater adaptability and flexibility. This increases the focus on data-driven digital twins enabling swift adaptations. Within the framework of cyber-physical systems, the digital twin is a digital model that is fully connected to the physical and digital assets. A digital model must follow a standardization for interoperable data exchange. Established ontologies and meta-models offer a basis in the definition of a schema, which is the first phase of creating a digital twin. The next phase is the standardized and structured modeling with static use-case specific data. The final phase is the deployment of digital twins into operation with a full connection of the digital model with the remaining cyber-physical system. In this deployment phase communication standards and protocols provide a standardized data exchange. A survey on the state-of-the-art of these three digital twin phases reveals the lack of a consistent workflow from ontology-driven definition to standardized modeling. Therefore, one goal of this paper is the design of an end-to-end digital twin pipeline to lower the threshold of creating and deploying digital twins. As the task of establishing a communication connection is highly repetitive, an automation concept by providing structured protocol data is the second goal. The planning and control of a line-less assembly system with manual stations and a mobile robot as resources and an industrial dog as the product serve as exemplary digital twin applications. Along this use-case the digital twin pipeline is transparently explained.

One of the most promising advancements in healthcare is the application of digital twin technology, offering valuable applications in monitoring, diagnosis, and development of treatment strategies tailored to individual patients. Furthermore, digital twins could also be helpful in finding novel treatment targets and predicting the effects of drugs and other chemical substances in development. In this review article, we consider digital twins as virtual counterparts of real human patients. The primary aim of this narrative review is to give an in-depth look into the various data sources and methodologies that contribute to the construction of digital twins across several healthcare domains. Each data source, including blood glucose levels, heart MRI and CT scans, cardiac electrophysiology, written reports, and multi-omics data, comes with different challenges regarding standardization, integration, and interpretation. We showcase how various datasets and methods are used to overcome these obstacles and generate a digital twin. While digital twin technology has seen significant progress, there are still hurdles in the way to achieving a fully comprehensive patient digital twin. Developments in non-invasive and high-throughput data collection, as well as advancements in modeling and computational power will be crucial to improve digital twin systems. We discuss a few critical developments in light of the current state of digital twin technology. Despite challenges, digital twin research holds great promise for personalized patient care and has the potential to shape the future of healthcare innovation.

Industry 4.0 is revolutionizing industrial production by bridging the physical and the virtual worlds and further improving digitalization. Two essential building blocks in industry 4.0 are digital twins (DT) and the internet of things (IoT). While IoT is about connecting resources and collecting data about the physical world, DTs are the virtual representations of resources organizing and managing information and being tightly integrated with artificial intelligence, machine learning and cognitive services to further optimize and automate production. The concepts of DTs and IoT are overlapping when it comes to describing, discovering and accessing resources. Currently, there are multiple DT and IoT standards covering these overlapping aspects created by different organizations with different backgrounds and perspectives. With regard to interoperability, which is presumably the most important aspect of industry 4.0, this barrier needs to be overcome by consolidation of standards. The objective of this paper is to investigate current DT and IoT standards and provide insights to stimulate this consolidation. Overlapping aspects are identified and a classification scheme is created and applied to the standards. The results are compared, aspects with high similarity or divergence are identified and a proposal for stimulating consolidation is presented. Consensus between standards are found regarding the elements a resource should consist of and which serialization format(s) and network protocols to use. Controversial topics include which query language to use for discovery as well as if geo-spatial, temporal and historical data should be explicitly supported.

Digital twins (DTs) are one of the latest technology trends in all industries. However, DT development in the architecture, engineering, and construction (AEC) industry is still in its infancy. Digital twins have been proposed as tools that can be applied to several challenges in various areas of the built environment. However, their widespread use is hampered due to the slow pace of digitization of the AEC industry, in addition to the absence of a formalized standard for digital twins’ implementation. We began this study by systematically reviewing publications related to DT applications in the AEC industry in four databases, resulting in 229 publications after applying the proposed criteria. The systematic review highlighted the lack of standardization for DTs in the AEC industry. Additionally, this study assessed the current status of DTs and analyzed the evolution of the concept of DTs in the AEC industry. We also proposed a conceptual framework for DT development for construction assets, using the existing BIM information management standards (i.e., ISO 19650) to promote a better interoperable digitalized built environment.

This study delves into the critical issue of Digital Twin (DT) technology standardization, focusing on introducing a novel approach called “correspondence measure.” We draw upon extensive literature review and case study analyses to investigate existing standardization methods, identifying sector-specific and problem-specific strategies. We also underline the importance of interoperability, data privacy and security, real-time synchronization, and accuracy and fidelity in the standardization of DTs. Unveiling the intricacies of the “correspondence measure,” we elucidate its potential in enhancing the standardization process by providing a standardized measure of the accuracy and reliability of a digital twin concerning its physical counterpart. Furthermore, we discuss the applications of our proposed method in various sectors such as manufacturing, healthcare, aerospace, maritime and shipping, and city management. Our findings suggest that the “correspondence measure” can significantly contribute to the existing standardization approaches by facilitating a better understanding of the DT's behavior, thereby fostering trust in these digital replications. This paper not only offers a theoretical contribution to the literature on DT standardization but also provides practical insights for the stakeholders involved in developing, implementing, and managing digital twins.

Building Information Modelling (BIM) – Geographic information System (GIS) data integration constitutes a crucial component in the development of urban digital twins. This integration can be achieved through a variety of techniques and methodologies; however, numerous challenges arise during the integration process that hinder seamless interoperability. Although several attempts have been made to address these challenges, prior studies tend to address these challenges in isolation, and the literature stills lacks from a standardized approach to perform the integration. This paper proposes a conceptual framework designed to support standardizing the integration practices by synthesizing these challenges into a progressive, structured model. The framework consists of four Levels of Integration (LoInt) that are designed from LoInt100 to LoInt400, each level identifies and categorizes integration challenges into distinct classes and provides a literature-based approach to address the challenges. Moreover, each level corresponding to the degree of interoperability between BIM and GIS systems. The framework can be utilized as a guideline and as a decision-making support system in applications that require BIM-GIS data integration.