A Digital Twin Applications in Rail Systems: A Real-Time Monitoring Framework Based on BIM–GIS–IoT Integration

Digital twin applications in radiology and radiotherapy: Applications, challenges, and future perspectives.

Construction 4.0 is witnessing exponential growth in digital twin (DT) technology developments and applications, revolutionizing the adoption of building information modelling (BIM) and other emerging technologies used throughout the built environment lifecycle. BIM provides technologies, procedures, and data schemas representing building components and systems. At the same time, the DT enhances this with real-time data for integrating cyber-physical systems, enabling live asset monitoring and better decision making. Despite being in the early stages of development, DT applications have rapidly progressed in the AEC sector, resulting in a diverse literature landscape due to the various technologies and parameters involved in fully developing the DT technology. The intricate complexities inherent in digital twin advancements have confused professionals and researchers. This confusion arises from the nuanced distinctions between the two technologies, i.e., BIM and DT, causing a convergence that hinders realizing their potential. To address this confusion and lead to a swift development of DT technology, this study provides a holistic review of the existing research focusing on the critical components responsible for developing the applications of DT technology in the construction industry. It highlights five crucial elements: technologies, maturity levels, data layers, enablers, and functionalities. Additionally, it identifies research gaps and proposes future avenues for streamlined DT developments and applications in the AEC sector. Future researchers and practitioners can target data integrity, integration and transmission, bi-directional interoperability, non-technical factors, and data security to achieve mature digital twin applications for AEC practices. This study highlights the growing significance of DTs in construction and provides a foundation for further advancements in this field to harness its potential to transform built environment practices. It also pinpoints the latest developments in AI, namely the large language model (LLM) and retrieval-augmented generation (RAG)’s implications for DT education, policies, and the construction industry’s practices.

Construction digital twin: a taxonomy and analysis of the application-technology-data triad

BackgroundMaintaining 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.MethodologyEmploying 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.ResultsThe 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.ConclusionsTherefore, 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.

The new economic context has brought new challenges to the supply chain and has increased the complexity of its processes. The digitalization; as one of these challenges, is a rapidly evolving paradigm that transforms supply chains by integrating data and communication technologies to optimize operations, enhance sustainability, and improve overall performance. Digital twin technology emerged as one of the most promising digital tools that offer an innovative approach to supply chain management. However, the adoption of digital twins in the supply chain is still in its early stages. Previous research papers presented limited overviews of the applications of digital twin technology in supply chain systems that need to be extended, as it is inevitably a work in progress. In this matter, we conducted a systematic literature review built upon 31 articles to determine the applications of supply chain digital twins (SCDT). This study is divided into three core themes; the first is a comprehensive review of the paradigm of digital supply chain with a focus on digital twin technology and its primary features. The second theme presents an analysis of the 31 papers where we explore the different purposes of SCDTs and their integration. in the third theme by using VOSviewer to conduct a network analysis. We aim; through this paper, to contribute significantly to the supply chain management field by summarizing and analyzing existing research and developments in the applications of digital twins in the different areas of supply chains.

Background: The integration of smart technologies into modern facilities has underscored the need for proactive maintenance strategies to minimize unplanned equipment failures and enhance operational efficiency. Traditional maintenance approaches, including reactive and time-based preventive maintenance, often fall short in dynamic building environments. Predictive maintenance, driven by machine learning (ML) and Internet of Things (IoT) sensor data, offers a data-driven solution to anticipate equipment failures before they occur. Methodology: This study proposes a comprehensive machine learning framework for predictive maintenance in smart facilities, evaluated using the ASHRAE Great Energy Predictor III dataset—a real-world benchmark containing operational data from diverse building systems. The framework compares both classical machine learning (Random Forest, XGBoost) and deep learning (LSTM) approaches to address different predictive maintenance scenarios. Data preprocessing included outlier removal, missing value imputation, feature engineering, and normalization. Model evaluation was conducted using precision, recall, F1-score, ROC-AUC, and inference time metrics. The system is designed for seamless integration with existing Computerized Maintenance Management Systems (CMMS) to ensure practical deployment. Results: Among the models tested, the LSTM network achieved the highest predictive performance (F1-score: 0.89, ROC-AUC: 0.93), while XGBoost provided an optimal balance between accuracy (F1-score: 0.84) and computational efficiency (10ms inference time). Implementation resulted in a 40% reduction in maintenance response time, 25% cost savings, and 47% decrease in unplanned downtime. The framework demonstrates strong scalability across different facility types and equipment classes. A revised bar chart (Fig. 2) has been included with enhanced visual clarity through color-coded bars. Additionally, Appendix A outlines the practical steps for integrating the framework with CMMS platforms. Conclusion: The developed machine learning framework effectively bridges the gap between research and practical implementation, offering a versatile solution for predictive maintenance in smart facilities. Its compatibility with CMMS platforms and demonstrated performance on real-world data (ASHRAE dataset) positions it as a viable tool for intelligent facility operations. Future work will focus on edge computing deployment, including strategies such as model quantization and device-level optimization, and expansion to additional industrial applications.

PurposeThis paper aims to investigate the theoretical and practical links between digital twin (DT) application in heritage facilities management (HFM) from a life cycle management perspective and to signpost the future development directions of DT in HFM.Design/methodology/approachThis state-of-the-art review was conducted using a systematic literature review method. Inclusive and exclusive criteria were identified and used to retrieve relevant literature from renowned literature databases. Shortlisted publications were analysed using the VOSviewer software and then critically reviewed to reveal the status quo of research in the subject area.FindingsThe review results show that DT has been mainly adopted to support decision-making on conservation approach and method selection, performance monitoring and prediction, maintenance strategies design and development, and energy evaluation and management. Although many researchers attempted to develop DT models for part of a heritage building at component or system level and test the models using real-life cases, their works were constrained by availability of empirical data. Furthermore, data capture approaches, data acquisition methods and modelling with multi-source data are found to be the existing challenges of DT application in HFM.Originality/valueIn a broader sense, this study contributes to the field of engineering, construction and architectural management by providing an overview of how DT has been applied to support management activities throughout the building life cycle. For the HFM practice, a DT-cum-heritage building information modelling (HBIM) framework was developed to illustrate how DT can be integrated with HBIM to facilitate future DT application in HFM. The overall implication of this study is that it reveals the potential of heritage DT in facilitating HFM in the urban development context.

Industrial digital twins have become popular in China and around the world in various fields in the past a few years. The application of industrial digital twins in nuclear power plants is to be researched in this paper. This article mainly summarizes the concept of industrial digital twin and its application status; sets up an application system of industrial digital twin in nuclear power plants, including the overall framework and application mode; introduces some typical application of digital twins in nuclear power plants; explores the expectation, challenges of the application of digital twins in the development of future intelligent nuclear power plant.

Digital twin (DT) technology, which creates virtual representations of physical systems to optimize their life-cycle, has drawn significant attention across various industries. The automotive and aviation industries have been pioneers in adopting DTs for enhanced efficiency, predictive maintenance, and real-time decision-making. However, the maritime industry, crucial to global trade and logistics, has lagged in DT implementation. This paper aims to bridge this gap by systematically surveying DT applications in the automotive and aviation industries and exploring how this knowledge can be transferred to the maritime industry. By analyzing existing literature, identifying key trends, and summarizing best practices, a comprehensive roadmap is provided for maritime industry adoption of DT technology. The surveyed papers are selected systematically following the PRISMA statement and categorized based on characteristics such as single vs. multiple systems, modeling methods (model-driven, data-driven, and hybrid), and life-cycle phases. We introduce DT models using a five-dimensional framework and analyze their characteristics in terms of research object, subsystem application, and modeling method. Additionally, DT applications from a product life-cycle perspective, covering design, manufacturing, operation, and maintenance phases are examined. Knowledge transfer from the automotive and aviation industries to the maritime industry is summarized. In the automotive industry, DTs enhance vehicle efficiency and safety, particularly for autonomous and electric vehicles. Aviation DT research focuses on predictive maintenance, pilot training, and real-time monitoring to improve operational efficiency and safety. The maritime industry faces data challenges and operational complexity but has significant potential for DTs to enhance ship performance, safety, and predictive maintenance.

Recent structural health monitoring (SHM) strategies in civil engineering increasingly leverage digital twins, which digitally represent the structures being monitored as well as the SHM systems installed on the structures. Despite the widespread adoption of digital twin applications in recent years in SHM, there is a lack of agreement on a common definition of digital twins. Furthermore, there is no consensus on digital twin architectures and on the internal elements that constitute digital twins. A common digital twin definition would advance digital twin implementations and operability, and insights into digital twin architectures and digital twin elements would be vital to enhance the reliability and performance of digital-twin-based SHM systems. A significant number of digital twin definitions have been proposed and a plethora of reviews have been published; however, little emphasis has been given to digital twin architectures and internal elements. This paper presents a multivocal review of digital twins in civil engineering, aiming to provide a panorama of the digital twin landscape in civil engineering with explicit insights into the architectures and internal elements used in digital twin applications. From a methodological standpoint, the review follows a twofold approach that encompasses (i) peer-reviewed, indexed literature (“white literature”) as well as (ii) non-indexed sources (“gray literature”) that include industrial digital twin applications. Besides the multivocal review, a generic digital twin reference architecture is drawn from the review results and a digital twin definition is formulated both in an informal and formal (i.e., mathematical) manner. It is expected that the generic reference architecture and the definitions proposed in this study may serve as a blueprint for digital-twin-based SHM applications, with significant implications for researchers, practitioners, and policymakers in structural health monitoring.

Digital twin application in the construction industry: A literature review

As health-care budgets are continuously under increasing demands, Artificial Intelligence resources such as digital heart twins could save millions of dollars by predicting results and preventing unnecessary surgery. Can we start to make digital human body twins to plant and predict health outcomes for a patient? By using a way to design competent simulation models from real objects, digital twins were created through IoT. But the digital twin is a complicated system and a very long-drawn step away from its possibilities. Researchers must design all components of entities or structures. There is a need to collect and merge various types of data. Many engineering researchers and participants aren’t sure about which technologies and resources to use. The 3D digital twin model offers a reference guide for digital twin comprehension and implementation. This paper aims to investigate and outline the recent technologies and tools used for digital twin applications from a 3-D digital model perspective, such as references to technologies and tools for future digital twin applications.

Abstract: Digital twin technology, which creates virtual replicas of physical assets, processes, and systems, is transforming industries by enabling real-time monitoring, simulation, and optimization. This book chapter explores the fundamental principles, key components, and diverse applications of digital twins across various sectors, including manufacturing, healthcare, energy, automotive, and smart cities. Through detailed case studies, the chapter illustrates the successful implementation and significant benefits of digital twins, such as enhanced operational efficiency, cost reduction, improved risk management, and accelerated innovation. It also addresses the technical challenges, security and privacy concerns, and regulatory issues associated with digital twin technology. Looking forward, the chapter highlights future trends and developments, predicting advancements in AI, edge computing, 5G, and quantum computing that will further enhance digital twin capabilities. The chapter concludes with a forward-looking perspective on the transformative potential of digital twins in shaping a smarter, more connected, and sustainable world. Keywords: Digital Twin Technology,Real-Time Monitoring,Simulation and Optimization,Manufacturing Efficiency,Predictive Maintenance,Healthcare Innovation,Energy Management,Smart Cities,IoT Integration,AI and Machine Learning,Edge Computing,5G Connectivity,Quantum Computing,Operational Efficiency,Risk Management,Sustainability,Industry 4.0,Virtual Prototyping and Future Trends in Technology. Condori, P. P. C. (2022). Digital Twin in Development of Products. Digital Twin Technology, 205–218. Portico. https://doi.org/10.1002/9781119842316.ch13 Fryer, T. (2019). Digital Twin - Introduction. This is the age of The Digital Twin. Engineering & Technology, 14(1), 28–29. https://doi.org/10.1049/et.2019.0125 Gnanamalar, R. H. (2024). Human Digital Twin Processes and their Future. Transforming Industry Using Digital Twin Technology, 187–217. https://doi.org/10.1007/978-3-031-58523-4_10 Korhan, O. (2023). Introductory Chapter: Digital Twin Technology. Digital Twin Technology - Fundamentals and Applications. https://doi.org/10.5772/intechopen.113345 Mythily, M., David, B., & Vijay, J. A. (2024). Digital Twin Application in Various Sectors. Transforming Industry Using Digital Twin Technology, 219–237. https://doi.org/10.1007/978-3-031-58523-4_11 Seolin Galindo, E., & Chagas, U. (2023). Perspective Chapter: Digital Twin Applied in the Brazilian Energy Sector. Digital Twin Technology - Fundamentals and Applications. https://doi.org/10.5772/intechopen.112598

A digital twin is a virtual counterpart of a physical object or system based on precise data collection. Although digital twin applications are gaining traction in the virtual reconstruction of built heritage, their relevance in archaeological sites remains limited, especially for those with only foundations. The Pishan (毘山) site in Huzhou, featuring the remains of a high-platform building and a large stilt-style architecture, represents the largest settlement site from the late Shang (商, ca. 1600 – 1046 BCE) and early Western Zhou (西周, 1046 – 771 BCE) dynasties in Zhejiang Province, China. At present, confronting contradictions among preservation, restoration, and reuse as a heritage park, the site leverages digital twin technologies to address two concerns: (i) reconstructing a 3D scene for further restoration and related studies and (ii) integrating multimedia to enhance visitors’ experiences and dissemination. Photogrammetry, unmanned aerial vehicle, and a mirrorless camera are employed to collect sky and ground dual graphic data and reconstruct the 3D model of the loess terrace. A panoramic roaming environment is created through panoramic photography. Geographic information system is integrated to enable visual analysis and information management while building information modeling facilitates the integration of parametric modeling and point cloud, aiding virtual restoration research. In conclusion, a workflow entitled “Virtual Reconstruction – Management and Analysis – Restoration – Exhibition” is proposed, promising in-depth exploration in further studies.

In line with the current construction revolution, it is time for the construction industry to embrace innovation and technology. This is corresponding with the National Construction Policy 2030 (NCP 2030) that comes out with the aim to digitalize the entire construction industry towards the IR 4.0. The focus is to boost the nation's construction industry's competitiveness and recognition worldwide. The construction industry has undergone a significant transformation in recent years such as BIM, IoT including Digital Twin due to the incorporation of digital technologies. A digital twin is a virtual representation of a physical asset. It is still a relatively new concept in the construction industry, but it offers an innovative method for improving cost management strategies in construction projects. Applications of the digital twin in construction cost management have the potential to revolutionize conventional methods. Therefore, this study seeks to determine the level of understanding of construction industry players on the concept of digital twin applications in construction cost management by providing the concept and to explore the challenges and strategies in implementing the digital twin applications in construction cost management. This research employed a mixed-method approach by means of questionnaire survey and interview for data collection. 35 samples that consist of construction industry players from different organizations participated in this study. The data collected from the survey and interview are analyses through descriptive and content analysis. Overall, the findings find out the understanding of digital twin applications with its challenges and strategies to overcome it. This research contributes to the body of knowledge regarding digital twin applications and construction cost management.