Digital Twins’ Application for Geotechnical Engineering: A Review of Current Status and Future Directions in China

Purpose Digital twins (DTs) offer promising advances in geotechnical engineering by improving prediction accuracy, operational efficiency and risk management. However, their adoption in the field remains limited compared to other industries such as manufacturing and civil engineering. This study reviews current DT applications in geotechnical engineering, examining research trends, enabling technologies and key challenges. By identifying gaps and future directions, this study aims to facilitate DT integration into underexplored areas and advance technological capabilities for more data-driven decision-making. Design/methodology/approach A systematic review of DT applications in geotechnical engineering was conducted, analysing publication trends, key research areas and enabling technologies. The study examines the integration of DT with machine learning, real-time data acquisition and interoperability challenges. Findings highlight research gaps and propose strategies for overcoming barriers to adoption, ensuring DTs’ effective application in diverse geotechnical contexts. Findings DT research in geotechnical engineering is fragmented, with most studies focused on tunnelling and slopes, while areas such as soil mechanics remain underexplored. The increasing integration of DT with machine learning is improving predictive capabilities, but challenges such as interoperability, scalable platforms and real-time data acquisition are hindering widespread adoption. Addressing these issues is critical to advancing DT applications and improving geotechnical project outcomes. Originality/value This study provides a structured assessment of DT adoption in geotechnical engineering, identifying key gaps and opportunities. By highlighting the need for interoperable platforms, broader applications and technological advances, it provides insights for researchers and practitioners. Addressing these challenges will help position DTs as transformative tools, enabling more efficient and data-driven geotechnical engineering.

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

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.

Digital twin application in the construction industry: A literature review

The digital twin (DT) presents an opportunity for the integration of the physical world into the digital world. DT technology has the potential to transform the construction industry and respond to some of its challenges. In conventional construction projects, progress is largely monitored by direct observation and measurement which suffers from numerous challenges, including low productivity, blunders, and poor technology advancements. Concerns are now being raised about integrating technology for autonomously monitoring building activity. In other sectors, DT technology has been responsible for saving product development time and costs by up to 50%. However, DT is still lagging the adoption of new technologies in the construction industry. The overarching aim of this study was to explore the adaptability of DT in construction site progress monitoring. This study comprehensively reviews and analyses DT concepts, technologies, and applications in the construction industry, parameters of applications of DT in construction site progress monitoring, how DT could be used for site progress monitoring in construction, common challenges in the implementation of DT in site progress monitoring, and strategies such as barriers related to DT in site progress monitoring, using literature findings while incorporating qualitative analysis of semi-structured interviews. This research shows that DT has a high potential to solve the numerous challenges in construction site progress monitoring, rather than other current technologies in use. Thus, this study raises awareness and the need for the application of DT in construction site progress monitoring

Digital Twin (DT) technology creates a dynamic virtual representation of a physical system using real-time data and computational modeling. While DTs have demonstrated profound impact in several medical disciplines, their translation into dentistry is still emerging and has not been comprehensively mapped. To systematically review and delineate the current applications, technological advancements, and prospective opportunities of digital twin (DT) technology in dentistry. A scoping review was conducted following the Joanna Briggs Institute (JBI) methodology and reported according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) guidelines. A systematic search of MEDLINE (PubMed), EMBASE, Scopus, and Web of Science identified English-language publications from January 2000 to April 2025. All empirical and conceptual studies describing DT development, validation, and/or application in dental contexts were eligible. Two reviewers independently conducted screening and study selection, with a third reviewer resolving discrepancies. No automation tools were used. A total of 5989 records were retrieved, and 7 studies met the inclusion criteria. Included studies represented orthodontics, prosthodontics, endodontics, and dental education. DT applications primarily involved: patient-specific virtual modeling for diagnosis and treatment simulation, predictive or performance-monitoring frameworks using biomechanical/algorithmic analysis, and simulation-based skill training. Most were conceptual or prototype studies with small samples and limited clinical validation. DT technology has substantial potential to enhance precision, simulation, monitoring, and personalization in dentistry. However, current evidence remains constrained by fragmented research, methodological inconsistency and insufficient clinical validation. Future adoption of DT requires standardized data pipelines, robust ethical and regulatory frameworks and interdisciplinary collaboration to achieve clinically meaningful and widely adoptable DT integration in dental care.

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

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.

Objectives To describe the potential of digital twin (DT) technology to enhance personalised root canal treatment within endodontics, drawing upon its established applications in various healthcare sectors.Discussion DT models are gaining traction as transformative tools for enabling individualised decision-making across different medical disciplines. These models leverage multimodal patient data to simulate physiological and clinical outcomes. In endodontics, DTs could facilitate the simulation of intricate parameters, such as root canal morphology, access cavity preparation, microbial biofilm dynamics, disinfection protocols, clinical techniques for root canal preparation and filling, and the long-term behaviour of restorations. The application of DTs will empower clinicians to formulate more tailored treatment plans and improve prognostic predictions. Beyond their clinical applications, DTs can enrich research settings, linking laboratory research with tailored patient care. While deploying DTs in endodontics remains largely aspirational at this stage, it has the potential to shift the paradigm from standardised approaches to personalised treatments. Key challenges to address include data standardisation, interoperability among systems, ethical regulations, and the need for specialised clinician training. This article suggests actionable strategies for the translational development of DTs in endodontics, inspired by successful frameworks in other medical domains.Conclusion DT models can reshape the vision in endodontics, facilitating real-time, patient-specific simulation and clinical decision-making. Moreover, DT technology presents a cohesive framework that could enhance precision in endodontic practice while also expediting the translation of research findings into clinical applications. This advancement may lead to the development of personalised and predictive approaches to root canal treatment, significantly improving patient outcomes.

Digital Twin (DT) technology is a pivotal innovation within the built environment industry, facilitating digital transformation through advanced data integration and analytics. DTs have demonstrated significant benefits in building design, construction, and asset management, including optimising lifecycle energy use, enhancing operational efficiency, enabling predictive maintenance, and improving user adaptability. By integrating real-time data from IoT sensors with advanced analytics, DTs provide dynamic and actionable insights for better decision-making and resource management. Despite these promising benefits, several challenges impede the widespread adoption of DT technology, such as technological integration, data consistency, organisational adaptation, and cybersecurity concerns. Addressing these challenges requires interdisciplinary collaboration, standardisation of data formats, and the development of universal design and development platforms for DTs. This paper provides a comprehensive review of DT definitions, applications, capabilities, and challenges within the Architecture, Engineering, and Construction (AEC) industries. This paper provides important insights for researchers and professionals, helping them gain a more comprehensive and detailed view of DT. The findings also demonstrate the significant impact that DTs can have on this sector, contributing to advancing DT implementations and promoting sustainable and efficient building management practices. Ultimately, DT technology is set to revolutionise the AEC industries by enabling autonomous, data-driven decision-making and optimising building operations for enhanced productivity and performance.

Digital twin technology, originally developed for industrial applications, is gaining increased attention in ocean governance and multilateral negotiations. While existing research gives insights into the scope of digital ocean twin applications for environmental governance and associated technical challenges, they do not sufficiently explore how their development and use takes place across politically contentious spaces in which various public and private actors operate. To address this gap, our paper pursues two research questions: ‘Who develops and uses digital twins of the oceans and for which purposes?’ and ‘Which promises and risks are associated with digital ocean twins in the context of multilateral negotiations?’. The paper is based on empirical bibliometric research into academic literature, patents, and policy documents to identify political, scientific, and corporate actors involved in developing and applying digital ocean twins and to map their discourses. By offering a holistic view of how digital twins are, or could be, applied in ocean governance, this paper aims to contribute to the development of effective, technology-driven, but also politically sensible approaches environmental governance.

The digital twin technology platform has not yet achieved the expected acceptance and wider implementation in the maritime industry. So far, most of the focus of the digital twin application discussions have centred around what to learn from big data in ship operation, and to a lesser extent, has anybody extended this discussion to include the benefits such new technology can contribute to the enhancement of the upstream ship concept and basic design activities, as well as detailed engineering. This paper particularly pays attention to this latter, partly forgotten, application area. There could be many reasons behind such a reluctance to take on new technology and utilize it to its full potential. It is hypothesized and argued by this article that the development has focused on applications that are too complex, too expensive and reflect, to a little extent, real-life needs. Lack of effective data transfer and transaction interphases among relevant stakeholders is another important factor creating these inefficiencies. This paper document how and why such inefficiencies in novel digitization technology adoption and adaptation exist and hamper the progress of achieving noticeable benefits of such implementations and how such development hurdles can be eliminated. Real-life user cases and several contributions in the professional literature suggest that more effective implementation of digital twin technology requires further discussions and investigations relating to three important aspects: i) a common and accepted definition of what is a digital twin; ii) an agreed-upon scalable and systemic approach to what is the solution space for a digital twin solution and iii) which systemic method to be used for digital twin development. Digital-twin technology must combine effective ship in operation and ship design feedback and feed forwarding, including their inherent people involvement and market behaviour. This article reviews the status of digital twin technology in the maritime domain and proposes a common definition of the digital twin. The latter part of the article proposes a systemic perspective for effective digital twin development and a method for a goal-oriented digital twin development in the novel ship design domain as well for ships in operations. Real-life user-case examples are elaborated upon to support our suggestions for improvement. The paper summarizes that, in its current form, the success rate of the digital twin technology implementation is so far, limited. Thus, the short- and long-term benefits to be achieved from digital twin applications in relation to vessel operations and their designs are also limited. This paper advises ways for improvement of the present situation.

The increasing digitalization of the construction industry, driven by Building Information Modeling (BIM) and the rise of digital twins, necessitates a holistic approach to worker well-being. Understanding how digital tools and processes, including BIM-based workflows and digital twin applications, impact the psychological and physiological states of construction workers is crucial for improving safety, productivity, and overall job satisfaction. This study integrates construction practices and neuroscience by systematically reviewing quantitative parameters and tools for assessing worker well-being within various digital construction workflows, with a specific focus on BIM and digital twin applications. We identify key stress detection parameters (e.g., EDA, HRV) and tools from medical research applicable to construction management for enhancing worker well-being and mitigating risks. A comprehensive literature review synthesizes findings from multiple disciplines, focusing on stress detection techniques and their application in optimizing digital construction processes, specifically within BIM-driven projects and the development and utilization of digital twins. Results highlight stress detection parameters and tools offering valuable insights into worker experience, emphasizing the need for both qualitative and quantitative measures in project management, particularly within the context of BIM and digital twin technologies. A holistic, interdisciplinary approach merging ergonomics, neuroscience, and construction methodologies is crucial for enhancing worker experience in increasingly digitalized construction environments. Integrating stress detection technologies into construction management processes, especially those leveraging BIM and digital twins, is essential for promoting worker well-being and safety, while acknowledging limitations in current systematic research. Future exploration includes developing human-centered digital tools within BIM and digital twin workflows and applying medical findings to improve construction workflows. This research aims to inspire construction professionals to prioritize worker well-being and adapt their methodologies to address the unique challenges of digital transformation in the industry, leveraging the potential of BIM and digital twins to create safer and more productive work environments.

The architecture, engineering and construction (AEC) industry is currently encountering numerous challenges and actively pursuing industrial upgrades through digital transformation. Digital twin (DT) technology aligns well with the industry's evolving needs due to its capabilities in data integration, intelligent decision-making, and effective management of project progress, efficiency, and quality. This study conducts a comprehensive literature review to analyze the strengths, weaknesses, opportunities, and threats (SWOT) associated with DT applications in the AEC sector. Additionally, field visits and semi-structured interviews were conducted on a selected construction project where DT was implemented, allowing for an in-depth examination of both its significant benefits and potential limitations. To further evaluate and prioritize the identified SWOT factors, a SWOT-AHP analysis was performed. The results indicate that the AEC industry has a strong interest in DT's advantages, particularly its potential to enhance resource allocation and process efficiency. Furthermore, the analysis reveals that the strategic quadrilateral occupies the largest area in the fourth quadrant (0.8554), with the strategic center of gravity located at (0.2321, -0.04135), suggesting that Strengths-Threats (ST) strategies should be implemented to support the future development and adoption of DT. Based on this strategic framework, the study formulates actionable development strategies to facilitate the effective integration and widespread adoption of DT in the AEC industry.

The latest advancement in digital technologies has greatly revolutionized modern manufacturing processes, particularly through the adoption of Lean Manufacturing initiatives aimed at minimizing wastage and enhancing operational efficiency. Predictive Maintenance (PM) being one of the primary drivers of transformation in lean manufacturing by reducing equipment downtime and optimizing asset performance. The lack of failure data is one of the biggest obstacles to PM deployment because traditional maintenance methods are used to maintain equipment after they break down. In order to address the issue of data scarcity, this study investigates the use of Digital Twin (DT) technology, which creates a virtual duplicate of the physical item and enables real-time monitoring utilizing sensors and Internet of Things devices for predictive analysis. IoT and data analytics are well complemented by digital twin technology, giving the manufacturer access to real-time information about the state of the machines while they are operating. This connectivity allows them to predict future asset failures accurately and strategically schedule maintenance activities in advance. The findings presented in this paper demonstrate that digital twin applications can reduce maintenance costs by 35% and machine uptime by 98%. It also presents case studies of DT application across different industries, and comparative study of positive impacts achieved through DT adoption. Cumulatively, the study highlights DT's transformational capability to facilitate lean initiatives and demands further investigation into integrations of emerging technology for process improvement.