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The main purpose of this study is to provide insight into the trend of AI-BIM integration, which has been studied by scholars around the world. To begin, a systematic review and bibliometric analysis was conducted to investigate English articles published between 2015 and 2022. This paper presents a systematic, scientometric, science mapping analysis through qualitative and quantitative evaluation and co-occurrence methods using VOSviewer, CiteSpace, and Gephi software. Conclusions indicate future research should concentrate on integrating AI and other smart systems with BIM to enhance digitalization and improve outcomes throughout the construction project life cycle. Based on the qualitative and quantitative evaluation of each scope (BIM and AI) and their status quo, this study suggests integrating the following domains with BIM to reduce complexity in the construction industry in the future: robotics, cloud systems, AIOT, digital twins, 4D printing, and block chain. [ABSTRACT FROM AUTHOR]

In the architecture, engineering, and construction industries, the integration of Building Information Modeling (BIM) has become instrumental in shaping the design and commissioning of smart buildings. At the center of this development is the pursuit of more intelligent, efficient, and sustainable built environments. The emergence of smart buildings equipped with advanced sensor networks and automation systems increasingly requires the implementation of Digital Twins (DT) for the direct coupling of BIM methods for integral building planning, commissioning, and operational monitoring. While simulation tools and methods exist in the design phase of developing advanced controls, their mapping to construction or post-construction models is less well developed. Through systematic, keyword-based literature research on publisher-independent databases, this review paper gives a comprehensive overview of the state of the research on BIM integration of building control systems with a primary focus on combined controls for daylight and artificial lighting systems. The review, supported by a bibliometric literature analysis, highlights major development fields in HVAC controls, failure detection, and fire-detection systems, while the integration of daylight and artificial lighting controls in Digital Twins is still at an early stage of development. In addition to already existing reviews in the context of BIM and Digital planning methods, this review particularly intends to build the necessary knowledge base to further motivate research activities to integrate simulation-based control methods in the BIM planning process and to further close the gap between planning, implementation, and commissioning. [ABSTRACT FROM AUTHOR]

Digital twin (DT) as a virtual replica of a physical asset, can enhance the design, construction, operation, and maintenance of physical infrastructure. This paper aims to review scientific articles using a bibliometric analysis approach to present an overview of research trends and explore the challenges and opportunities related to DT-enabled infrastructures. Bibliographic coupling, co-authorship, citation, and keyword occurrence analysis address the study's research questions. This review contributes to advancing the knowledge of DT and infrastructure integration while introducing the most recent trends, dominant research topics, the most used technologies with DT-enabled infrastructures, DT application in infrastructure projects, identifying gaps, and defining future research topics. Research on DT and infrastructure integration is primarily conducted in silos, implying that ideas and initiatives should be exchanged between researchers. DT application at the macro level of urban infrastructures and their management and planning will be the direction of future studies. [ABSTRACT FROM AUTHOR]

Objective: To understand the current status of research in the field of metaverse, and to analyze the research progress and evolutionary trends in this field. Methods: Based on the bibliometric analysis, a total of 921 papers were obtained by searching the Web of science core database for the keyword "metaverse". CiteSpace was used to visualize and analyze the current status and trends of metaverse research in China. Results: Ireland is currently the country with the highest research impact. China is currently the country with the largest number of publications in this field, but the impact of the research is insufficient. The current research in the highly cited literature focuses on technical and history reviews of the metaverse as well as its development in the field of education. Artificial Intelligence and utaut2 are the underlying clusters of cited literature in this research area. Several research hotspots have been formed, such as virtual reality, augmented reality, mixed reality, digital twins and artificial intelligence. Conclusion: The current research on metaverse in various fields is basically in its infancy, but has a great potential for future development and will gradually penetrate into many different directions with many challenges. [ABSTRACT FROM AUTHOR]

This article systematically summarises the digital twin technology in the existing scientific field using the bibliometric method, puts forward the critical areas and research directions at different stages of the life cycle of the digital twin research topic. Using R-Studio 'Biblioshiny' to measure scientifically, we visually analyze 514 articles related to digital twin in the Web of Science's core set from 2014 to 2021 (including SCI-Expanded and SSCI database). Firstly, we analyzed the core journals, articles, authors, institutions, relevant countries to determine the vital influence of digital twin-related literature. Secondly, we use theme maps to draw the research topics of digital twin-related literature as icons on the conceptual structure and offer four cluster parts (Motor themes, highly developed and isolated themes, Emerging themes, and Basic themes). Finally, we concluded by summarising the limitations of our study and subsequently highlighted future research directions. The study shows that monitoring, maintenance, and computer vision are research topics with good development momentum. Disturbance observer, reinforcement learning, and friction are relatively small research topics with a good development foundation, and scheduling, process modelling, and deep reinforcement learning are emerging research directions. [ABSTRACT FROM AUTHOR]

Virtual manufacturing (VM) technology emerged in the 1980s as a revolutionary strategy to optimize and streamline the entire product/service manufacturing lifecycle. However, over the years, its popularity appears to have waned. Further, the advent of the fourth industrial revolution (4IR) or Industry 4.0 brings with it other integrated digital technologies, including the Internet of Things (IoT), Blockchain, and digital twin (DT), among others. DT offers functions like VM plus other benefits, including intelligent manufacturing, to revolutionize future manufacturing operations activities and predictive capability using real-time data. This paper employs bibliographic metadata from publications indexed on SCOPUS to evaluate the recent trends in VM research and develop predictive models to forecast VM's future trajectory and relevance in 4IR. The results of the bibliometric evaluation of VM-related scientific literature publications show a rapidly declining research productivity and highlight an exponential decline from the mid-2000s. This period of VM publication decline coincides with the advent of 4IR and DT technology, which are trending. The results of the predictive analytics using the quadratic regression model created in this study to forecast the future relevance of VM in the 4IR era suggest that VM publications show a similar conclusion. VM research output increased until 2009 and then started decreasing exponentially. The quadratic model implies an exponential decrease in yearly VM publications. Future works can evaluate DT and VM research trends from the last two decades. [ABSTRACT FROM AUTHOR]

Digital twin is the digital representation of an entity, and it drives Industry 4.0. This paper presents a bibliometric analysis of digital twin in the supply chain to help researchers, industry practitioners, and academics to understand the trend, development, and focus of the areas of digital twin in the supply chain. This paper found several key clusters of research, including the designing of a digital twin model, integration of a digital twin model, application of digital twin in quality control, and digital twin in digitalization. In the embryonic stage of research, digital twin was tested in the production line with limited optimization. In the development stage, the importance of digital twin in Industry 4.0 was observed, as big data, machine learning, Industrial Internet of Things, blockchain, edge computing, and cloud-based systems complemented digital twin models. Digital twin was applied to improve sustainability in manufacturing and production logistics. In the current prosperity stage with high annual publications, the recent trends of this topic focus on the integration of deep learning, data models, and artificial intelligence for digitalization. This bibliometric analysis also found that the COVID-19 pandemic drove the start of the prosperity stage of digital twin research in the supply chain. Researchers in this field are slowly moving towards applying digital twin for human-centric systems and mass personalization to prepare to transit to Industry 5.0. [ABSTRACT FROM AUTHOR]

Construction faces many safety accidents with urbanization, particularly in hoisting. However, there is a lack of systematic review studies in this area. This paper explored the factors and methods of risk assessment in hoisting for industrial building system (IBS) construction. Firstly, bibliometric analysis revealed that future research will focus on "ergonomics", "machine learning", "computer simulation", and "wearable sensors". Secondly, the previous 80 factors contributing to hoisting risks were summarized from a "human–equipment–management–material–environment" perspective, which can serve as a reference point for managers. Finally, we discussed, in-depth, the application of artificial neural networks (ANNs) and digital twins (DT). ANNs have improved the efficiency and accuracy of risk assessment. Still, they require high-quality and significant data, which traditional methods do not provide, resulting in the low accuracy of risk simulation results. DT data are emerging as an alternative, enabling stakeholders to visualize and analyze the construction process. However, DT's interactivity, high cost, and information security need further improvement. Based on the discussion and analysis, the risk control model created in this paper guides the direction for future research. [ABSTRACT FROM AUTHOR]

Logistics and transport are major sources of energy consumption that still rely heavily on fossil fuels. Especially in the freight sector, finding means to optimise fuel consumption and energy efficiency are increasingly important. Digital twins' adaptation in logistics and transport is not as frequent as in production, but their implementation potential is immense. This technology can replicate real environments, allowing verification of various scenarios without real-life application, leading to optimal implementation outcome faster and more efficiently. This paper aims to research digital twins' use in logistics and transport, focusing on digital twins' potential and effects to optimise energy consumption. Firstly, previous research on digital twins in specified fields was identified, followed by a quantitative literature review. The latter focused on codifying the selected publications based on criteria such as modality, specific environment, energy consumption, etc. Furthermore, a qualitative overview of 57 relevant publications on digital twins' use in logistics and transport was made. The main contribution of this paper is the systematic overview of digital twins from the viewpoint of energy optimisation in one of the most energy-dependent sectors. This gives researchers a good starting point for further research and digital twins' practical implementation cases. [ABSTRACT FROM AUTHOR]

As deep underground digital twins have not yet been established worldwide, this study extracted keywords from national or city-led digital twin practices and elements of digital twins and through these keywords selected research papers and topics that could contribute to the establishment of deep underground digital twins in the future. We applied the concept of digital twins in geology and underground research to collect 1702 papers from the Web of Science and conducted semantic network analysis and topic modeling. The keywords digital, three dimensions, and real time were placed in the middle and have many links in the word network. Artificial intelligence, deep learning, and neural networks all showed a low degree of centrality. As a result of topic modeling using Latent Dirichlet allocation (LDA), topics related to topography, geological structure, and rock distribution, which are the basic data for building a deep underground digital twin, were noted, and topics related to earthquakes/vibrations, landslides, groundwater, and volcanoes were identified. Energy resources and space utilization have emerged as the main themes. [ABSTRACT FROM AUTHOR]

Facility Management (FM) regards the management of buildings' assets and requires the administration of a large amount of data that must always be available, but today, they still consist of paper documents, which are susceptible to loss. This is one of the principal causes for costs increase in the Operation and Maintenance (O&M) phase, together with the lack of interoperability and communication. In recent years, the construction industry has been undergoing a process of digitalization, supported from the advent of new technologies that hit the market at low prices. This study aims to indagate the technologies introduced in in the FM sector, in addition the major authors and sources that are studying them. A bibliometric analysis is conducted on 220 documents belonging to the Scopus DB. The documents were analyzed using bibliometric analysis tools, such as Biblioshiny and VOSviewer. The results show how FM and the implementation of technologies in its practices, such as Building information modelling (BIM), Internet of Things (IoT) and Digital Twin (DT), are fields with exponential growth in recent years. Other technologies, such as blockchain, artificial intelligence (AI), machine learning (ML) and augmented reality (AR), emerged as part of digitalization in FM, which is part of the transition towards Construction 4.0. [ABSTRACT FROM AUTHOR]

This study employs bibliometric methods utilizing VOSviewer analysis of Scopus data from 2013 to 2023 to investigate trends in artificial intelligence (AI) research within the aviation industry. The analysis reveals a substantial increase in publication volume over time, peaking at 406 articles in 2022, signifying a heightened interest in AI implementation within the aviation sector. Key publication sources notably include conferences such as AIAA IEEE Digital Avionics Systems Conference Proceedings and ACM International Conference Proceeding Series. Leading contributions in publications emerge from countries such as the United States, China, India, Germany, the United Kingdom, and France, reflecting global involvement in AI research within the aviation industry. Citation analysis identifies highly cited articles addressing topics such as Digital Twin (DT) optimization processes in aviation, AI application in aircraft navigation, and machine learning for weather forecasting. These findings underscore researchers' interest in fundamental topics such as aviation, aircraft-related artificial intelligence, flight delay, and deep learning. Furthermore, co-citation analysis delineates research clusters, illustrating thematic similarities within AI research in the aviation industry. Overall, this bibliometric analysis provides comprehensive insights into the evolution of AI research in the aviation industry, potentially guiding researchers, practitioners, and stakeholders in directing research efforts, formulating policies, and understanding current trends in the application of artificial intelligence within the aviation sector. [ABSTRACT FROM AUTHOR]

The digital twin has recently become a popular topic in research related to manufacturing, such as Industry 4.0, the industrial internet of things, and cyber-physical systems. In addition, digital twins are the focus of several research areas: construction, urban management, digital transformation of the economy, medicine, virtual reality, software testing, and others. The concept is not yet fully defined, its scope seems unlimited, and the topic is relatively new; all this can present a barrier to research. The main goal of this paper is to develop a proper methodology for visualizing the digital-twin science landscape using modern bibliometric tools, text-mining and topic-modeling, based on machine learning models—Latent Dirichlet Allocation (LDA) and BERTopic (Bidirectional Encoder Representations from Transformers). The scope of the study includes 8693 publications on the topic selected from the Scopus database, published between January 1993 and September 2022. Keyword co-occurrence analysis and topic-modeling indicate that studies on digital twins are still in the early stage of development. At the same time, the core of the topic is growing, and some topic clusters are emerging. More than 100 topics can be identified; the most popular and fastest-growing topic is 'digital twins of industrial robots, production lines and objects.' Further efforts are needed to verify the proposed methodology, which can be achieved by analyzing other research fields. [ABSTRACT FROM AUTHOR]

The industrial transformation led by digitization-related technologies has attracted research attention in recent decades, enhancing its application in different sectors. The transport industry is a crucial driving force for economic growth and social development. It is still necessary to make transportation infrastructure and services safer, cleaner, and more affordable to cope with increasing urbanization and mobility. This paper systematically examines the science mapping of building information modeling and digital twins technologies in the digitalization of transportation. Through the bibliometric and content analysis approaches, 493 related documents were screened and analyzed from the Web of Science and Scopus databases. The software programs VOSviewer and Bibliometrix were used to determine research trends and current gaps, which will be beneficial to future research in this vital field. The results showed that over 80% of the relevant documents have been published since 2018. China is the most productive country, followed by the United States and Italy, and Germany is the most cited and influential country. Moreover, research also revealed the leading authors, top journals, and highly cited papers. The findings may be used as a guide for: (1) improving the efficiency of intelligent transportation system element management; (2) the development and application of digital technologies; (3) the flow and goals of entire-life-cycle management; and (4) the optimization of related algorithms and models. [ABSTRACT FROM AUTHOR]

Purpose: Although organizations have more data than ever at their disposal, actually deriving meaningful insights and actions from them is easier said than done. In this concern, the main objective of this study is to identify trends and research opportunities regarding data management within new product development (NPD) and collaborative engineering. Design/methodology/approach: Bibliometric and systemic analyses have been carried out using the methodological procedure ProKnow-C, which provides a structured framework for the literature review. A bibliographic portfolio (BP) was consolidated with 33 papers that represent the state of art in the subject. Findings: Most recent researches within the BP indicate new trends and paradigm shifts in this area of research, tackling subjects such as the internet of things, cloud computing, big data analytics and digital twin. Research gaps include the lack of data automation and the absence of a common architecture for systems integration. However, from a general perspective of the BP, the management of experimental data is suggested as a research opportunity for future works. Although many studies have tackled data and collaboration based on computer-aided technologies environments, no study examined the management of the measured data collected during the verification and validation stages of a product. Originality/value: This work provides a fresh and relevant source of authors, journals and studies for researchers and practitioners interested in the domain of data management applied to NPD and collaborative engineering. [ABSTRACT FROM AUTHOR]

The Digital Twin (DT) concept in the manufacturing industry has received considerable attention from researchers because of its versatile application potential. Machine Learning (ML) adds a new dimension to DT by enhancing its functionality. Many studies on DT in the manufacturing industry have recently been published. However, there is still a lack of a systematic literature review on different aspects of ML-based DT in the manufacturing industry from a bibliometric and evolutionary perspective. Therefore, the proposed study is mainly aimed at reviewing DT in the manufacturing industry to identify the contribution of ML, current methods, and future research directions. According to the findings, the contribution of ML to this domain is significant. Additionally, the results show that the latest ML technologies are being used in the DT domain; neural networks have evolved based on application-specific requirements. The total number of papers and citations per paper on ML-based DT is increasing. The relevance of ML in DT has increased over time. The current trend is to use ML-based DT for data analytics. Additionally, there are many unfilled gaps; certain gaps include industrial applications of DT, synchronisation with real-time data through sensors, heterogeneous data management, and benchmarking. [ABSTRACT FROM AUTHOR]

Data-driven design (D 3), a new design paradigm benefited from advanced data analytics and computational intelligence, has gradually promoted the research of data-driven product design (DDPD) ever since 2000 s. In today's Intelligence Age, some theoretical and practical studies have tried to achieve the advanced intelligence capabilities in DDPD. However, to the authors' best knowledge, there is still a lack of a holistic summary of DDPD with chronological concern in the intelligence age. To bridge the gap, this research undertakes a literature review of DDPD publications from 2000 to date (19/09/2022), of which 172 relevant papers are discussed via bibliometric analysis and state-of-the-art analysis. The results shown that DDPD has vitality in the Intelligence Age by combining the cutting-edge digital technologies, such as AI, additive manufacturing, digital twin, and so on. Moreover, current DDPD studies could outperform the classical design methods on the well-defined tasks, but it still cannot master the creative/innovative design tasks which require the cognitive capability. This survey further highlights several future research potentials including cognitive intelligence-enabled design, end-to-end design integration, advanced design knowledge support, design for additive manufacturing, and sustainable smart product-service systems. It is hoped that this work can be regarded as a reference to understand the roadmap of DDPD and offer insights for the design practitioners to complete relevant tasks in today's intelligence age. [ABSTRACT FROM AUTHOR]