Abstract
In recent years, the architecture, engineering, construction, and facility management (FM) industries have been applying various emerging digital technologies to facilitate the design, construction, and management of infrastructure facilities. Digital twin (DT) has emerged as a solution for enabling real-time data acquisition, transfer, analysis, and utilization for improved decision-making toward smart FM. Substantial research on DT for FM has been undertaken in the past decade. This paper presents a bibliometric analysis of the literature on DT for FM. A total of 248 research articles are obtained from the Scopus and Web of Science databases. VOSviewer is then utilized to conduct bibliometric analysis and visualize keyword co-occurrence, citation, and co-authorship networks; furthermore, the research topics, authors, sources, and countries contributing to the use of DT for FM are identified. The findings show that the current research of DT in FM focuses on building information modeling-based FM, artificial intelligence (AI)-based predictive maintenance, real-time cyber–physical system data integration, and facility lifecycle asset management. Several areas, such as AI-based real-time asset prognostics and health management, virtual-based intelligent infrastructure monitoring, deep learning-aided continuous improvement of the FM systems, semantically rich data interoperability throughout the facility lifecycle, and autonomous control feedback, need to be further studied. This review contributes to the body of knowledge on digital transformation and smart FM by identifying the landscape, state-of-the-art research trends, and future needs with regard to DT in FM.
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References
Agnusdei G P, Elia V, Gnoni M G (2021). Is digital twin technology supporting safety management? A bibliometric and systematic review. Applied Sciences, 11(6): 2767
Al-Kasasbeh M, Abudayyeh O, Liu H (2020). A unified work breakdown structure-based framework for building asset management. Journal of Facilities Management, 18(4): 437–450
Al-Kasasbeh M, Abudayyeh O, Liu H (2021). An integrated decision support system for building asset management based on BIM and Work Breakdown Structure. Journal of Building Engineering, 34: 101959
Ali A S, Kamaruzzaman S N, Sulaiman R, Cheong Peng Y (2010). Factors affecting housing maintenance cost in Malaysia. Journal of Facilities Management, 8(4): 285–298
Almatared M, Liu H, Tang S, Sulaiman M, Lei Z, Li H X (2022). Digital twin in the architecture, engineering, and construction industry: A bibliometric review. In: Construction Research Congress. Arlington, VA: ASCE, 670–678
Angjeliu G, Coronelli D, Cardani G (2020). Development of the simulation model for Digital Twin applications in historical masonry buildings: The integration between numerical and experimental reality. Computers & Structures, 238: 106282
Baracho R M A, Soergel D, Pereira Jr M L, Henriques M A (2019). A proposal for developing a comprehensive ontology for smart cities/smart buildings/smart life. In: Proceedings of the 10th International Multi-Conference on Complexity, Informatics and Cybernetics. Orlando, FL: Curran Associates, Inc., 12–15
Burnham J F (2006). Scopus database: A review. Biomedical Digital Libraries, 3(1): 1–8
Cardoso D, Ferreira L (2021). Application of predictive maintenance concepts using artificial intelligence tools. Applied Sciences, 11(1): 1–18
Chen C, Zhao Z, Xiao J, Tiong R (2021). A conceptual framework for estimating building embodied carbon based on digital twin technology and life cycle assessment. Sustainability, 13(24): 13875
Congress S S C, Puppala A J (2021). tDigital twinning approach for transportation infrastructure asset management using UAV data. In: International Conference on Transportation and Development. ASCE, 321–331
Coupry C, Noblecourt S, Richard P, Baudry D, Bigaud D (2021). BIM-based digital twin and XR devices to improve maintenance procedures in smart buildings: A literature review. Applied Sciences, 11(15): 6810
Daniotti B, Masera G, Bolognesi C M, Lupica Spagnolo S, Pavan A, Iannaccone G, Signorini M, Ciuffreda S, Mirarchi C, Lucky M, Cucuzza M (2022). The development of a BIM-based interoperable toolkit for efficient renovation in buildings: From BIM to digital twin. Buildings, 12(2): 231
Deng M, Menassa C C, Kamat V R (2021). From BIM to digital twins: A systematic review of the evolution of intelligent building representations in the AEC-FM industry. Journal of Information Technology in Construction, 26: 58–83
Di Ciaccio D, Maroder E, Ambrosio S, Paccaniccio F (2021). BIM and cloud platforms for facility management of Roman temple “Hadrianeum”: Chamber of commerce in Rome, digitalization as a solution for historical heritage management. WIT Transactions on the Built Environment, 205: 187–192
Felsberger L, Todd B, Kranzlmüller D (2019). Power converter maintenance optimization using a model-based digital reliability twin paradigm. In: Proceedings of the 4th International Conference on System Reliability and Safety (ICSRS). Rome: IEEE, 213–217
Florian E, Sgarbossa F, Zennaro I (2021). Machine learning-based predictive maintenance: A cost-oriented model for implementation. International Journal of Production Economics, 236: 108114
Jain P, Poon J, Singh J P, Spanos C, Sanders S R, Panda S K (2020). A digital twin approach for fault diagnosis in distributed photovoltaic systems. IEEE Transactions on Power Electronics, 35(1): 940–956
Kaewunruen S, Xu N (2018). Digital twin for sustainability evaluation of railway station buildings. Frontiers in Built Environment, 4: 77
Kang J S, Chung K, Hong E J (2021). Multimedia knowledge-based bridge health monitoring using digital twin. Multimedia Tools and Applications, 80(26–27): 34609–34624
Katona A, Panfilov P (2018). Building predictive maintenance framework for smart environment application systems. In: Proceedings of the 29th DAAAM International Symposium on Intelligent Manufacturing and Automation. Vienna, 460–470
Khajavi S H, Motlagh N H, Jaribion A, Werner L C, Holmstrom J (2019). Digital twin: Vision, benefits, boundaries, and creation for buildings. IEEE Access, 7: 147406–147419
Khujamuratov B, Takhirova G, Khudaybergenov T (2022). Smart City: Sensor infrastructure monitoring system. Harvard Educational and Scientific Review, 2(1): 114–120
Klein P, Bergmann R (2019). Generation of complex data for AI-based predictive maintenance research with a physical factory model. In: Proceedings of the 16th International Conference on Informatics in Control, Automation and Robotics. Prague: SciTePress, 40–50
Liu H, Abudayyeh O, Liou W (2020). BIM-based smart facility management: A review of present research status, challenges, and future needs. In: Construction Research Congress, Computer Applications. Tempe, AZ: ASCE, 1087–1095
Liu L, Li B, Zlatanova S, van Oosterom P (2021). Indoor navigation supported by the Industry Foundation Classes (IFC): A survey. Automation in Construction, 121: 103436
Liu Z, Yuan C, Sun Z, Cao C (2022). Digital twins-based impact response prediction of prestressed steel structure. Sensors, 22(4): 1647
Love P E D, Matthews J (2019). The “how” of benefits management for digital technology: From engineering to asset management. Automation in Construction, 107: 102930
Lu Q, Parlikad A K, Woodall P, Don Ranasinghe G, Xie X, Liang Z, Konstantinou E, Heaton J, Schooling J M (2020a). Developing a digital twin at building and city levels: Case study of West Cambridge campus. Journal of Management in Engineering, 36(3): 05020004
Lu Q, Xie X, Parlikad A K, Schooling J M (2020b). Digital twin-enabled anomaly detection for built asset monitoring in operation and maintenance. Automation in Construction, 118: 103277
Lu R, Brilakis I (2019). Digital twinning of existing reinforced concrete bridges from labelled point clusters. Automation in Construction, 105: 102837
Macchi M, Roda I, Negri E, Fumagalli L (2018). Exploring the role of digital twin for asset lifecycle management. IFAC-PapersOnLine, 51(11): 790–795
Macchiarulo V, Milillo P, Blenkinsopp C, Reale C, Giardina G (2022). Multi-temporal InSAR for transport infrastructure monitoring: Recent trends and challenges. Proceedings of the Institution of Civil Engineers: Bridge Engineering, in press, doi:https://doi.org/10.1680/jbren.21.00039
Moiceanu G, Paraschiv G (2022). Digital twin and smart manufacturing in industries: A bibliometric analysis with a focus on Industry 4.0. Sensors, 22(4): 1388
Mongeon P, Paul-Hus A (2016). The journal coverage of Web of Science and Scopus: A comparative analysis. Scientometrics, 106(1): 213–228
Neto A A, Carrijo B S, Brock J G R, Deschamps F, de Lima E P (2021). Digital twin-driven decision support system for opportunistic preventive maintenance scheduling in manufacturing. Procedia Manufacturing, 55: 439–446
Nguyen T N, Ponciroli R, Bruck P, Esselman T C, Rigatti J A, Vilim R B (2022). A digital twin approach to system-level fault detection and diagnosis for improved equipment health monitoring. Annals of Nuclear Energy, 170: 109002
Opoku D G J, Perera S, Osei-Kyei R, Rashidi M (2021). Digital twin application in the construction industry: A literature review. Journal of Building Engineering, 40: 102726
Ozturk G B (2021). Digital twin research in the AECO-FM industry. Journal of Building Engineering, 40: 102730
Parlina A, Ramli K, Murfi H (2020). Theme mapping and bibliometrics analysis of one decade of big data research in the Scopus database. Information, 11(2): 69
Pishdad-Bozorgi P (2017). Future smart facilities: State-of-the-art BIM-enabled facility management. Journal of Construction Engineering and Management, 143(9): 02517006
Pranckutė R (2021). Web of Science (WoS) and Scopus: The titans of bibliographic information in today’s academic world. Publications, 9(1): 12
Rathore M M, Shah S A, Shukla D, Bentafat E, Bakiras S (2021). The role of AI, machine learning, and big data in digital twinning: A systematic literature review, challenges, and opportunities. IEEE Access, 9: 32030–32052
Samatas G G, Moumgiakmas S S, Papakostas G A (2021). Predictive maintenance: Bridging artificial intelligence and IoT. In: IEEE World AI IoT Congress (AIIoT). Seattle, WA: IEEE, 413–419
Schiavi B, Havard V, Beddiar K, Baudry D (2022). BIM data flow architecture with AR/VR technologies: Use cases in architecture, engineering and construction. Automation in Construction, 134: 104054
Shim C S, Dang N S, Lon S, Jeon C H (2019). Development of a bridge maintenance system for prestressed concrete bridges using 3D digital twin model. Structure and Infrastructure Engineering, 15(10): 1319–1332
Sivalingam K, Sepulveda M, Spring M, Davies P (2018). A review and methodology development for remaining useful life prediction of offshore fixed and floating wind turbine power converter with digital twin technology perspective. In: 2nd International Conference on Green Energy and Applications (ICGEA). Singapore: IEEE, 197–204
Tosti F, Gagliardi V, Ciampoli L B, Benedetto A, Threader S, Alani A M (2021). Integration of remote sensing and ground-based non-destructive methods in transport infrastructure monitoring: Advances, challenges and perspectives. In: IEEE Asia-Pacific Conference on Geoscience, Electronics and Remote Sensing Technology (AGERS). Jakarta Pusat: IEEE, 1–7
Toth J C, Marmillo J, Biedenbach G (2019). Development of a digital twin for the determination of transmission line conductor asset health. In: Proceedings of the 21st International Symposium on High Voltage Engineering. Budapest: Springer, 917–925
van Eck J N, Waltman L (2022). VOSviewer Manual Villa V, Naticchia B, Bruno G, Aliev K, Piantanida P, Antonelli D (2021). IoT open-source architecture for the maintenance of building facilities. Applied Sciences, 11(12): 5374
Wagg D J, Worden K, Barthorpe R J, Gardner P (2020). Digital twins: State-of-the-art and future directions for modeling and simulation in engineering dynamics applications. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering, 6(3): 030901
Waltman L, van Eck N J, Noyons E C (2010). A unified approach to mapping and clustering of bibliometric networks. Journal of Informetrics, 4(4): 629–635
Wang Y, Cao Y, Wang F Y (2021). Anomaly detection in digital twin model. In: IEEE 1st International Conference on Digital Twins and Parallel Intelligence (DTPI). Beijing: IEEE, 208–211
Wong J K W, Ge J, He S X (2018). Digitisation in facilities management: A literature review and future research directions. Automation in Construction, 92: 312–326
Xie X, Lu Q, Parlikad A K, Schooling J M (2020). Digital twin enabled asset anomaly detection for building facility management. IFAC-PapersOnLine, 53(3): 380–385
Ye C, Butler L, Calka B, Iangurazov M, Lu Q, Gregory A, Girolami M, Middleton C (2019). A digital twin of bridges for structural health monitoring. In: Proceedings of the 12th International Workshop on Structural Health Monitoring. Stanford, CA: DEStech Publications, Inc., 1619–1626
Ye S, Lai X, Bartoli I, Aktan A E (2020). Technology for condition and performance evaluation of highway bridges. Journal of Civil Structural Health Monitoring, 10(4): 573–594
Zeng X, Yang M, Yang X, Bo Y, Feng C, Zhou Y (2020). Anomaly detection of wind turbine gearbox based on digital twin drive. In: IEEE 3rd Student Conference on Electrical Machines and Systems (SCEMS). Jinan: IEEE, 184–188
Zhu Y, Li N (2021). Virtual and augmented reality technologies for emergency management in the built environments: A state-of-the-art review. Journal of Safety Science and Resilience, 2(1): 1–10
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Hakimi, O., Liu, H. & Abudayyeh, O. Digital twin-enabled smart facility management: A bibliometric review. Front. Eng. Manag. 11, 32–49 (2024). https://doi.org/10.1007/s42524-023-0254-4
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DOI: https://doi.org/10.1007/s42524-023-0254-4