Skip to main content
Log in

Concept, Creation, Services and Future Directions of Digital Twins in the Construction Industry: A Systematic Literature Review

  • Review article
  • Published:
Archives of Computational Methods in Engineering Aims and scope Submit manuscript

Abstract

Currently, the engineering problems encountered in digital transformation of the construction industry are very complicated and need to be solved by integrating multiple technologies. Consequently, the concept of digital twin (DT) was introduced and quickly applied throughout the building lifecycle. Despite this, many practitioners lack understanding of DT in the construction industry (DT-CI) and its implementation. To overcome this issue, this paper presents a comprehensive and detailed review of DT-CI through a systematic literature review (SLR) that incorporates both quantitative and qualitative analysis. In this study, 222 DT-CI studies were selected from a pool of 2619 publications across multiple databases, and 43 related researches were supplemented by the backward snowballing method based on co-citation analysis to generate the final bibliographic database. This paper quantitatively analyzes the current state, hotspots, and development trends of DT-CI research through a bibliometric review, and systematically clarifies the concept, creation, services, and future directions of DT-CI through a framework-based review. Finally, based on the SLR outcomes, this paper offers recommendations for future work and DT-CI implementation. Contrary to other reviews within this field, this paper adheres to a rigorous SLR protocol to ensure the reproducibility of review results. Moreover, by comparing construction and non-construction DT concepts, we highlight the unique characteristics of DT-CI, namely its association with building information modeling (BIM) and emphasis on geometric reconstruction of building entities.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

Data Availability

Data will be made available on request.

References

  1. Graham R, Jeremy L, Toby W (2021) Future of Construction - A Global Forecast for the Construction Industry to 2030. https://www.oxfordeconomics.com/resource/future-of-construction/. Accessed 24 Jul 2023

  2. Song Y, Koeck R, Luo S (2021) Review and analysis of augmented reality (AR) literature for digital fabrication in architecture. Autom Constr 128. https://doi.org/10.1016/j.autcon.2021.103762

  3. Ali KN, Alhajlah HH, Kassem MA (2022) Collaboration and risk in Building Information Modelling (BIM): a systematic literature review. Buildings 12. https://doi.org/10.3390/buildings12050571

  4. Zhao Y, Taib N (2022) Cloud-based Building Information Modelling (Cloud-BIM): systematic literature review and bibliometric-qualitative analysis. Autom Constr 142. https://doi.org/10.1016/j.autcon.2022.104468

  5. Boschert S, Rosen R (2016) Digital twin-the simulation aspect. In: Mechatronic Futures: Challenges and Solutions for Mechatronic Systems and Their Designers. pp 59–74

  6. Grieves M (2014) Digital Twin: Manufacturing Excellence through Virtual Factory Replication. https://www.researchgate.net/publication/275211047_Digital_Twin_Manufacturing_Excellence_through_Virtual_Factory_Replication. Accessed 24 Jul 2023

  7. Shafto M, Conroy M, Doyle R, Glaessgen E et al (2010) DRAFT modeling, simulation, information technology & processing roadmap - technology area 11. National Aeronautics and Space Administration. https://www.nasa.gov/pdf/501321main_TA11-MSITP-DRAFT-Nov2010-A1.pdf. Accessed 24 Jul 2023

  8. Liu M, Fang S, Dong H, Xu C (2021) Review of digital twin about concepts, technologies, and industrial applications. J Manuf Syst 58:346–361. https://doi.org/10.1016/j.jmsy.2020.06.017

    Article  Google Scholar 

  9. Jiang F, Ma L, Broyd T, Chen K (2021) Digital twin and its implementations in the civil engineering sector. Autom Constr 130. https://doi.org/10.1016/j.autcon.2021.103838

  10. MarketsandMarkets (2022) Digital twin market by enterprise: Application, industry, and geography-global forecast to 2027. https://www.marketsandmarkets.com/Market-Reports/digital-twin-market-225269522.html. Accessed 24 Jul 2023

  11. Boje C, Guerriero A, Kubicki S, Rezgui Y (2020) Towards a semantic construction Digital Twin: directions for future research. Autom Constr 114. https://doi.org/10.1016/j.autcon.2020.103179

  12. Opoku D-GJ, Perera S, Osei-Kyei R, Rashidi M (2021) Digital twin application in the construction industry: a literature review. J Build Eng 40. https://doi.org/10.1016/j.jobe.2021.102726

  13. Opoku D-GJ, Perera S, Osei-Kyei R et al (2022) Drivers for Digital Twin Adoption in the Construction Industry: a systematic literature review. Buildings 12: https://doi.org/10.3390/buildings12020113

  14. Davila Delgado JM, Oyedele L (2021) Digital Twins for the built environment: learning from conceptual and process models in manufacturing. Adv Eng Inf 49. https://doi.org/10.1016/j.aei.2021.101332

  15. Hosamo HH, Nielsen HK, Alnmr AN et al (2022) A review of the Digital Twin technology for fault detection in buildings. Front Built Environ 8. https://doi.org/10.3389/fbuil.2022.1013196

  16. Hou L, Wu S, Zhang GK et al (2021) Literature Review of Digital Twins Applications in Construction Workforce Safety. Appl Sci-BASEL 11:1–21. https://doi.org/10.3390/app11010339

    Article  Google Scholar 

  17. Zhang H, Zhou Y, Zhu H et al (2021) Digital twin-driven intelligent construction: features and trends. SDHM Struct Durab Health Monit 15:183–206. https://doi.org/10.32604/SDHM.2021.018247

    Article  Google Scholar 

  18. Kitchenham B, Pearl Brereton O, Budgen D et al (2009) Systematic literature reviews in software engineering - A systematic literature review. Inf Softw Technol 51:7–15. https://doi.org/10.1016/j.infsof.2008.09.009

    Article  Google Scholar 

  19. Papaioannou D (2016) Systematic Approaches to a Successful Literature Review. 1–336

  20. Paul J, Lim WM, O’Cass A et al (2021) Scientific procedures and rationales for systematic literature reviews (SPAR-4-SLR). Int J Consum Stud. https://doi.org/10.1111/ijcs.12695

    Article  Google Scholar 

  21. Naghshbandi SN, Varga L, Hu Y (2021) Technologies for safe and resilient earthmoving operations: a systematic literature review. Autom Constr. 125. https://doi.org/10.1016/j.autcon.2021.103632

    Article  Google Scholar 

  22. Wohlin C (2014) Guidelines for snowballing in systematic literature studies and a replication in software engineering

  23. Bardou P, Mariette J, Escudié F et al (2014) Jvenn: an interactive Venn diagram viewer. BMC Bioinformatics 15. https://doi.org/10.1186/1471-2105-15-293

  24. Palmatier RW, Houston MB, Hulland J (2018) Review articles: purpose, process, and structure. J Acad Mark Sci 46. https://doi.org/10.1007/s11747-017-0563-4

  25. Pan Y, Zhang L (2023) Integrating BIM and AI for Smart Construction Management: current status and future directions. Arch Comput Methods Eng 30:1081–1110. https://doi.org/10.1007/s11831-022-09830-8

    Article  Google Scholar 

  26. van Eck NJ, Waltman L (2010) Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics 84:523–538. https://doi.org/10.1007/s11192-009-0146-3

    Article  Google Scholar 

  27. Paul J, Benito GRG (2018) A review of research on outward foreign direct investment from emerging countries, including China: what do we know, how do we know and where should we be heading? Asia Pac Bus Rev 24:90–115. https://doi.org/10.1080/13602381.2017.1357316

    Article  Google Scholar 

  28. Paul J, Parthasarathy S, Gupta P (2017) Exporting challenges of SMEs: a review and future research agenda. J World Bus 52:327–342. https://doi.org/10.1016/j.jwb.2017.01.003

    Article  Google Scholar 

  29. Paul J, Rosado-Serrano A (2019) Gradual internationalization vs Born-Global/International new venture models: a review and research agenda. Int Mark Rev 36:830–858. https://doi.org/10.1108/IMR-10-2018-0280

    Article  Google Scholar 

  30. Callahan JL (2014) Writing literature reviews: a reprise and update. Hum Resour Dev Rev 13:271–275. https://doi.org/10.1177/1534484314536705

    Article  Google Scholar 

  31. Lim WM (2020) Challenger marketing. Ind Mark Manag 84:342–345. https://doi.org/10.1016/j.indmarman.2019.08.009

    Article  Google Scholar 

  32. Ozturk GB (2020) Interoperability in building information modeling for AECO/FM industry. Autom Constr 113. https://doi.org/10.1016/j.autcon.2020.103122

  33. Negri E, Fumagalli L, Macchi M (2017) A review of the roles of Digital Twin in CPS-based Production systems. pp 939–948

  34. Pan Y, Zhang L (2021) A BIM-data mining integrated digital twin framework for advanced project management. Autom Constr 124. https://doi.org/10.1016/j.autcon.2021.103564

  35. Lu Q, Parlikad A, Woodall P et al (2020) Developing a Digital Twin at Building and City levels: Case Study of West Cambridge Campus. J Manag Eng 36. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000763

  36. Lu Q, Xie X, Parlikad AK, Schooling JM (2020) Digital twin-enabled anomaly detection for built asset monitoring in operation and maintenance. Autom Constr 118. https://doi.org/10.1016/j.autcon.2020.103277

  37. 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. Comput Struct 238. https://doi.org/10.1016/j.compstruc.2020.106282

  38. Tao F, Cheng J, Qi Q et al (2018) Digital twin-driven product design, manufacturing and service with big data. Int J Adv Manuf Technol 94:3563–3576. https://doi.org/10.1007/s00170-017-0233-1

    Article  Google Scholar 

  39. Alam K, El Saddik A (2017) C2PS: a Digital Twin Architecture Reference Model for the cloud-based Cyber-physical systems. IEEE ACCESS 5:2050–2062. https://doi.org/10.1109/ACCESS.2017.2657006

    Article  Google Scholar 

  40. Khajavi SH, Motlagh NH, Jaribion A et al (2019) Digital Twin: Vision, benefits, boundaries, and creation for buildings. IEEE Access 7:147406–147419. https://doi.org/10.1109/ACCESS.2019.2946515

    Article  Google Scholar 

  41. Kaewunruen S, Rungskunroch P, Welsh J (2019) A Digital-Twin evaluation of net Zero Energy Building for existing buildings. SUSTAINABILITY 11. https://doi.org/10.3390/su11010159

  42. Bosche F, Ahmed M, Turkan Y et al (2015) The value of integrating scan-to-BIM and scan-vs-BIM techniques for construction monitoring using laser scanning and BIM: the case of cylindrical MEP components. Autom Constr 49:201–213. https://doi.org/10.1016/j.autcon.2014.05.014

    Article  Google Scholar 

  43. Boje C, Hahn Menacho ÁJ, Marvuglia A et al (2023) A framework using BIM and digital twins in facilitating LCSA for buildings. J Build Eng 76:107232. https://doi.org/10.1016/j.jobe.2023.107232

    Article  Google Scholar 

  44. Moretti N, Xie X, Merino Garcia J et al (2023) Federated Data Modeling for Built Environment Digital Twins. J Comput Civ Eng 37:04023013. https://doi.org/10.1061/JCCEE5.CPENG-4859

    Article  Google Scholar 

  45. Phoong SW, Phoong SY, Khek SL (2022) Systematic Literature Review With Bibliometric Analysis on Markov Switching Model: Methods and Applications. SAGE Open 12:. https://doi.org/10.1177/21582440221093062

  46. Kaewunruen S, Lian Q (2019) Digital twin aided sustainability-based lifecycle management for railway turnout systems. J Clean Prod 228:1537–1551. https://doi.org/10.1016/j.jclepro.2019.04.156

    Article  Google Scholar 

  47. Bortolini R, Rodrigues R, Alavi H et al (2022) Digital Twins’ applications for Building Energy Efficiency: a review. Energies 15: https://doi.org/10.3390/en15197002

  48. Su S, Zhong RY, Jiang Y et al (2023) Digital twin and its potential applications in construction industry: state-of-art review and a conceptual framework. Adv Eng Inf 57. https://doi.org/10.1016/j.aei.2023.102030

  49. Patterson EA, Taylor RJ, Bankhead M (2016) A framework for an integrated nuclear digital environment. Prog Nucl Energy 87:97–103. https://doi.org/10.1016/j.pnucene.2015.11.009

    Article  Google Scholar 

  50. Yoon S (2022) Virtual sensing in intelligent buildings and digitalization. Autom Constr 143. https://doi.org/10.1016/j.autcon.2022.104578

  51. Pan Y, Braun A, Brilakis I, Borrmann A (2022) Enriching geometric digital twins of buildings with small objects by fusing laser scanning and AI-based image recognition. Autom Constr 140. https://doi.org/10.1016/j.autcon.2022.104375

  52. Bolton A, Enzer M, Schooling J (2018) The Gemini principles: guiding values for the National Digital Twin and Information Management Framework. https://doi.org/10.17863/CAM.32260

  53. Brilakis I, Pan Y, Borrmann A et al (2020) Built Environment Digital Twinning, 2020. https://mediatum.ub.tum.de/1553893. Accessed 24 Jul 2023

  54. RIBA (2020) RIBA: Plan of Work 2020 Overview; Royal Institute of British Architects: London, UK, https://www.architecture.com/-/media/GatherContent/Test-resources-page/Additional-Documents/2020RIBAPlanofWorkoverviewpdf.pdf. Accessed 24 Jul 2023

  55. Gao X, Pishdad-Bozorgi P, Shelden DR, Tang S (2021) Internet of things enabled Data Acquisition Framework for Smart Building Applications. J Constr Eng Manag 147. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001983

  56. Alanne K, Sierla S (2022) An overview of machine learning applications for smart buildings. Sustain Cities Soc 76. https://doi.org/10.1016/j.scs.2021.103445

  57. Youn H-C, Yoon J-S, Ryoo S-L (2021) HBIM for the characteristics of Korean traditional wooden architecture: Bracket set modelling based on 3D scanning. Buildings 11. https://doi.org/10.3390/buildings11110506

  58. Wang W, Guo H, Li X et al (2022) Deep learning for assessment of environmental satisfaction using BIM big data in energy efficient building digital twins. Sustain Energy Technol Assess 50. https://doi.org/10.1016/j.seta.2021.101897

  59. 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. https://doi.org/10.3390/su132413875

  60. Pantoja-Rosero BG, Achanta R, Kozinski M et al (2022) Generating LOD3 building models from structure-from-motion and semantic segmentation. Autom Constr 141. https://doi.org/10.1016/j.autcon.2022.104430

  61. Koltsios S, Fokaides P, Georgali P-Z et al (2022) An enhanced framework for next-generation operational buildings energy performance certificates. Int J Energy Res 46:20079–20095. https://doi.org/10.1002/er.8517

    Article  Google Scholar 

  62. Jiang L, Shi J, Wang C, Pan Z (2023) Intelligent control of building fire protection system using digital twins and semantic web technologies. Autom Constr 147. https://doi.org/10.1016/j.autcon.2022.104728

  63. Lydon GP, Caranovic S, Hischier I, Schlueter A (2019) Coupled simulation of thermally active building systems to support a digital twin. Energy Build 202. https://doi.org/10.1016/j.enbuild.2019.07.015

  64. Agapaki E, Brilakis I (2021) CLOI: an Automated Benchmark Framework for Generating Geometric Digital Twins of Industrial Facilities. J Constr Eng Manag 147:04021145. https://doi.org/10.1061/(ASCE)CO.1943-7862.0002171

    Article  Google Scholar 

  65. Hosamo HH, Svennevig PR, Svidt K et al (2022) A Digital Twin predictive maintenance framework of air handling units based on automatic fault detection and diagnostics. Energy Build 261. https://doi.org/10.1016/j.enbuild.2022.111988

  66. Zhao J, Feng H, Chen Q, Garcia de Soto B (2022) Developing a conceptual framework for the application of digital twin technologies to revamp building operation and maintenance processes. J Build Eng 49. https://doi.org/10.1016/j.jobe.2022.104028

  67. Teisserenc B, Sepasgozar S (2021) Adoption of blockchain technology through digital twins in the construction industry 4.0: a PESTELS approach. Buildings 11. https://doi.org/10.3390/buildings11120670

  68. Mêda P, Calvetti D, Hjelseth E, Sousa H (2021) Incremental digital twin conceptualisations targeting data-driven circular construction. Buildings 11. https://doi.org/10.3390/buildings11110554

  69. Villa V, Naticchia B, Bruno G et al (2021) Iot open-source architecture for the maintenance of building facilities. Appl Sci-BASEL 11. https://doi.org/10.3390/app11125374

  70. Zhao L, Zhang H, Wang Q et al (2022) Digital Twin Evaluation of Environment and Health of Public Toilet Ventilation Design Based on building information modeling. Buildings 12. https://doi.org/10.3390/buildings12040470

  71. Luo J, Liu P, Cao L (2022) Coupling a physical replica with a Digital Twin: a comparison of participatory decision-making methods in an Urban Park Environment. ISPRS Int J GEO-Inf 11. https://doi.org/10.3390/ijgi11080452

  72. Tan Y, Chen P, Shou W, Sadick A-M (2022) Digital Twin-driven approach to improving energy efficiency of indoor lighting based on computer vision and dynamic BIM. Energy Build 270. https://doi.org/10.1016/j.enbuild.2022.112271

  73. Jiang W, Ding L, Zhou C (2022) Digital twin: Stability analysis for tower crane hoisting safety with a scale model. Autom Constr 138. https://doi.org/10.1016/j.autcon.2022.104257

  74. Zhang J, Kwok HHL, Luo H et al (2022) Automatic relative humidity optimization in underground heritage sites through ventilation system based on digital twins. Build Environ 216. https://doi.org/10.1016/j.buildenv.2022.108999

  75. Kang K, Besklubova S, Dai Y, Zhong RY (2022) Building demolition waste management through smart BIM: a case study in Hong Kong. Waste Manag 143:69–83. https://doi.org/10.1016/j.wasman.2022.02.027

    Article  Google Scholar 

  76. Zhang C, Sun Q, Sun W et al (2021) A construction method of digital twin model for contact characteristics of assembly interface. Int J Adv Manuf Technol 113:2685–2699. https://doi.org/10.1007/s00170-021-06751-x

    Article  Google Scholar 

  77. Khan AA, Khan MA, Leung K et al (2022) A review of critical fire event library for buildings and safety framework for smart firefighting. Int J Disaster Risk Reduct 83. https://doi.org/10.1016/j.ijdrr.2022.103412

  78. Seo H, Yun W-S (2022) Digital Twin-Based Assessment Framework for Energy Savings in University Classroom Lighting. Buildings 12. https://doi.org/10.3390/buildings12050544

  79. Chiachío M, Megía M, Chiachío J et al (2022) Structural digital twin framework: Formulation and technology integration. Autom Constr 140. https://doi.org/10.1016/j.autcon.2022.104333

  80. Ni Z, Liu Y, Karlsson M, Gong S (2022) Enabling Preventive Conservation of historic buildings through cloud-based Digital Twins: a Case Study in the City Theatre, Norrköping. IEEE Access 10:90924–90939. https://doi.org/10.1109/ACCESS.2022.3202181

    Article  Google Scholar 

  81. Zhao Y, Wang N, Liu Z, Mu E (2022) Construction theory for a Building Intelligent operation and maintenance system based on Digital Twins and Machine Learning. Buildings 12. https://doi.org/10.3390/buildings12020087

  82. Xie X, Lu Q, Rodenas-Herraiz D et al (2020) Visualised inspection system for monitoring environmental anomalies during daily operation and maintenance. Eng Constr Archit Manag 27:1835–1852. https://doi.org/10.1108/ECAM-11-2019-0640

    Article  Google Scholar 

  83. Zhu H, Wang Y (2022) Intelligent analysis for safety-influencing factors of prestressed steel structures based on digital twins and random forest. METALS 12. https://doi.org/10.3390/met12040646

  84. Dang H, Tatipamula M, Nguyen HX (2022) Cloud-based Digital Twinning for Structural Health Monitoring using deep learning. IEEE Trans Ind Inf 18:3820–3830. https://doi.org/10.1109/TII.2021.3115119

    Article  Google Scholar 

  85. Wang W, Guo H, Li X et al (2022) BIM Information integration based VR modeling in digital twins in industry 5.0. J Ind Inf Integr 28. https://doi.org/10.1016/j.jii.2022.100351

  86. Liu Z, Meng X, Xing Z, Jiang A (2021) Digital twin-based safety risk coupling of prefabricated building hoisting. Sensors 21. https://doi.org/10.3390/s21113583

  87. Lee D, Lee S (2021) Digital twin for supply chain coordination in modular construction. Appl Sci-BASEL 11. https://doi.org/10.3390/app11135909

  88. Liu Z, Zhang A, Wang W (2020) A framework for an indoor safety management system based on digital twin. SENSORS 20:1–20. https://doi.org/10.3390/s20205771

    Article  Google Scholar 

  89. Cruz Franco PA, Rueda Márquez, de la Plata A, Gómez Bernal E (2022) Protocols for the Graphic and Constructive Diffusion of Digital Twins of the Architectural Heritage That Guarantee Universal Accessibility through AR and VR. Appl Sci-BASEL 12:. https://doi.org/10.3390/app12178785

  90. Talmaki SA, Kamat VR (2022) Sensor Acquisition and Allocation for Real-Time Monitoring of Articulated Construction Equipment in Digital Twins. Sensors 22. https://doi.org/10.3390/s22197635

  91. Zhu H, Wang Y (2022) Key Component capture and Safety Intelligent Analysis of Beam String structure based on Digital Twins. SYMMETRY-BASEL 14.https://doi.org/10.3390/sym14061152

  92. Liu Z, Shi G, Jiang A, Li W (2021) Intelligent discrimination Method based on Digital Twins for analyzing sensitivity of mechanical parameters of Prestressed Cables. Appl Sci-BASEL 11. https://doi.org/10.3390/app11041485

  93. Zhang T, Wang Z, Zeng Y et al (2022) Building Artificial-Intelligence Digital Fire (AID-Fire) system: a real-scale demonstration. J Build Eng 62. https://doi.org/10.1016/j.jobe.2022.105363

  94. Greif T, Stein N, Flath CM (2020) Peeking into the void: Digital twins for construction site logistics. Comput Ind 121. https://doi.org/10.1016/j.compind.2020.103264

  95. Cheok EWW, Qian X, Chen C et al (2024) A local digital twin approach for identifying, locating and sizing cracks in CHS X-joints subjected to brace axial loading. Eng Struct 299. https://doi.org/10.1016/j.engstruct.2023.117085

  96. Zhang K, Chen H, Dai H-N et al (2022) SpoVis: decision support system for Site Selection of Sports Facilities in Digital Twinning cities. IEEE Trans Ind Inf 18:1424–1434. https://doi.org/10.1109/TII.2021.3089330

    Article  Google Scholar 

  97. Chen L, Whyte J (2022) Understanding design change propagation in complex engineering systems using a digital twin and design structure matrix. Eng Constr Archit Manag 29:2950–2975. https://doi.org/10.1108/ECAM-08-2020-0615

    Article  Google Scholar 

  98. Zhao Y, Cao C, Liu Z (2022) A Framework for Prefabricated Component Hoisting Management systems based on Digital Twin Technology. https://doi.org/10.3390/buildings12030276. Buildings 12:

  99. Tran H, Nguyen TN, Christopher P et al (2021) A digital twin approach for geometric quality assessment of as-built prefabricated façades. J Build Eng 41. https://doi.org/10.1016/j.jobe.2021.102377

  100. Wang X, Liu C, Song X, Cui X (2022) Development of an internet-of-things-based Technology System for Construction Safety Hazard Prevention. J Manag Eng 38:04022009. https://doi.org/10.1061/(ASCE)ME.1943-5479.0001035

    Article  Google Scholar 

  101. Desogus G, Quaquero E, Rubiu G et al (2021) Bim and Iot sensors integration: a framework for consumption and indoor conditions data monitoring of existing buildings. SUSTAINABILITY 13. https://doi.org/10.3390/su13084496

  102. Fujii TY, Hayashi VT, Arakaki R et al (2022) A Digital Twin Architecture Model Applied with MLOps techniques to improve short-term energy consumption prediction. MACHINES 10. https://doi.org/10.3390/machines10010023

  103. Göçer Ö, Hua Y, Göçer K (2016) A BIM-GIS integrated pre-retrofit model for building data mapping. Build Simul 9:513–527. https://doi.org/10.1007/s12273-016-0293-4

    Article  Google Scholar 

  104. Lutters E (2018) Pilot production environments driven by digital twins. South Afr J Ind Eng 29:40–53. https://doi.org/10.7166/29-3-2047

    Article  Google Scholar 

  105. Sepasgozar SME, Hui FKP, Shirowzhan S et al (2021) Lean practices using building information modeling (bim) and digital twinning for sustainable construction. SUSTAINABILITY 13:1–22. https://doi.org/10.3390/su13010161

    Article  Google Scholar 

  106. Liu Z, Li A, Sun Z et al (2022) Digital Twin-based Risk Control during Prefabricated Building Hoisting operations. Sensors 22. https://doi.org/10.3390/s22072522

  107. Reja VK, Varghese K, Ha QP (2022) Computer vision-based construction progress monitoring. Autom Constr. 138. https://doi.org/10.1016/j.autcon.2022.104245

    Article  Google Scholar 

  108. Wang W-C, Weng S-W, Wang S-H, Chen C-Y (2014) Integrating building information models with construction process simulations for project scheduling support. Autom Constr 37:68–80. https://doi.org/10.1016/j.autcon.2013.10.009

    Article  Google Scholar 

  109. González-Böhme LF, Valenzuela-Astudillo E (2023) Mixed reality for safe and Reliable Human-Robot collaboration in timber Frame Construction. Buildings 13. https://doi.org/10.3390/buildings13081965

  110. Kamari M, Ham Y (2022) AI-based risk assessment for construction site disaster preparedness through deep learning-based digital twinning. Autom Constr 134. https://doi.org/10.1016/j.autcon.2021.104091

  111. Abdelrahman MM, Chong A, Miller C (2022) Personal thermal comfort models using digital twins: preference prediction with BIM-extracted spatial–temporal proximity data from Build2Vec. Build Environ 207. https://doi.org/10.1016/j.buildenv.2021.108532

  112. Wong MO, Lee S (2023) Indoor navigation and information sharing for collaborative fire emergency response with BIM and multi-user networking. Autom Constr 148:104781. https://doi.org/10.1016/j.autcon.2023.104781

    Article  Google Scholar 

  113. Levine NM, Spencer BF Jr (2022) Post-earthquake building evaluation using UAVs: a BIM-Based Digital Twin Framework. Sensors 22. https://doi.org/10.3390/s22030873

  114. Agostinelli S, Cumo F, Guidi G, Tomazzoli C (2021) Cyber-physical systems improving building energy management: Digital twin and artificial intelligence. Energies 14. https://doi.org/10.3390/en14082338

  115. Moyano J, Gil-Arizón I, Nieto-Julián JE, Marín-García D (2022) Analysis and management of structural deformations through parametric models and HBIM workflow in architectural heritage. J Build Eng 45. https://doi.org/10.1016/j.jobe.2021.103274

  116. Antón D, Medjdoub B, Shrahily R, Moyano J (2018) Accuracy evaluation of the semi-automatic 3D modeling for historical building information models. Int J Archit Herit 12:790–805. https://doi.org/10.1080/15583058.2017.1415391

    Article  Google Scholar 

  117. Volk R, Luu TH, Mueller-Roemer JS et al (2018) Deconstruction project planning of existing buildings based on automated acquisition and reconstruction of building information. Autom Constr 91:226–245. https://doi.org/10.1016/j.autcon.2018.03.017

    Article  Google Scholar 

  118. Züst S, Züst R, Züst V et al (2021) A graph based Monte Carlo simulation supporting a digital twin for the curatorial management of excavation and demolition material flows. J Clean Prod 310. https://doi.org/10.1016/j.jclepro.2021.127453

  119. Jiang Y, Li M, Guo D et al (2022) Digital twin-enabled smart modular integrated construction system for on-site assembly. Comput Ind 136. https://doi.org/10.1016/j.compind.2021.103594

  120. Ye Z, Jingyu L, Hongwei Y (2022) A digital twin-based human-robot collaborative system for the assembly of complex-shaped architectures. Proc Inst Mech Eng PART B-J Eng Manuf. https://doi.org/10.1177/09544054221110960

    Article  Google Scholar 

  121. Liu Z, Shi G, Qin J et al (2022) Prestressed Steel Material-Allocation path and construction using Intelligent Digital Twins. Metals 12. https://doi.org/10.3390/met12040631

  122. Tian Y, Gao S (2023) A brief analysis of the View on the scale of Digital Twin City. Urban Plann Int 38:14–21. https://doi.org/10.19830/j.upi.2021.734

    Article  Google Scholar 

  123. Liu Z, Shi G, Zhang A, Huang C (2020) Intelligent tensioning method for prestressed cables based on digital twins and artificial intelligence. SENSORS 20:1–20. https://doi.org/10.3390/s20247006

    Article  Google Scholar 

  124. Shahzad M, Shafiq MT, Douglas D, Kassem M (2022) Digital Twins in Built Environments: An Investigation of the Characteristics, Applications, and Challenges. Buildings 12:. https://doi.org/10.3390/buildings12020120

  125. Xu S, Wang J, Shou W et al (2021) Computer Vision Techniques in construction: a critical review. Arch Comput Methods Eng 28:3383–3397. https://doi.org/10.1007/s11831-020-09504-3

    Article  Google Scholar 

  126. Qi CR, Su H, Mo K, Guibas LJ (2017) PointNet: Deep learning on point sets for 3D classification and segmentation. pp 77–85

  127. Peraković D, Periša M, Zorić P, Cvitić I (2020) Development and implementation possibilities of 5G in industry 4.0. Lect Notes Mech Eng 166–175. https://doi.org/10.1007/978-3-030-50794-7_17

  128. Yue Q, Mu S, Zhang L et al (2022) Assisting Smart Construction with Reliable Edge Computing Technology. Front Energy Res 10. https://doi.org/10.3389/fenrg.2022.900298

  129. Hu Z-Z, Leng S, Lin J-R et al (2022) Knowledge extraction and Discovery based on BIM: a critical review and future directions. Arch Comput Methods Eng 29:335–356. https://doi.org/10.1007/s11831-021-09576-9

    Article  Google Scholar 

  130. Li J, Kassem M (2021) Applications of distributed ledger technology (DLT) and blockchain-enabled smart contracts in construction. Autom Constr 132. https://doi.org/10.1016/j.autcon.2021.103955

  131. Wang C, Song L-H, Yuan Z, Fan J-S (2023) State-of-the-art AI-based computational analysis in civil engineering. J Ind Inf Integr 33. https://doi.org/10.1016/j.jii.2023.100470

  132. Sidani A, Dinis FM, Sanhudo L et al (2021) Recent tools and techniques of BIM-Based virtual reality: a systematic review. Arch Comput Methods Eng 28:449–462. https://doi.org/10.1007/s11831-019-09386-0

    Article  Google Scholar 

  133. Schiavi B, Havard V, Beddiar K, Baudry D (2022) BIM data flow architecture with AR/VR technologies: use cases in architecture, engineering and construction. Autom Constr 134. https://doi.org/10.1016/j.autcon.2021.104054

  134. Wang X, Liang C-J, Menassa CC, Kamat VR (2021) Interactive and immersive process-level Digital Twin for Collaborative Human–Robot Construction Work. J Comput Civ Eng 35:04021023. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000988

    Article  Google Scholar 

Download references

Acknowledgements

The work was financially supported by the Science and Technology Commission of Shanghai Municipality (No. 21DZ1204600).

Funding

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jincheng Zhao.

Ethics declarations

Competing Interests

The authors declare that they have no known competing financial interests or personal.

relationships that could have appeared to influence the work reported in this paper.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liu, J., Duan, L., Lin, S. et al. Concept, Creation, Services and Future Directions of Digital Twins in the Construction Industry: A Systematic Literature Review. Arch Computat Methods Eng (2024). https://doi.org/10.1007/s11831-024-10140-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11831-024-10140-4

Navigation