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Abstract

This chapter aims to evaluate the use of the digital twin method in the field of construction projects by reviewing previous works related to this topic. While architects and engineers use BIM to perform conflict detection and material collection during the design phase and project contractors use it to control erection inspection, construction investigation, site, and comfort the board, it does not operate with real-time data. Therefore, according to the evolution and complexity of construction projects, the use of digital twin became important, especially after the successful implementation in manufacturing industries and other areas. This chapter shows the current status of the digital twin in construction projects. Moreover, it explains the differences between these method’s three classifications or categories, namely the digital model, digital shadow, and digital twin. According to these three classifications, the construction industries are still in the digital model, which shows the current practice of BIM. The result likewise showed that most studies previously conducted on digital twin are referred to as BIM or BIM-FM. This chapter shows the implementation of digital twins in the construction life cycle. Moreover, the use of digital twin in construction projects still suffers from the lack of a clear and compelling model to rely on when applying digital twin in construction projects. However, it could be considered shifting to the digital twin. The study shows that using DTs during the design and engineering stages could help determine which elements and information should be gained or discarded during the object’s redesign and re-engineering. At the same time, the DT’s duty during construction is to reduce building costs economically and effectively while simultaneously enhancing quality, something the traditional system cannot accomplish. The study also shows that in the long term, stakeholders in construction projects will profit from DT’s application to intelligent project life cycle management through creative and lean building procedures. Several digital twin skills or capabilities are listed that would enable real-time, web-integrated, intelligent construction digital twins, where existing methods and tools can be significantly improved to provide more innovative construction services in general.

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References

  1. Akanmu, A., & Anumba, C. J. (2015). Cyber-physical systems integration of building information models and the physical construction. Engineering, Construction and Architectural Management., 22, 516–535. https://doi.org/10.1108/ECAM-07-2014-0097

    Article  Google Scholar 

  2. Aljohani, K., & Thompson, R. G. (2016). Impacts of logistics sprawl on the urban environment and logistics: Taxonomy and review of literature. Journal of Transport Geography, 57, 255–263. https://doi.org/10.1016/J.JTRANGEO.2016.08.009

    Article  Google Scholar 

  3. Anderl, R., Haag, S., Schützer, K., & Zancul, E. (2021). Digital twin technology—An approach for Industrie 4.0 vertical and horizontal lifecycle integration. IT—Information Technology, 60, 125–132. https://doi.org/10.1515/ITIT-2017-0038/MACHINEREADABLECITATION/RIS

  4. 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 and Structures, 238, 106282. https://doi.org/10.1016/J.COMPSTRUC.2020.106282

    Article  Google Scholar 

  5. Antonino, M., Nicola, M., Claudio, D. M., Luciano, B., & Fulvio, R. C. (2019). Office building occupancy monitoring through image recognition sensors. International Journal of Safety and Security Engineering, 9, 371–380. https://doi.org/10.2495/SAFE-V9-N4-371-380

    Article  Google Scholar 

  6. Arayici, Y., Coates, P., Koskela, L., Kagioglou, M., Usher, C., & O’Reilly, K. (2011). Technology adoption in the BIM implementation for lean architectural practice. Automation in Construction. https://doi.org/10.1016/j.autcon.2010.09.016

    Article  Google Scholar 

  7. Arditi, D., & Mochtar, K. (2010). Trends in productivity improvement in the US construction industry. Construction Management and Economics, 18, 15–27. https://doi.org/10.1080/014461900370915

    Article  Google Scholar 

  8. Baldwin, A., & Bordoli, D. (2014). Handbook for construction planning and scheduling. Wiley.

    Google Scholar 

  9. Barazzetti, L., Banfi, F., Brumana, R., Oreni, D., Previtali, M., & Roncoroni, F. (2015). HBIM and augmented information: towards a wider user community of image and range-based reconstructions. In 25th International CIPA Symposium 2015 (pp. 35–42)

    Google Scholar 

  10. Barbosa, F., Woetzel, J., & Mischke, J. (2017). Reinventing construction: A route of higher productivity. McKinsey Global Institute.

    Google Scholar 

  11. Batty, M. (2018). Digital twins.

    Google Scholar 

  12. Bilal, M., Oyedele, L. O., Qadir, J., Munir, K., Ajayi, S. O., Akinade, O. O., Owolabi, H. A., Alaka, H. A., & Pasha, M. (2016). Big Data in the construction industry: A review of present status, opportunities, and future trends. Advanced Engineering Informatics, 30, 500–521. https://doi.org/10.1016/j.aei.2016.07.001

  13. Boje, C., Guerriero, A., Kubicki, S., & Rezgui, Y. (2020). Towards a semantic Construction Digital Twin: Directions for future research. Automation in Construction, 114, 103179.

    Article  Google Scholar 

  14. Bolton, A., Butler, L., Dabson, I., Enzer, M., Evans, M., Fenemore, T., Harradence, F., Keaney, E., Kemp, A., Luck, A., Pawsey, N., Saville, S., Schooling, J., Sharp, M., Smith, T., Tennison, J., Whyte, J., Wilson, A., & Makri, C. (2018). Gemini Principles (CDBB_REP_006). Britain.

    Google Scholar 

  15. Boschert, S., & Rosen, R. (2016). Digital Twin—The simulation aspect. In P. Hehenberger & D. Bradley (Eds.), Mechatronic futures: Challenges and solutions for mechatronic systems and their designers (pp. 59–74). Springer International Publishing.

    Google Scholar 

  16. Bradley, D., & Hehenberger, P. (2016). Mechatronic futures. In P. Hehenberger & D. Bradley (Eds.), Mechatronic futures: Challenges and solutions for mechatronic systems and their designers (pp. 1–15). Springer International Publishing.

    Google Scholar 

  17. Brynjolfsson, E., Hitt, L. M., & Kim, H. H. (2011). Strength in numbers: How does data-driven decisionmaking affect firm performance? SSRN Electronic Journal. https://doi.org/10.2139/ssrn.1819486

    Article  Google Scholar 

  18. Chen, W., Chen, K., Cheng, J. C. P., Wang, Q., & Gan, V. J. L. (2018). BIM-based framework for automatic scheduling of facility maintenance work orders. Automation in Construction, 91, 15–30. https://doi.org/10.1016/j.autcon.2018.03.007

  19. Dixit, S., Mandal, S. N., Sawhney, A., & Singh, S. (2017). Relationship between skill development and productivity in construction sector: A literature review. International Journal of Civil Engineering and Technology, 8, 649–665.

    Google Scholar 

  20. Doumbouya, L., Gao, G., & Guan, C. (2016). Adoption of the Building Information Modeling (BIM) for construction project effectiveness: The review of BIM benefits. American Journal of Civil Engineering and Architecture, 4, 74–79.

    Google Scholar 

  21. Götz, C. S., Karlsson, P., & Yitmen, I. (2020). Exploring applicability, interoperability and integrability of Blockchain-based digital twins for asset life cycle management. Smart and Sustainable Built Environment. ahead-of-print. https://doi.org/10.1108/SASBE-08-2020-0115

    Article  Google Scholar 

  22. Greif, T., Stein, N., & Flath, C. M. (2020). Peeking into the void: Digital twins for construction site logistics. Computers in Industry, 121, 103264. https://doi.org/10.1016/j.compind.2020.103264

  23. Grieves, M. (2014). Digital Twin: Manufacturing excellence through virtual factory replication.

    Google Scholar 

  24. Grieves, M., & Vickers, J. (2017). Digital Twin: Mitigating unpredictable, undesirable emergent behavior in complex systems. In F.-J. Kahlen, S. Flumerfelt, & A. Alves (Eds.), Transdisciplinary perspectives on complex systems: New findings and approaches (pp. 85–113). Springer International Publishing.

    Chapter  Google Scholar 

  25. Häkkinen, T., Kuittinen, M., Ruuska, A., & Jung, N. (2015). Reducing embodied carbon during the design process of buildings. Journal of Building Engineering, 4, 1–13. https://doi.org/10.1016/j.jobe.2015.06.005

  26. Han, K. K., & Golparvar-Fard, M. (2017). Potential of big visual data and building information modeling for construction performance analytics: An exploratory study. Automation in Construction, 73, 184–198. https://doi.org/10.1016/j.autcon.2016.11.004

  27. Hardin, B., & McCool, D. (2015). BIM and construction management: Proven tools, methods, and workflows. Wiley.

    Google Scholar 

  28. He, Y., Guo, J., & Zheng, X. (2018). From surveillance to digital twin: Challenges and recent advances of signal processing for industrial internet of things. IEEE Signal Processing Magazine, 35, 120–129.

    Article  Google Scholar 

  29. Howell, S., & Rezgui, Y. (2018). Beyond BIM: Knowledge management for a smarter future. BRE Electronic Publications.

    Google Scholar 

  30. Ilhan, B., & Yaman, H. (2016). Green building assessment tool (GBAT) for integrated BIM-based design decisions. Automation in Construction, 70, 26–37. https://doi.org/10.1016/j.autcon.2016.05.001

  31. el Jazzar, M., Piskernik, M., & Nassereddine, H. (2020). Digital twin in construction: An empirical analysis. In Proceedings of EG-ICE 2020 Workshop on Intelligent Computing in Engineering (pp. 501–510).

    Google Scholar 

  32. Jia, W., Wang, W., & Zhang, Z. (2022). From simple digital twin to complex digital twin Part I: A novel modeling method for multi-scale and multi-scenario digital twin. Advanced Engineering Informatics, 53, 101706. https://doi.org/10.1016/j.aei.2022.101706

    Article  Google Scholar 

  33. Jouan, P., & Hallot, P. (2020). Digital twin: Research framework to support preventive conservation policies. ISPRS International Journal of Geo-Information, 9, 228.

    Google Scholar 

  34. Kaewunruen, S., & Lian, Q. (2019). Digital twin aided sustainability-based lifecycle management for railway turnout systems. Journal of Cleaner Production, 228, 1537–1551. https://doi.org/10.1016/j.jclepro.2019.04.156

  35. Khajavi, S. H., Motlagh, N. H., Jaribion, A., Werner, L. C., & Holmström, J. (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 

  36. Kor, M. (2021). Integration of digital twin and deep learning for facilitating smart planning and construction: An exploratory analysis.

    Google Scholar 

  37. Krämer, M., & Besenyői, Z. (2018). Towards digitalization of building operations with BIM. IOP Conference Series: Materials Science and Engineering, 365, 022067. https://doi.org/10.1088/1757-899x/365/2/022067

  38. Kritzinger, W., Karner, M., Traar, G., Henjes, J., & Sihn, W. (2018). Digital Twin in manufacturing: A categorical literature review and classification. IFAC-PapersOnLine, 51, 1016–1022. https://doi.org/10.1016/j.ifacol.2018.08.474

  39. Lee, D., Lee, S. H., Masoud, N., Krishnan, M. S., & Li, V. C. (2021). Integrated digital twin and blockchain framework to support accountable information sharing in construction projects. Automation in Construction, 127, 103688. https://doi.org/10.1016/j.autcon.2021.103688

  40. Lee, J., Lapira, E., Bagheri, B., & Kao, H. (2013). Recent advances and trends in predictive manufacturing systems in big data environment. Manufacturing letters, 1, 38–41. https://doi.org/10.1016/j.mfglet.2013.09.005

  41. Li, J., Greenwood, D., & Kassem, M. (2019). Blockchain in the built environment and construction industry: A systematic review, conceptual models and practical use cases. Automation in Construction, 102, 288–307. https://doi.org/10.1016/j.autcon.2019.02.005

  42. Lin, Y.-C., & Cheung, W.-F. (2020). Developing WSN/BIM-based environmental monitoring management system for parking garages in smart cities. Journal of Management in Engineering, 36, 4020012. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000760

    Article  Google Scholar 

  43. Lin, Y.-C., Su, Y.-C., & Chen, Y.-P. (2014). Developing mobile BIM/2D barcode-based automated facility management system. The Scientific World Journal, 2014, 1–16. https://doi.org/10.1155/2014/374735

    Article  Google Scholar 

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

  45. Lu, Q., Chen, L., Li, S., & Pitt, M. (2020a). Semi-automatic geometric digital twinning for existing buildings based on images and CAD drawings. Automation in Construction, 115, 103183. https://doi.org/10.1016/j.autcon.2020.103183

  46. Lu, Q., Parlikad, A. K., Woodall, P., Don Ranasinghe, G., Xie, X., Liang, Z., Konstantinou, E., Heaton, J., & Schooling, J. (2020b). Developing a digital twin at building and city levels: Case study of West Cambridge Campus. Journal of Management in Engineering, 36. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000763

  47. Lu, Q., Xie, X., Parlikad, A. K., & Schooling, J. M. (2020c) Digital twin-enabled anomaly detection for built asset monitoring in operation and maintenance. Automation in Construction, 118, 103277. https://doi.org/10.1016/j.autcon.2020.103277

  48. Mertala-Lindsay, T., & Strålman, J. (2021). Construction digital twin: From early design to project delivery.

    Google Scholar 

  49. Miehe, R., Waltersmann, L., Sauer, A., & Bauernhansl, T. (2021). Sustainable production and the role of digital twins—Basic reflections and perspectives. Journal of Advanced Manufacturing and Processing, 3, e10078. https://doi.org/10.1002/amp2.10078

  50. Modena, C., da Porto, F., & Valluzzi, M. R. (Eds.) (2016). Brick and Block Masonry. In Proceedings of the 16th International Brick and Block Masonry Conference. CRC Press.

    Google Scholar 

  51. Nassereddine, H., Veeramani, D., & Hanna, A. (2019). Augmented reality-enabled production strategy process. Presented at the May 24.

    Google Scholar 

  52. Negri, E., Fumagalli, L., Cimino, C., & Macchi, M. (2019). FMU-supported simulation for CPS Digital Twin. Procedia Manufacturing, 28, 201–206. https://doi.org/10.1016/j.promfg.2018.12.033

  53. Negri, E., Fumagalli, L., & Macchi, M. (2017). A review of the roles of digital twin in CPS-based production systems. Procedia Manufacturing, 11, 939–948. https://doi.org/10.1016/j.promfg.2017.07.198

  54. 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. https://doi.org/10.1016/j.jobe.2021.102726

  55. Pallonetto, F., de Rosa, M., D’Ettorre, F., & Finn, D. P. (2020). On the assessment and control optimisation of demand response programs in residential buildings. Renewable and Sustainable Energy Reviews, 127, 109861. https://doi.org/10.1016/j.rser.2020.109861

  56. Pan, Y., & Zhang, L. (2021). A BIM-data mining integrated digital twin framework for advanced project management. Automation in Construction, 124, 103564. https://doi.org/10.1016/j.autcon.2021.103564

  57. Peng, Y., Lin, J. -R., Zhang, J. -P., & Hu, Z. -Z. (2017). A hybrid data mining approach on BIM-based building operation and maintenance. Building and Environment, 126, 483–495. https://doi.org/10.1016/j.buildenv.2017.09.030

  58. Peng, Y., Zhang, M., Yu, F., Xu, J., & Gao, S. (2020). Digital twin hospital buildings: An exemplary case study through continuous lifecycle integration. Advances in Civil Engineering, 2020, 1–13. https://doi.org/10.1155/2020/8846667

    Article  Google Scholar 

  59. Psarommatis, F., & May, G. (2022). A literature review and design methodology for digital twins in the era of zero defect manufacturing. International Journal of Production Research, 1–21. https://doi.org/10.1080/00207543.2022.2101960

  60. Qi, Q., & Tao, F. (2018). Digital twin and big data towards smart manufacturing and industry 4.0: 360 degree comparison. IEEE Access, 6, 3585–3593. https://doi.org/10.1109/ACCESS.2018.2793265

  61. Rausch, C., & Haas, C. (2021). Automated shape and pose updating of building information model elements from 3D point clouds. Automation in Construction, 124, 103561. https://doi.org/10.1016/j.autcon.2021.103561

  62. Rokooei, S. (2015). Building information modeling in project management: Necessities, challenges and outcomes. The Procedia—Social and Behavioral Sciences, 210, 87–95. https://doi.org/10.1016/j.sbspro.2015.11.332

  63. Sacks, R., Brilakis, I., Pikas, E., Xie, H. S., & Girolami, M. (2020). Construction with digital twin information systems. Data-Centric Engineering, 1, e14. https://doi.org/10.1017/dce.2020.16

  64. Schleich, B., Anwer, N., Mathieu, L., & Wartzack, S. (2017). Shaping the digital twin for design and production engineering. CIRP Annals, 66, 141–144. https://doi.org/10.1016/j.cirp.2017.04.040

  65. Schluse, M., Priggemeyer, M., Atorf, L., & Rossmann, J. (2018). Experimentable digital twins—Streamlining simulation-based systems engineering for industry 4.0. IEEE Transactions on Industrial Informatics, 14, 1722–1731. https://doi.org/10.1109/TII.2018.2804917

  66. Schroeder, G. N., Steinmetz, C., Pereira, C. E., Espindola, D. B. (2016). Digital twin data modeling with automation ML and a communication methodology for data exchange. IFAC-PapersOnLine, 49, 12–17. https://doi.org/10.1016/j.ifacol.2016.11.115

  67. Schweigkofler, A., Monizza, G. P., Domi, E., Popescu, A., Ratajczak, J., Marcher, C., Riedl, M., & Matt, D. (2018). Development of a digital platform based on the integration of augmented reality and BIM for the management of information in construction processes. In P. Chiabert, A. Bouras, F. Noël, & J. Ríos (Eds.), Product lifecycle management to support industry 4.0 (pp. 46–55). Springer International Publishing.

    Google Scholar 

  68. Shafto, M., Conroy, M., Doyle, R., Glaessgen, E., Kemp, C., LeMoigne, J., & Wang, L. (2012). Modeling, simulation, information technology and processing roadmap. National Aeronautics and Space Administration, 32, 1–38.

    Google Scholar 

  69. Shahat, E., Hyun, C. T., & Yeom, C. (2021). City digital twin potentials: A review and research agenda. Sustainability, 13. https://doi.org/10.3390/su13063386

  70. Shen, W., Hao, Q., & Xue, Y. (2012). A loosely coupled system integration approach for decision support in facility management and maintenance. Automation in Construction, 25, 41–48. https://doi.org/10.1016/j.autcon.2012.04.003

  71. Strother, J. B., Ulijn, J. M., & Fazal, Z. (2012). Drowning in data: A review of information overload within organizations and the viability of strategic communication principles. In Information overload: An international challenge for professional engineers and technical communicators (pp. 231–250). IEEE.

    Google Scholar 

  72. Succar, B. (2009). Building information modelling framework: A research and delivery foundation for industry stakeholders. Automation in Construction, 18, 357–375. https://doi.org/10.1016/j.autcon.2008.10.003

  73. Tao, F., Cheng, J., Qi, Q., Zhang, M., Zhang, H., & Sui, F. (2018). Digital twin-driven product design, manufacturing and service with big data. The International Journal of Advanced Manufacturing Technology, 94, 3563–3576. https://doi.org/10.1007/s00170-017-0233-1

    Article  Google Scholar 

  74. Tao, F., Sui, F., Liu, A., Qi, Q., Zhang, M., Song, B., Guo, Z., Lu, S.C.-Y., & Nee, A. Y. C. (2019). Digital twin-driven product design framework. International Journal of Production Research, 57, 3935–3953. https://doi.org/10.1080/00207543.2018.1443229

    Article  Google Scholar 

  75. Teicholz, P. M. (2013). BIM for facility managers. IFMA Foundation, Wiley.

    Google Scholar 

  76. Volk, R., Stengel, J., Schultmann, F. (2014). Building Information Modeling (BIM) for existing buildings—Literature review and future needs. Automation in Construction, 38, 109–127. https://doi.org/10.1016/j.autcon.2013.10.023

  77. Wang, D., Chen, J., Zhao, D., Dai, F., Zheng, C., Wu, X. (2017). Monitoring workers’ attention and vigilance in construction activities through a wireless and wearable electroencephalography system. Automation in Construction, 82, 122–137. https://doi.org/10.1016/j.autcon.2017.02.001

  78. Xu, X., Ma, L., & Ding, L. (2014). A framework for BIM-enabled life-cycle information management of construction project. International Journal of Advanced Robotic Systems, 11, 126. https://doi.org/10.5772/58445

    Article  Google Scholar 

  79. Yuan, X., & Anumba, C. J. (2020). Cyber-physical systems for temporary structures monitoring. In C. J. Anumba & N. Roofigari-Esfahan (Eds.), Cyber-physical systems in the built environment (pp. 107–138). Springer International Publishing.

    Chapter  Google Scholar 

  80. Zaballos, A., Briones, A., Massa, A., Centelles, P., Caballero, V. (2020). A smart campus’ digital twin for sustainable comfort monitoring. Sustainability, 12. https://doi.org/10.3390/su12219196

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Yıldırım, M., Giran, O. (2023). Digital Twin in Construction. In: Karaarslan, E., Aydin, Ö., Cali, Ü., Challenger, M. (eds) Digital Twin Driven Intelligent Systems and Emerging Metaverse. Springer, Singapore. https://doi.org/10.1007/978-981-99-0252-1_12

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