Revolutionizing Structural Engineering: A Review of Digital Twins, BIM, and AI Applications
Article Sidebar
Main Article Content
Abstract
The structural engineering industry is at a pivotal juncture, driven by the integration of cutting-edge digital tools that are transforming traditional design, analysis, and construction practices. This review provides a comprehensive examination of three major technological advancements—Digital Twins, Building Information Modeling (BIM), and Artificial Intelligence (AI)—that are reshaping the landscape of structural engineering. By synthesizing recent research and case studies, we assess the current applications, benefits, and challenges associated with these technologies, along with their synergistic effects when used in tandem. Digital Twins enable real-time data monitoring and predictive analysis, allowing for enhanced lifecycle management and operational efficiency of infrastructure systems. BIM improves design coordination and collaboration, reducing errors and optimizing resource allocation throughout the project lifecycle. AI, meanwhile, introduces powerful data processing capabilities, enabling predictive maintenance, design optimization, and automated decision-making processes that enhance both safety and performance. Our findings indicate that while these technologies offer immense potential, there are significant implementation barriers, including data privacy concerns, high initial costs, and the need for skilled labor capable of managing complex digital tools. Future directions emphasize the need for standardized data integration protocols, advancements in digital twin modeling for structural health monitoring, and a push toward AI-driven automation in structural analysis and safety inspections. This review provides insights for engineers, researchers, and industry stakeholders aiming to leverage these technologies to achieve more sustainable, efficient, and resilient structural systems, ultimately guiding the field of structural engineering into a more digital, data-centric future.
Downloads
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
How to Cite
References
Rasheed, A., San, O., & Kvamsdal, T. (2020). A comprehensive review of enabling technologies, real-time applications, and future research directions. Mechanical Systems and Signal Processing, 140, 106490. https://doi.org/10.1016/j.ymssp.2019.106490
Jones, D., Snider, C., Nassehi, A., Yon, J., & Hicks, B. (2020). A systematic literature review. CIRP Journal of Manufacturing Science and Technology, 29, 36-52. https://doi.org/10.1016/j.cirpj.2020.02.002
Eastman, C., Teicholz, P., Sacks, R., & Liston, K. (2018). A Guide to Building Information Modeling for Owners, Designers, Engineers, Contractors, and Facility Managers (3rd ed.). John Wiley & Sons. https://doi.org/10.1002/9781119287544
Succar, B. (2019). Building information modelling maturity in the construction industry. Automation in Construction, 37, 56-68. https://doi.org/10.1016/j.autcon.2019.06.007
Love, P. E. D., Matthews, J., Simpson, I., Hill, A., & Olatunji, O. (2019). The role of BIM in managing risk in construction projects. Journal of Construction Engineering and Management, 145(6), 05019003. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001651
Smith, G. N., & Tiwari, R. (2021). Generative design using artificial intelligence in structural engineering. Journal of Computing in Civil Engineering, 35(4), 04021018. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000968
Zhang, Y., & Qin, X. (2022). Artificial intelligence for predictive maintenance in structural engineering. Engineering Structures, 244, 112755. https://doi.org/10.1016/j.engstruct.2021.112755
González, V., & Lucas, S. (2021). Machine learning approaches for structural health monitoring: A review. Journal of Civil Structural Health Monitoring, 11(3), 327-343. https://doi.org/10.1007/s13349-020-00443-3
Kassem, M., Akintoye, A., & Imran, S. (2020). Integrating BIM, AI, and digital twins for smart infrastructure asset management. Automation in Construction, 122, 103482. https://doi.org/10.1016/j.autcon.2020.103482
Ayaz, E., & Arslan, M. (2021). Future trends in artificial intelligence and BIM integration in construction. Journal of Construction Innovation, 22(1), 45-62. https://doi.org/10.1108/CI-06-2020-0093
Boje, C., Guerriero, A., Kubicki, S., & Rezgui, Y. (2020). Towards a semantic Construction Digital Twin: Directions for future research. Automation in Construction, 114, 103179. https://doi.org/10.1016/j.autcon.2020.10317
Grieves, M., & Vickers, J. (2017). Digital twin: Mitigating unpredictable, undesirable emergent behavior in complex systems. In Kahlen, F.-J., Flumerfelt, S., & Alves, A. (Eds.), Transdisciplinary Perspectives on Complex Systems (pp. 85-113). Springer. https://doi.org/10.1007/978-3-319-38756-7-4_
Lu, Q., Chen, L., Li, X., & Pitt, M. (2020). Semi-automated geometric digital twinning of existing buildings using images and CAD models. Automation in Construction, 113, 103114. https://doi.org/10.1016/j.autcon.2020.103114
Tao, F., & Qi, Q. (2019). Make more digital twins. Nature, 573(7775), 490-491. https://doi.org/10.1038/d41586-019-02849-1
Wang, Z., Jiang, Y., Zhang, X., Song, H., & Qi, Q. (2022). Towards building information modelling (BIM)-driven digital twin for lifecycle management of building energy efficiency. Energy and Buildings, 254, 111528. https://doi.org/10.1016/j.enbuild.2021.111528
G. m. azanaw,(2024).application of digital twin in structural health monitoring of civil structures: a systematic literature review based on prisma, j. mech. constr. eng., vol. 4, no. 1, pp. 1–10, apr. DOI:10.54060/a2zjournals.jmce.50
Fuller, A., Fan, Z., Day, C., & Barlow, C. (2020). Digital twin: Enabling technologies, challenges and open research. IEEE Access, 8, 108952-108971. https://doi.org/10.1109/ACCESS.2020.2998358
Blanco, J. L., Chen, L., & Bridgeman, J. (2018). Emerging trends in sustainability and innovation in civil engineering. Journal of Civil Engineering and Management, 24(7), 529-541. https://doi.org/10.1080/13923730.2018.1492496
Bock, T., & Linner, T. (2015). Robot-oriented design: Design and management tools for the deployment of automation and robotics in construction. Cambridge University Press. https://doi.org/10.1017/CBO9781316092640
Ghaffar, S. H., Corker, J., & Fan, M. (2020). Additive manufacturing technology and its implementation in construction as an eco-innovative solution. Automation in Construction, 93, 1-11. https://doi.org/10.1016/j.autcon.2018.05.005
Akinci, B., Boukamp, F., Gordon, C., Huber, D., Lyons, C., & Park, K. (2006). A formalism for the automated generation of building information models from unstructured data. Automation in Construction, 15(6), 765-778. https://doi.org/10.1016/j.autcon.2005.09.008
Talebi, S., Tamayo, A., & Alshari, A. (2020). Deep learning approaches for structural damage detection: A review of the literature. Structural Control and Health Monitoring, 27(9), e2586. https://doi.org/10.1002/stc.2586
Kouider, M., & Malecot, Y. (2018). Self-healing materials for concrete structures: An overview. Construction and Building Materials, 166, 579-593.https://doi.org/10.1016/j.conbuildmat.2018.01.129
Huang, Y., Wang, S., & Yi, X. (2022). Exploring resilience in civil infrastructure through digital twins: State of the art and future directions. Engineering Structures, 256, 113885. https://doi.org/10.1016/j.engstruct.2022.113885
Wang, Y., & He, J. (2018). Sustainability in civil engineering: A bibliometric analysis of research trends. Sustainable Cities and Society, 39, 155-165.https://doi.org/10.1016/j.scs.2018.03.003
Pourdeihimi, B., & Wilkins, J. R. (2021). Evolution of composite materials in structural engineering: Benefits, challenges, and future scope. Journal of Composites for Construction, 25(4), 04021020. https://doi.org/10.1061/(ASCE)CC.1943-5614.0001135
Tahmasebinia, F., & Pasternack, M. (2022). Structural engineering’s contribution to sustainability in the built environment: Recent advancements. Environmental Impact Assessment Review, 93, 106736. https://doi.org/10.1016/j.eiar.2022.106736
Miller, B., & Lavery, D. (2019). The impact of artificial intelligence on the future of structural engineering. Proceedings of the Institution of Civil Engineers – Structures and Buildings, 172(3), 129-137. https://doi.org/10.1680/jstbu.18.00078
Park, S., Cudney, E. A., & Agrawal, V. P. (2010). Quality management and Six Sigma in civil engineering applications. International Journal of Quality & Reliability Management, 27(7), 915-935. https://doi.org/10.1108/02656711011062371
Stark, J., & Chew, Y. H. (2018). Managing product lifecycle management implementation in civil engineering projects: Lessons and strategies. Construction Management and Economics, 36(7), 407-417. https://doi.org/10.1080/01446193.2018.1425409
C, N. (2023). Digital Twins and Its Security Issues and Implications. In International Journal of Innovative Science and Modern Engineering (Vol. 11, Issue 11, pp. 7–14). https://doi.org/10.35940/ijisme.f4228.11111123
Shah, K. (2023). Using Digital Twin Technology to Overcome Challenges in Civil Engineering and Construction: A Review. In International Journal of Engineering and Advanced Technology (Vol. 13, Issue 1, pp. 49–57). https://doi.org/10.35940/ijeat.a4305.1013123
Sudha Rani Jainoju, B. Kishore Babu, T. Annamani, T. Hemalatha, Digital Twins for Simulation Systems. (2019). In International Journal of Recent Technology and Engineering (Vol. 8, Issue 2S11, pp. 2276–2279). https://doi.org/10.35940/ijrte.b1252.0982s1119