Exploring the impact of digital twin technology in infrastructure management: a comprehensive review
Abstract
This paper examines the role of Digital Twin Technology (DTT) in transforming infrastructure management, with a focus on sustainability. It highlights how advancements in Artificial Intelligence (AI), Building Information Modeling (BIM), and the Internet of Things (IoT) are driving the effectiveness of Digital Twins in real-world applications. Through detailed case studies, the paper showcases the practical benefits of DTT across various infrastructure sectors. It also evaluates current trends and strategies for enhancing DTT integration into infrastructure systems. The research reveals a striking 80% increase in DTT-related publications from 2019 to 2024, with Asia, particularly China, leading in contributions. The paper concludes by addressing the future potential, challenges, and risks of DTT, offering valuable insights for stakeholders aiming to optimize infrastructure management in the digital era.
Keyword : digital twin (DT), infrastructure management, advanced technologies, challenges, future directions

This work is licensed under a Creative Commons Attribution 4.0 International License.
References
Aheleroff, S., Xu, X., Zhong, R. Y., & Lu, Y. (2021). Digital twin as a service (DTaaS) in Industry 4.0: An architecture reference model. Advanced Engineering Informatics, 47, Article 101225. https://doi.org/10.1016/j.aei.2020.101225
Akanmu, A. A., Anumba, C. J., & Ogunseiju, O. O. (2021). Towards next generation cyber-physical systems and digital twins for construction. ITcon, 26, 505–525. https://doi.org/10.36680/j.itcon.2021.027
Akhtar, M. N., Shaikh, A. J., Khan, A., Awais, H., Bakar, E. A., & Othman, A. R. (2021). Smart sensing with edge computing in precision agriculture for soil assessment and heavy metal monitoring: A review. Agriculture, 11(6), Article 475. https://doi.org/10.3390/agriculture11060475
Alexopoulos, K., Nikolakis, N., & Chryssolouris, G. (2020). Digital twin-driven supervised machine learning for the development of artificial intelligence applications in manufacturing. International Journal of Computer Integrated Manufacturing, 33(5), 429–439. https://doi.org/10.1080/0951192X.2020.1747642
Ali, T., Irfan, M., Shaf, A., Alwadie, A. S., Sajid, A., Awais, M., & Aamir, M. (2020). A secure communication in iot enabled underwater and wireless sensor network for smart cities. Sensors, 20(15), Article 4309. https://doi.org/10.3390/s20154309
Altowaijri, S. M. (2020). An architecture to improve the security of cloud computing in the healthcare sector. In R. Mehmood, R. S. See, I. Katib, & I. Chlamtac (Eds.), Smart infrastructure and applications. EAI/Springer innovations in communication and computing (pp. 249–266). Springer, Cham. https://doi.org/10.1007/978-3-030-13705-2_10
Ammar, A., Nassereddine, H., AbdulBaky, N., AbouKansour, A., Tannoury, J., Urban, H., & Schranz, C. (2022). Digital twins in the construction industry: A perspective of practitioners and building authority. Frontiers in Built Environment, 8, Article 834671. https://doi.org/10.3389/fbuil.2022.834671
Arup. (2019). Digital twin: towards a meaningful framework.
Attaran, M., Attaran, S., & Celik, B. G. (2023). The impact of digital twins on the evolution of intelligent manufacturing and Industry 4.0. Advances in Computational Intelligence, 3(3), Article 11. https://doi.org/10.1007/s43674-023-00058-y
Attaran, S., Attaran, M., & Gokhan, B. (2024). Digital twins and industrial Internet of Things: Uncovering operational intelligence in industry 4.0. Decision Analytics Journal, 10, Article 100398. https://doi.org/10.1016/j.dajour.2024.100398
Austin, M., Delgoshaei, P., Coelho, M., & Heidarinejad, M. (2020). Architecting smart city digital twins: Combined semantic model and machine learning approach. Journal of Management in Engineering, 36(4), Article 04020026. https://doi.org/10.1061/(asce)me.1943-5479.0000774
Azhar, S. (2011). Building information modeling (BIM): Trends, benefits, risks, and challenges for the AEC industry. Leadership and Management in Engineering, 11(3), 241–252. https://doi.org/10.1061/(ASCE)LM.1943-5630.0000127
Bado, M. F., Tonelli, D., Poli, F., Zonta, D., & Casas, J. R. (2022). Digital twin for civil engineering systems: An exploratory review for distributed sensing updating. Sensors, 22(9), Article 3168. https://doi.org/10.3390/s22093168
Bao, L., Wang, Q., & Jiang, Y. (2021). Review of digital twin for intelligent transportation system. In Proceedings of 2021 International Conference on Information Control, Electrical Engineering and Rail Transit (ICEERT 2021) (pp. 309–315), Lanzhou, China. IEEE. https://doi.org/10.1109/ICEERT53919.2021.00064
Barricelli, B. R., Casiraghi, E., & Fogli, D. (2019). A survey on digital twin: Definitions, characteristics, applications, and design implications. IEEE Access, 7, 167653–167671. https://doi.org/10.1109/ACCESS.2019.2953499
Bello, S. A., Yu, S., Wang, C., Adam, J. M., & Li, J. (2020). Review: Deep learning on 3D point clouds. Remote Sensing, 12(11), Article 1729. https://doi.org/10.3390/rs12111729
Boje, C., Guerriero, A., Kubicki, S., & Rezgui, Y. (2020). Towards a semantic construction digital twin: Directions for future research. Automation in Construction, 114, Article 103179. https://doi.org/10.1016/j.autcon.2020.103179
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, Cham. https://doi.org/10.1007/978-3-319-32156-1_1
Callcut, M., Cerceau Agliozzo, J. P., Varga, L., & McMillan, L. (2021). Digital twins in civil infrastructure systems. Sustainability, 13(20), Article 11549. https://doi.org/10.3390/su132011549
Cheng, M. Y., Khasani, R. R., & Setiono, K. (2023). Image quality enhancement using HybridGAN for automated railway track defect recognition. Automation in Construction, 146(11), Article 104669. https://doi.org/10.1016/j.autcon.2022.104669
Chui, K. T., Gupta, B. B., Torres-Ruiz, M., Arya, V., Alhalabi, W., & Zamzami, I. F. (2023). A convolutional neural network-based feature extraction and weighted twin support vector machine algorithm for context-aware human activity recognition. Electronics, 12(8), Article 1915. https://doi.org/10.3390/electronics12081915
Cimino, C., Negri, E., & Fumagalli, L. (2019). Review of digital twin applications in manufacturing. Computers in Industry, 113, Article 103130. https://doi.org/10.1016/j.compind.2019.103130
Correia, A. G., Azenha, M., Cruz, P. J. S., Novais, P., & Pereira, P. (Eds.). (2023). Trends on construction in the digital era. Proceedings of ISIC 2022 (Vol. 306). Springer. https://doi.org/10.1007/978-3-031-20241-4
Cortese, T. T. P., de Almeida, J. F. S., Batista, G. Q., Storopoli, J. E., Liu, A., & Yigitcanlar, T. (2022). Understanding sustainable energy in the context of smart cities: A PRISMA review. Energies, 15(7), Article 2382. https://doi.org/10.3390/en15072382
Cui, D., Ai, C., Zaheer, Q., Wang, J., Qiu, S., Li, F., & Xiong, J. (2023). Target-free recognition of cable vibration in complex backgrounds based on computer vision. Mechanical Systems and Signal Processing, 197, Article 110392. https://doi.org/10.1016/j.ymssp.2023.110392
Dan, D., Ying, Y., & Ge, L. (2022). Digital twin system of bridges group based on machine vision fusion monitoring of bridge traffic load. IEEE Transactions on Intelligent Transportation Systems, 23(11), 22190–22205. https://doi.org/10.1109/TITS.2021.3130025
Dang, N. S., & Shim, C. S. (2020). Bridge assessment for PSC girder bridge using digital twins model. In C. Ha-Minh, D. Dao, F. Benboudjema, S. Derrible, D. Huynh, & A. Tang (Eds.), Lecture notes in civil engineering: Vol. 54. CIGOS 2019, Innovation for sustainable infrastructure (pp. 1241–1246). Springer, Singapore. https://doi.org/10.1007/978-981-15-0802-8_199
Deryabin, S. A., Temkin, I. O., & Zykov, S. V. (2020). About some issues of developing digital twins for the intelligent process control in quarries. Procedia Computer Science, 176, 3210–3216. https://doi.org/10.1016/j.procs.2020.09.128
El Saddik, A. (2018). Digital twins: The convergence of multimedia technologies. IEEE Multimedia, 25(2), 87–92. https://doi.org/10.1109/MMUL.2018.023121167
Elfarri, E. M., Rasheed, A., & San, O. (2023). Artificial intelligence-driven digital twin of a modern house demonstrated in virtual reality. IEEE Access, 11, 35035–35058. https://doi.org/10.1109/ACCESS.2023.3265191
Enders, M. R., & Hoßbach, N. (2019). Dimensions of digital twin applications – A literature review. In 25th Americas Conference on Information Systems (AMCIS 2019).
Ford, D. N., & Wolf, C. M. (2020). Smart cities with digital twin systems for disaster management. Journal of Management in Engineering, 36(4), Article 04020027. https://doi.org/10.1061/(asce)me.1943-5479.0000779
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
Gao, Y., Qian, S., Li, Z., Wang, P., Wang, F., & He, Q. (2021). Digital twin and its application in transportation infrastructure. In 2021 IEEE 1st International Conference on Digital Twins and Parallel Intelligence (DTPI 2021) (pp. 298–301). IEEE. https://doi.org/10.1109/DTPI52967.2021.9540108
Ghansah, F. A., & Lu, W. (2025). Cyber-physical systems and digital twins for “cognitive building” in the construction industry. Construction Innovation. 25(3), 787–818. https://doi.org/10.1108/CI-07-2022-0164
Grieves, M. (2022). Intelligent digital twins and the development and management of complex systems. Digital Twin, 2(8). https://doi.org/10.12688/digitaltwin.17574.1
Grigg, N. S. (1998). Infrastructure engineering and management. John Wiley & Sons.
Gürdür Broo, D., Bravo-Haro, M., & Schooling, J. (2022). Design and implementation of a smart infrastructure digital twin. Automation in Construction, 136, Article 104171. https://doi.org/10.1016/j.autcon.2022.104171
Guskova, M. F., Shutina, A. O., & Trofimov, D. A. (2020). Modeling the digital twins of transport infrastructure objects. In Proceedings of the 2020 IEEE International Conference “Quality Management, Transport and Information Security, Information Technologies” (IT&QM&IS 2020) (pp. 368–371), Yaroslavl, Russia. IEEE. https://doi.org/10.1109/ITQMIS51053.2020.9322938
Ham, Y., & Kim, J. (2020). Participatory sensing and digital twin city: Updating virtual city models for enhanced risk-informed decision-making. Journal of Management in Engineering, 36(3), Article 04020005. https://doi.org/10.1061/(asce)me.1943-5479.0000748
Hofmann, W., & Branding, F. (2019). Implementation of an IoT- And cloud-based digital twin for real-time decision support in port operations. IFAC-PapersOnLine, 52(13), 2104–2109. https://doi.org/10.1016/j.ifacol.2019.11.516
Hu, W., Wang, W., Ai, C., Wang, J., Wang, W., Meng, X., Liu, J., Tao, H., & Qiu, S. (2021). Machine vision-based surface crack analysis for transportation infrastructure. Automation in Construction, 132, Article 103973. https://doi.org/10.1016/j.autcon.2021.103973
Hu, W., Wang, W., Liu, X., Peng, J., Wang, S., Ai, C., Qiu, S., Wang, W., Wang, J., Zaheer, Q., & Wang, L. (2024). Hybrid pixel-level crack segmentation for ballastless track slab using digital twin model and weakly supervised style transfer. Structural Control and Health Monitoring, 2024, Article 8846470. https://doi.org/10.1155/2024/8846470
Huang, W., Zhang, Y., & Zeng, W. (2022). Development and application of digital twin technology for integrated regional energy systems in smart cities. Sustainable Computing: Informatics and Systems, 36, Article 100781. https://doi.org/10.1016/j.suscom.2022.100781
Hussain, M., Ye, Z., Chi, H.-L., & Hsu, S.-C. (2024). Predicting degraded lifting capacity of aging tower cranes: A digital twin-driven approach. Advanced Engineering Informatics, 59, Article 102310. https://doi.org/10.1016/j.aei.2023.102310
Hossam, O. M. A., & Youssef, N. A. H. (2024). Deep learning-based integration of IoT and intelligent infrastructure: Enabling real-time decision-making in smart environments. Journal of Sustainable Technologies and Infrastructure Planning, 8(4), 71–90.
Ignat, N. D., & Stanculeanu, F. (2009). An overview on modeling and simulation of critical infrastructures. In International Conference on Management and Industrial Engineering (pp. 192–198). Niculescu Publishing House.
Ilyas, M., Jin, Z., Ullah, I., Zaheer, Q., & Ali Aden, W. (2024). The influence of customer relationships on supply chain risk mitigation in international logistics. Civil Engineering Journal, 10(6), 1874–1889. https://doi.org/10.28991/cej-2024-010-06-010
Jafari, M., Kavousi-Fard, A., Chen, T., & Karimi, M. (2023). A review on digital twin technology in smart grid, transportation system and smart city: Challenges and future. IEEE Access, 11, 17471–17484. https://doi.org/10.1109/ACCESS.2023.3241588
Jeong, D. Y., Baek, M. S., Lim, T. B., Kim, Y. W., Kim, S. H., Lee, Y. T., Jung, W. S., & Lee, I. B. (2022). Digital twin: Technology evolution stages and implementation layers with technology elements. IEEE Access, 10, 52609–52620. https://doi.org/10.1109/ACCESS.2022.3174220
Jiang, F., Ma, L., Broyd, T., & Chen, K. (2021). Digital twin and its implementations in the civil engineering sector. Automation in Construction, 130, Article 103838. https://doi.org/10.1016/j.autcon.2021.103838
Jiao, Y., Zhai, X., Peng, L., Liu, J., Liang, Y., & Yin, Z. (2024). A digital twin-based motion forecasting framework for preemptive risk monitoring. Advanced Engineering Informatics, 59, Article 102250. https://doi.org/10.1016/j.aei.2023.102250
Jones, D., Snider, C., Nassehi, A., Yon, J., & Hicks, B. (2020). Characterising the digital twin: A systematic literature review. CIRP Journal of Manufacturing Science and Technology, 29, 36–52. https://doi.org/10.1016/j.cirpj.2020.02.002
Kaewunruen, S., Sresakoolchai, J., Ma, W., & Phil-Ebosie, O. (2021). Digital twin aided vulnerability assessment and risk-based maintenance planning of bridge infrastructures exposed to extreme conditions. Sustainability, 13(4), Article 2051. https://doi.org/10.3390/su13042051
Karaarslan, E., Aydin, Ö., Cali, Ü., & Challenger, M. (2023). Digital twin driven intelligent systems and emerging metaverse. Springer, Cham. https://doi.org/10.1007/978-981-99-0252-1
Khan, M. A., & Park, H. (2024). Exploring explainable artificial intelligence techniques for interpretable neural networks in traffic sign recognition systems. Electronics, 13(2), Article 306. https://doi.org/10.3390/electronics13020306
Kshetri, N. (2021). The economics of digital twins. Computer, 54, 86–90. https://doi.org/10.1109/MC.2021.3055683
Lee, D., Lee, S. H., Masoud, N., Krishnan, M. S., & Li, V. C. (2022). Digital twin-driven deep reinforcement learning for adaptive task allocation in robotic construction. Advanced Engineering Informatics, 53, Article 101710. https://doi.org/10.1016/j.aei.2022.101710
Lee, A. J., Song, W., Yu, B., Choi, D., Tirtawardhana, C., & Myung, H. (2023). Survey of robotics technologies for civil infrastructure inspection. Journal of Infrastructure Intelligence and Resilience, 2(1), Article 100018. https://doi.org/10.1016/j.iintel.2022.100018
Leitão, P., Karnouskos, S., Ribeiro, L., Lee, J., Strasser, T., & Colombo, A. W. (2016). Smart agents in industrial cyber-physical systems. Proceedings of the IEEE, 104(5), 1086–1101. https://doi.org/10.1109/JPROC.2016.2521931
Li, J., & Kassem, M. (2021). Applications of distributed ledger technology (DLT) and Blockchain-enabled smart contracts in construction. Automation in Construction, 132, Article 103955. https://doi.org/10.1016/j.autcon.2021.103955
Li, Z., Cheng, C., Kwan, M. P., Tong, X., & Tian, S. (2019). Identifying asphalt pavement distress using UAV LiDAR point cloud data and random forest classification. ISPRS International Journal of Geo-Information, 8(1), Article 39. https://doi.org/10.3390/ijgi8010039
Liu, C., Le Roux, L., Körner, C., Tabaste, O., Lacan, F., & Bigot, S. (2022). Digital twin-enabled collaborative data management for metal additive manufacturing systems. Journal of Manufacturing Systems, 62, 857–874. https://doi.org/10.1016/j.jmsy.2020.05.010
Liu, C., Zhang, P., & Xu, X. (2023). Literature review of digital twin technologies for civil infrastructure. Journal of Infrastructure Intelligence and Resilience, 2(3), Article 100050. https://doi.org/10.1016/j.iintel.2023.100050
Lu, Q., Parlikad, A. K., Woodall, P., Don Ranasinghe, G., Xie, X., Liang, Z., Konstantinou, E., Heaton, J., & Schooling, J. (2020a). Developing a digital twin at building and city levels: Case study of West Cambridge Campus. Journal of Management in Engineering, 36(3), Article 05020004. https://doi.org/10.1061/(asce)me.1943-5479.0000763
Lu, Q., Xie, X., Parlikad, A. K., Schooling, J. M., & Konstantinou, E. (2020b). Moving from building information models to digital twins for operation and maintenance. Proceedings of the Institution of Civil Engineers: Smart Infrastructure and Construction, 174(2), 46–56. https://doi.org/10.1680/jsmic.19.00011
Lv, Z., Li, Y., Feng, H., & Lv, H. (2022). Deep learning for security in digital twins of cooperative intelligent transportation systems. IEEE Transactions on Intelligent Transportation Systems, 23(9), 16666–16675. https://doi.org/10.1109/TITS.2021.3113779
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. https://doi.org/10.1016/j.ifacol.2018.08.415
Mahmoodian, M., Shahrivar, F., Setunge, S., & Mazaheri, S. (2022). Development of digital twin for intelligent maintenance of civil infrastructure. Sustainability, 14(14), Article 8664. https://doi.org/10.3390/su14148664
Maimour, M., Ahmed, A., & Rondeau, E. (2024). Survey on digital twins for natural environments: A communication network perspective. Internet of Things, 25, Article 101070. https://doi.org/10.1016/j.iot.2024.101070
Manocha, A., Sood, S. K., & Bhatia, M. (2024). IoT-digital twin-inspired smart irrigation approach for optimal water utilization. Sustainable Computing: Informatics and Systems, 41, Article 100947. https://doi.org/10.1016/j.suscom.2023.100947
Mitra, S. (2017). Applications of machine learning and computer vision for smart infrastructure management in civil engineering [Master’s thesis]. University of New Hampshire, Durham. https://scholars.unh.edu/thesis/1138
Montero, J., & Finger, M. (2021). Digitalizing infrastructure: active management for smarter networks. In J. Montero, & M. Finger (Eds.), A modern guide to the digitalization of infrastructure (pp. 1–42). Elgar. https://doi.org/10.4337/9781839106057.00007
Mylonas, G., Kalogeras, A., Kalogeras, G., Anagnostopoulos, C., Alexakos, C., & Munoz, L. (2021). Digital twins from smart manufacturing to smart cities: A survey. IEEE Access, 9, 143222–143249. https://doi.org/10.1109/ACCESS.2021.3120843
Nativi, S., Mazzetti, P., & Craglia, M. (2021). Digital ecosystems for developing digital twins of the earth: The destination earth case. Remote Sensing, 13(11), Article 2119. https://doi.org/10.3390/rs13112119
Nor, A. K. B. M., Pedapait, S. R., & Muhammad, M. (2021). Explainable AI (XAI) for PHM of industrial asset: A state-of-the-art, PRISMA-compliant systematic review. arXiv. http://arxiv.org/abs/2107.03869
Pan, Y., & Zhang, L. (2021). A BIM-data mining integrated digital twin framework for advanced project management. Automation in Construction, 124, Article 103564. https://doi.org/10.1016/j.autcon.2021.103564
Pappaterra, M. J., Flammini, F., Vittorini, V., & Bešinović, N. (2021). A systematic review of artificial intelligence public datasets for railway applications. Infrastructures, 6(10), Article 136. https://doi.org/10.3390/infrastructures6100136
Park, S., Wang, X., Menassa, C. C., Kamat, V. R., & Chai, J. Y. (2023). Natural language instructions for intuitive human interaction with robotic assistants in field construction work. Automation in Construction, 161, Article 105345. https://doi.org/10.1016/j.autcon.2024.105345
Parmar, R., Leiponen, A., & Thomas, L. D. W. (2020). Building an organizational digital twin. Business Horizons, 63(6), 725–736. https://doi.org/10.1016/j.bushor.2020.08.001
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, Article 8846667. https://doi.org/10.1155/2020/8846667
Piras, G., Muzi, F., & Tiburcio, V. A. (2024). Enhancing space management through digital twin: A case study of the Lazio region headquarters. Applied Sciences, 14(17), Article 7463. https://doi.org/10.3390/app14177463
Qi, H., Xu, T., Wang, G., Cheng, Y., & Chen, C. (2020). MYOLOv3-Tiny: A new convolutional neural network architecture for real-time detection of track fasteners. Computers in Industry, 123, Article 103303. https://doi.org/10.1016/j.compind.2020.103303
Qi, Q., Tao, F., Hu, T., Anwer, N., Liu, A., Wei, Y., Wang, L., & Nee, A. Y. C. (2021). Enabling technologies and tools for digital twin. Journal of Manufacturing Systems, 58(PB), 3–21. https://doi.org/10.1016/j.jmsy.2019.10.001
Qiu, S., Cai, B., Wang, W., Wang, J., Zaheer, Q., Liu, X., Hu, W., & Peng, J. (2024a). Automated detection of railway defective fasteners based on YOLOv8-FAM and synthetic data using style transfer. Automation in Construction, 162, Article 105363. https://doi.org/10.1016/j.autcon.2024.105363
Qiu, S., Zaheer, Q., Ehsan, H., Shah, S. M. A. H., A., Ai, C., Wang, J., & Zheng, A. A. (2024b). Multimodal fusion network for crack segmentation with modified U-Net and transfer learning–based MobileNetV2. Journal of Infrastructure Systems, 30(4), Article 04024029. https://doi.org/10.1061/JITSE4.ISENG-2499
Qiu, S., Zaheer, Q., Shah, S. M. A. H., Ai, C., Wang, J., & Zhan, Y. (2024c). Vector-quantized variational teacher and multimodal collaborative student based knowledge distillation paradigm for crack segmentation. [Preprint article]. https://doi.org/10.2139/ssrn.4791791
Ramonell, C., Chacón, R., & Posada, H. (2023). Knowledge graph-based data integration system for digital twins of built assets. Automation in Construction, 156, Article 105109. https://doi.org/10.1016/j.autcon.2023.105109
Rudskoy, A., Ilin, I., & Prokhorov, A. (2021). Digital twins in the intelligent transport systems. Transportation Research Procedia, 54, 927–935. https://doi.org/10.1016/j.trpro.2021.02.152
Sacks, R., Brilakis, I., Pikas, E., Xie, H. S., & Girolami, M. (2020). Construction with digital twin information systems. Data-Centric Engineering, 1(6), Article e14. https://doi.org/10.1017/dce.2020.16
Salehi, H., Burgueño, R., Chakrabartty, S., Lajnef, N., & Alavi, A. H. (2021). A comprehensive review of self-powered sensors in civil infrastructure: State-of-the-art and future research trends. Engineering Structures, 234, Article 111963. https://doi.org/10.1016/j.engstruct.2021.111963
Schrettenbrunnner, M. B. (2020). Artificial-intelligence-driven management. IEEE Engineering Management Review, 48(2), 15–19. https://doi.org/10.1109/EMR.2020.2990933
Semeraro, C., Lezoche, M., Panetto, H., & Dassisti, M. (2021). Digital twin paradigm: A systematic literature review. Computers in Industry, 130, Article 103469. https://doi.org/10.1016/j.compind.2021.103469
Shahzad, M., Shafiq, M. T., Douglas, D., & Kassem, M. (2022). Digital twins in built environments: An investigation of the characteristics, applications, and challenges. Buildings, 12(2), Article 120. https://doi.org/10.3390/buildings12020120
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. https://doi.org/10.1080/15732479.2019.1620789
Sivori, D., Ierimonti, L., Venanzi, I., Ubertini, F., & Cattari, S. (2023). An equivalent frame digital twin for the seismic monitoring of historic structures: A case study on the Consoli Palace in Gubbio, Italy. Buildings, 13(7), Article 1840. https://doi.org/10.3390/buildings13071840
Song, J., Liu, S., Ma, T., Sun, Y., Tao, F., & Bao, J. (2023). Resilient digital twin modeling: A transferable approach. Advanced Engineering Informatics, 58, Article 102148. https://doi.org/10.1016/j.aei.2023.102148
Song, X., Wang, W., Deng, Y., Su, Y., Jia, F., Zaheer, Q., & Long, X. (2024). Data-driven modeling for residual velocity of projectile penetrating reinforced concrete slabs. Engineering Structures, 306, Article 117761. https://doi.org/10.1016/j.engstruct.2024.117761
Sørensen, A. Ø., Olsson, N., & Landmark, A. D. (2016). Big Data in construction management research. In World Building Congress (pp. 405–416), Tampere, Finland.
Stark, R., Fresemann, C., & Lindow, K. (2019). Development and operation of digital twins for technical systems and services. CIRP Annals, 68(1), 129–132. https://doi.org/10.1016/j.cirp.2019.04.024
Su, S., Zhong, R. Y., Jiang, Y., Song, J., Fu, Y., & Cao, H. (2023). Digital twin and its potential applications in construction industry: State-of-art review and a conceptual framework. Advanced Engineering Informatics, 57, Article 102030. https://doi.org/10.1016/j.aei.2023.102030
Tang, R., De Donato, L., Bes̆inović, N., Flammini, F., Goverde, R. M. P., Lin, Z., Liu, R., Tang, T., Vittorini, V., & Wang, Z. (2022). A literature review of artificial intelligence applications in railway systems. Transportation Research Part C: Emerging Technologies, 140, Article 103679. https://doi.org/10.1016/j.trc.2022.103679
Tao, F., Zhang, H., Liu, A., & Nee, A. Y. C. (2019). Digital twin in industry: State-of-the-art. IEEE Transactions on Industrial Informatics, 15(4), 2405–2415. https://doi.org/10.1109/TII.2018.2873186
Tao, F., Xiao, B., Qi, Q., Cheng, J., & Ji, P. (2022). Digital twin modeling. Journal of Manufacturing Systems, 64, 372–389. https://doi.org/10.1016/j.jmsy.2022.06.015
VanDerHorn, E., & Mahadevan, S. (2021). Digital twin: Generalization, characterization and implementation. Decision Support Systems, 145, Article 113524. https://doi.org/10.1016/j.dss.2021.113524
Vats, T., Singh, S. K., Kumar, S., Gupta, B. B., Gill, S. S., Arya, V., & Alhalabi, W. (2023). Explainable context-aware IoT framework using human digital twin for healthcare. Multimedia Tools and Applications, 83, 62489–62490. https://doi.org/10.1007/s11042-023-16922-5
Vu, T. T., Yamazaki, F., & Matsuoka, M. (2009). Multi-scale solution for building extraction from LiDAR and image data. International Journal of Applied Earth Observation and Geoinformation, 11(4), 281–289. https://doi.org/10.1016/j.jag.2009.03.005
Wang, H., & Markine, V. (2018). Corrective countermeasure for track transition zones in railways: Adjustable fastener. Engineering Structures, 169, 1–14. https://doi.org/10.1016/j.engstruct.2018.05.004
Wang, L., Xue, X., Zhao, Z., & Wang, Z. (2018). The impacts of transportation infrastructure on sustainable development: Emerging trends and challenges. International Journal of Environmental Research and Public Health, 15(6), Article 1172. https://doi.org/10.3390/ijerph15061172
Wang, K., Hu, Q., Zhou, M., Zun, Z., & Qian, X. (2021). Multi-aspect applications and development challenges of digital twin-driven management in global smart ports. Case Studies on Transport Policy, 9(3), 1298–1312. https://doi.org/10.1016/j.cstp.2021.06.014
Wang, Z., Gupta, R., Han, K., Wang, H., Ganlath, A., Ammar, N., & Tiwari, P. (2022). Mobility digital twin: Concept, architecture, case study, and future challenges. IEEE Internet of Things Journal, 9(18), 17452–17467. https://doi.org/10.1109/JIOT.2022.3156028
Wang, J., Wei, X., Wang, W., Wang, J., Peng, J., Wang, S., Zaheer, Q., You, J., Xiong, J., & Qiu, S. (2023). A multistation 3D point cloud automated global registration and accurate positioning method for railway tunnels. Structural Control and Health Monitoring, 2023, Article 6705090. https://doi.org/10.1155/2023/6705090
Wang, W., Zaheer, Q., Qiu, S., Wang, W., Ai, C., Wang, J., Wang, S., & Hu, W. (2024a). Introduction to digital twin technologies in transportation infrastructure management (TIM). In Digital twin technologies in transportation infrastructure management (pp. 1–25). Springer, Singapore. https://doi.org/10.1007/978-981-99-5804-7_1
Wang, W., Peng, J., Hu, W., Wang, J., Xu, X., Zaheer, Q., & Qiu, S. (2024b). A multi-degree-of-freedom monitoring method for slope displacement based on stereo vision. Computer-Aided Civil and Infrastructure Engineering, 39(13), 2010–2027. https://doi.org/10.1111/mice.13173
Wang, W., Zaheer, Q., Qiu, S., Wang, W., Ai, C., Wang, J., Wang, S., & Hu, W. (2024c). Digital twins in operation and maintenance (O & P). In Digital twin technologies in transportation infrastructure management (pp. 179–203). Springer, Singapore. https://doi.org/10.1007/978-981-99-5804-7_6
Wang, W., Zaheer, Q., Qiu, S., Wang, W., Ai, C., Wang, J., Wang, S., & Hu, W. (2024d). Future digital twin in infrastructure management. In Digital twin technologies in transportation infrastructure management (pp. 205–222). Springer, Singapore. https://doi.org/10.1007/978-981-99-5804-7_7
Wang, W., Zaheer, Q., Qiu, S., Wang, W., Ai, C., Wang, J., Wang, S., & Hu, W. (2024e). Digital twin in TIM. In Digital twin technologies in transportation infrastructure management (pp. 111–145). Springer, Singapore. https://doi.org/10.1007/978-981-99-5804-7_4
Wang, W., Zaheer, Q., Qiu, S., Wang, W., Ai, C., Wang, J., Wang, S., & Hu, W. (2024f). Digital twins in design and construction. In Digital twin technologies in transportation infrastructure management (pp. 147–178). Springer, Singapore. https://doi.org/10.1007/978-981-99-5804-7_5
Wang, W., Zaheer, Q., Qiu, S., Wang, W., Ai, C., Wang, J., Wang, S., & Hu, W. (2024g). Digital twins technologies. In Digital twin technologies in transportation infrastructure management (pp. 27–74). Springer, Singapore. https://doi.org/10.1007/978-981-99-5804-7_2
Wang, W., Zaheer, Q., Qiu, S., Wang, W., Ai, C., Wang, J., Wang, S., & Hu, W. (2024h). Transportation infrastructure management. In Digital twin technologies in transportation infrastructure management (pp. 75–109). Springer, Singapore. https://doi.org/10.1007/978-981-99-5804-7_3
Wang, W., Yin, Q., Ai, C., Wang, J., Zaheer, Q., Niu, H., Cai, B., Qui, S., & Peng, J. (2025). Automation railway fastener tightness detection based on instance segmentation and monocular depth estimation. Engineering Structures, 322(Part B), Article 119229. https://doi.org/10.1016/j.engstruct.2024.119229
Wei, X., Wang, J., Ai, C., Liu, X., Qiu, S., Wang, J., Luo, Y., Zaheer, Q., & Li, N. (2024). Terrestrial laser scanning-assisted roughness assessment for initial support of railway tunnel. Journal of Civil Structural Health Monitoring, 14, 781–800. https://doi.org/10.1007/s13349-023-00753-x
Wei, X., Wang, J., Xiao, C., Zaheer, Q., Wang, W., Liu, X., Wang, J., Ph, D., & Qiu, S. (2025). Quantification and evaluation of roughness of initial support using terrestrial laser scanning. Journal of Computing in Civil Engineering, 39(1), Article 04024052. https://doi.org/10.1061/JCCEE5.CPENG-6069
Wetzel, E. M., & Thabet, W. Y. (2015). The use of a BIM-based framework to support safe facility management processes. Automation in Construction, 60, 12–24. https://doi.org/10.1016/j.autcon.2015.09.004
Wu, J., Yang, Y., Cheng, X. U. N., Zuo, H., & Cheng, Z. (2020). The development of digital twin technology review. In 2020 Chinese Automation Congress (CAC 2020) (pp. 4901–4906), Shanghai, China. IEEE. https://doi.org/10.1109/CAC51589.2020.9327756
Wu, Y., Zhang, K., & Zhang, Y. (2021a). Digital twin networks: A survey. IEEE Internet of Things Journal, 8(18), 13789–13804. https://doi.org/10.1109/JIOT.2021.3079510
Wu, Z., Liu, Z., Shi, K., Wang, L., & Liang, X. (2021b). Review on the construction and application of digital twins in transportation scenes. Xitong Fangzhen Xuebao / Journal of System Simulation, 33(2), 295–305 (in Chinese). https://doi.org/10.16182/j.issn1004731x.joss.20-0754
Wu, J., Wang, X., Dang, Y., & Lv, Z. (2022a). Digital twins and artificial intelligence in transportation infrastructure: Classification, application, and future research directions. Computers and Electrical Engineering, 101, Article 107983. https://doi.org/10.1016/j.compeleceng.2022.107983
Wu, Z., Chang, Y., Li, Q., & Cai, R. (2022b). A novel method for tunnel digital twin construction and virtual-real fusion application. Electronics, 11(9), Article 1413. https://doi.org/10.3390/electronics11091413
Xie, J., Li, S., & Wang, X. (2022). A digital smart product service system and a case study of the mining industry: MSPSS. Advanced Engineering Informatics, 53(216), Article 101694. https://doi.org/10.1016/j.aei.2022.101694
Xue, F., Lu, W., Chen, K., & Webster, C. J. (2019). BIM reconstruction from 3D point clouds: A semantic registration approach based on multimodal optimization and architectural design knowledge. Advanced Engineering Informatics, 42, Article 100965. https://doi.org/10.1016/j.aei.2019.100965
Yan, B., Yang, F., Qiu, S., Wang, J., Cai, B., Wang, S., Zaheer, Q., Wang, W., Chen, Y., & Hu, W. (2023). Digital twin in transportation infrastructure management: A systematic review. Intelligent Transportation Infrastructure, 2, Article liad024. https://doi.org/10.1093/iti/liad024
Yang, C., Huang, Q., Li, Z., Liu, K., & Hu, F. (2017). Big Data and cloud computing: innovation opportunities and challenges. International Journal of Digital Earth, 10(1), 13–53. https://doi.org/10.1080/17538947.2016.1239771
Yang, C., Wang, Q., Lu, W., & Li, Y. (2024). Integrated uncertain optimal design strategy for truss configuration and attitude–vibration control in rigid–flexible coupling structure with interval uncertainties. Nonlinear Dynamics, 113, 2215–2238. https://doi.org/10.1007/s11071-024-10291-w
Yao, Y., Luo, Z., Li, S., Fang, T., & Quan, L. (2018). MVSNet: Depth inference for unstructured multi-view stereo. In V. Ferrari, M. Hebert, C. Sminchisescu, & Y. Weiss (Eds.), Lecture notes in computer science: Vol. 11212. Computer vision – ECCV 2018 (pp. 785–801). Springer, Cham. https://doi.org/10.1007/978-3-030-01237-3_47
Yonggang, T., & Qamar, F. (2022). Literature review of bridge structure’s optimization and it’s development over time. International Journal for Simulation and Multidisciplinary Design Optimization, 13, Article 5. https://doi.org/10.1051/smdo/2021039
Zaheer, Q., Manzoor, M. M., & Ahamad, M. J. (2023). A review on developing optimization techniques in civil engineering. Engineering Computations, 40(2), 348–377. https://doi.org/10.1108/EC-01-2022-0034
Zhang, D., Li, Q., Chen, Y., Cao, M., He, L., & Zhang, B. (2017). An efficient and reliable coarse-to-fine approach for asphalt pavement crack detection. Image and Vision Computing, 57, 130–146. https://doi.org/10.1016/j.imavis.2016.11.018
Zhang, J., Cheng, J. C. P., Chen, W., & Chen, K. (2022). Digital twins for construction sites: Concepts, LoD definition, and applications. Journal of Management in Engineering, 38(2), Article 04021094. https://doi.org/10.1061/(asce)me.1943-5479.0000948
Zhang, A., Yang, J., & Wang, F. (2023). Application and enabling digital twin technologies in the operation and maintenance stage of the AEC industry: A literature review. Journal of Building Engineering, 80, Article 107859. https://doi.org/10.1016/j.jobe.2023.107859
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. Journal of Building Engineering, 49, Article 104028. https://doi.org/10.1016/j.jobe.2022.104028
Zhou, H., Yang, C., & Sun, Y. (2021). Intelligent ironmaking optimization service on a cloud computing platform by digital twin. Engineering, 7(9), 1274–1281. https://doi.org/10.1016/j.eng.2021.04.022
Zhuang, H., Zhang, J., & Liao, F. (2023). A systematic review on application of deep learning in digestive system image processing. The Visual Computer, 39(6), 2207–2222. https://doi.org/10.1007/s00371-021-02322-z
Zonta, D., Zandonini, R., & Bortot, F. (2007). A reliability-based bridge management concept. Structure and Infrastructure Engineering, 3(3), 215–235. https://doi.org/10.1080/15732470500315740
Zou, B., Chen, Y., Bao, Y., Liu, Z., Hu, B., Ma, J., Kuang, G., Tang, C., Sun, H., Zaheer, Q., & Long, X. (2025). Impact of tunneling parameters on disc cutter wear during rock breaking in transient conditions. Wear, 560–561, Article 205620. https://doi.org/10.1016/j.wear.2024.205620