Digital Twin Enabling Technologies for Advancing Road Engineering and Lifecycle Applications

Yu Yan, Lei Ni, Lijun Sun, Ying Wang, Jianing Zhou

Engineering ›› 2025, Vol. 44 ›› Issue (1) : 184-206.

PDF(5630 KB)
PDF(5630 KB)
Engineering ›› 2025, Vol. 44 ›› Issue (1) : 184-206. DOI: 10.1016/j.eng.2024.12.017
Research
Review

Digital Twin Enabling Technologies for Advancing Road Engineering and Lifecycle Applications

Author information +
History +

Abstract

Road infrastructure is facing significant digitalization challenges within the context of new infrastructure construction in China and worldwide. Among the advanced digital technologies, digital twin (DT) has gained prominence across various engineering sectors, including the manufacturing and construction industries. Specifically, road engineering has demonstrated a growing interest in DT and has achieved promising results in DT-related applications over the past several years. This paper systematically introduces the development of DT and examines its current state in road engineering by reviewing research articles on DT-enabling technologies, such as model creation, condition sensing, data processing, and interaction, as well as its applications throughout the lifecycle of road infrastructure. The findings indicate that research has primarily focused on data perception and virtual model creation, while real-time data processing and interaction between physical and virtual models remain underexplored. DT in road engineering has been predominantly applied during the operation and maintenance phases, with limited attention given to the construction and demolition phases. Future efforts should focus on establishing uniform standards, developing innovative perception and data interaction techniques, optimizing development costs, and expanding the scope of lifecycle applications to facilitate the digital transformation of road engineering. This review provides a comprehensive overview of state-of-the-art advancements in this field and paves the way for leveraging DT in road infrastructure lifecycle management.

Graphical abstract

Keywords

Digital twin / Road infrastructure / Enabling technology / Life cycle

Cite this article

Download citation ▾
Yu Yan, Lei Ni, Lijun Sun, Ying Wang, Jianing Zhou. Digital Twin Enabling Technologies for Advancing Road Engineering and Lifecycle Applications. Engineering, 2025, 44(1): 184‒206 https://doi.org/10.1016/j.eng.2024.12.017

References

[1]
Wang JW, Gao C, Dong S, Xu S, Yuan CW, Zhang C, et al. Current status and future prospects of existing research on digitalization of highway infrastructure. China J Highw Transp 2020; 33(11):101-124.
[2]
US Department of Transportation. Smart grants program [Internet]. Washington, DC: US Department of Transportation; [cited 2024 Jul 1]. Available from: https://www.transportation.gov/grants/SMART.
[3]
Shafto M, Conroy M, Doyle R, Glaessgen E, Wang L. Modeling, simulation, information technology and processing roadmap. National Aeronautics and Space Administration, Washington, DC (2010).
[4]
Naderi H, Shojaei A. Digital twinning of civil infrastructures: current state of model architectures, interoperability solutions, and future prospects. Autom Construct 2023; 149:104785.
[5]
Jiang F, Ma L, Broyd T, Chen K. Digital twin and its implementations in the civil engineering sector. Autom Construct 2021; 130:103838.
[6]
Tao F, Qi Q, Wang L, Nee AYC. Digital twins and cyber–physical systems toward smart manufacturing and Industry 4.0: correlation and comparison. Engineering 2019; 5(4):653-661.
[7]
Kritzinger W, Karner M, Traar G, Henjes J, Sihn W. Digital twin in manufacturing: a categorical literature review and classification. IFAC-PapersOnLine 2018; 51(11):1016-1022.
[8]
Yan B, Yang F, Qiu S, Wang J, Cai B, Wang S, et al. Digital twin in transportation infrastructure management: a systematic review. Intell Transp Infrastruct 2023; 2:liad024.
[9]
Tao F, Zhang M, Liu Y, Nee AYC. Digital twin driven prognostics and health management for complex equipment. CIRP Ann 2018; 67(1):169-172.
[10]
Grieves M, Vickers J. Origins of the digital twin concept. In: Digital twin: mitigating unpredictable, undesirable emergent behavior in complex systems (Excerpt). Melbourne: Florida Institute of Technology; 2016.
[11]
Tao F, Liu W, Zhang M, Hu T, Qi Q, Zhang H, et al. Five-dimension digital twin model and its ten applications. Comput Integr Manuf 2019; 25(1):1-18.
[12]
Tao F, Zhang H, Liu A, Nee AYC. Digital twin in industry: state-of-the-art. IEEE Trans Industr Inform 2019; 15(4):2405-2415.
[13]
Tao F, Cheng J, Qi Q, Zhang M, Zhang H, Sui F. Digital twin-driven product design, manufacturing and service with big data. Int J Adv Manuf Technol 2018; 94(9–12):3563-3576.
[14]
Zhuang C, Liu J, Xiong H. Digital twin-based smart production management and control framework for the complex product assembly shop-floor. Int J Adv Manuf Technol 2018; 96(1–4):1149-1163.
[15]
Leng J, Wang D, Shen W, Li X, Liu Q, Chen X. Digital twins-based smart manufacturing system design in Industry 4.0: a review. J Manuf Syst 2021; 60:119-137.
[16]
Corral-Acero J, Margara F, Marciniak M, Rodero C, Loncaric F, Feng Y, et al. The “digital twin” to enable the vision of precision cardiology. Eur Heart J 2020; 41(48):4556-4564.
[17]
Li W, Rentemeister M, Badeda J, Jöst D, Schulte D, Sauer DU. Digital twin for battery systems: cloud battery management system with online state-of-charge and state-of-health estimation. J Energy Storage 2020; 30:101557.
[18]
Lu Y, Huang X, Zhang K, Maharjan S, Zhang Y. Low-latency federated learning and blockchain for edge association in digital twin empowered 6G networks. IEEE Trans Industr Inform 2021; 17(7):5098-5107.
[19]
Glaessgen E, Stargel D. The digital twin paradigm for future NASA and US Air Force vehicles. In: Proceedings of the 53rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference; 2012 Apr 23–26; Honolulu, HI, USA. Reston: American Institute of Aeronautics and Astronautics; 2012. p. 1818.
[20]
Dalibor M, Jansen N, Rumpe B, Schmalzing D, Wachtmeister L, Wimmer M, et al. A cross-domain systematic mapping study on software engineering for digital twins. J Syst Softw 2022; 193:111361.
[21]
Muctadir HM, Manrique DA Negrin, Gunasekaran R, Cleophas L, van M den Brand, Haverkort BR. Current trends in digital twin development, maintenance, and operation: an interview study. Soft Syst Model 2024; 23(5):1275-1305.
[22]
The approach to delivering a national digital twin for the United Kingdom [Internet]. Cambridge: University of Cambridge; [cited 2024 Apr 28]. Available from: https://www.cdbb.cam.ac.uk/files/approach_summaryreport_final.pdf.
[23]
AlBalkhy W, Karmaoui D, Ducoulombier L, Lafhaj Z, Linner T. Digital twins in the built environment: definition, applications, and challenges. Autom Construct 2024; 162:105368.
[24]
Boje C, Guerriero A, Kubicki S, Rezgui Y. Towards a semantic construction digital twin: directions for future research. Autom Construct 2020; 114:103179.
[25]
Lu Q, Parlikad AK, Woodall P, Don G Ranasinghe, Xie X, Liang Z, et al. Developing a digital twin at building and city levels: a case study of west Cambridge campus. J Manage Eng 2020; 36(3):05020004.
[26]
Su S, Zhong RY, Jiang Y, Song J, Fu Y, Cao H. Digital twin and its potential applications in construction industry: state-of-art review and a conceptual framework. Adv Eng Inform 2023; 57:102030.
[27]
Madubuike OC, Anumba CJ, Khallaf R. A review of digital twin applications in construction. J Inf Technol Constr 2022; 27:145-172.
[28]
Opoku DGJ, Perera S, Osei-Kyei R, Rashidi M. Digital twin application in the construction industry: a literature review. J Build Eng 2021; 40:102726.
[29]
Gao Y, Qian S, Li Z, Wang P, Wang F, He Q. Digital twin and its application in transportation infrastructure. In: Proceedings of the 2021 IEEE 1st International Conference on Digital Twins and Parallel Intelligence (DTPI 2021); 2021 Jul 15–Aug 15; Beijing, China. New York City: IEEE; 2021. p. 298–301.
[30]
Sanfilippo F, Thorstensen RT, Jha A, Jiang Z, Robbersmyr KG. A perspective review on digital twins for roads, bridges, and civil infrastructures. In: Proceedings of the 2022 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME 2022); 2022 Nov 16–18; Maldives, Maldives. New York City: IEEE; 2024. p. 1–6.
[31]
Vieira J, Martins JP, de NM Almeida, Patrício H, Morgado JG. Towards resilient and sustainable rail and road networks: a systematic literature review on digital twins. Sustainability 2022; 14(12):7060.
[32]
Hosamo HH, Hosamo MH. Digital twin technology for bridge maintenance using 3D laser scanning: a review. Adv Civ Eng 2022 Jul:2194949.
[33]
Tuhaise VV, Tah JHM, Abanda FH. Technologies for digital twin applications in construction. Autom Construct 2023; 152:104931.
[34]
Song H, Yang G, Li H, Zhang T, Jiang A. Digital twin enhanced BIM to shape full life cycle digital transformation for bridge engineering. Autom Construct 2023; 147:104736.
[35]
Gerges M, Koris K, Fawad M, Al-Hijazeen AZO, Salamak M. Implementation of digital twin and support vector machine in structural health monitoring of bridges. Arch Civ Eng 2023; 69(3):31-47.
[36]
Zhang A, Yang J, Wang F. Application and enabling digital twin technologies in the operation and maintenance stage of the AEC industry: a literature review. J Build Eng 2023; 80:107859.
[37]
Vieira J, Patrício H, Po Jças Martins, Gomes J Morgado, Almeida N. The potential value of digital twin in rail and road infrastructure asset management. Lect Notes Mech Eng (2023 Feb), pp. 439-447.
[38]
Wang X, Zhang Y, Li H, Wang C, Feng P. Applications and challenges of digital twin intelligent sensing technologies for asphalt pavements. Autom Construct 2024; 164:105480.
[39]
Wang Y, Wang H, Wang W, Song S, Fu X. Architecture, application, and prospect of digital twin for highway infrastructure. J Traffic Transp Eng 2024; 11(5):835-852.
[40]
Wang Y, Chen Z, Zhang C, Hu H, Zhang Z, Zhu M. Digital twin-driven smart transportation infrastructure: connotation, reference model, applications and research issues. In: Proceedings of the 2024 25th IEEE International Conference on Industrial Technology (ICIT 2024); 2024 Mar 25–27; Bristol, UK. New York City: IEEE; 2024. p. 1–6.
[41]
Sakr M, Sadhu A. Recent progress and future outlook of digital twins in structural health monitoring of civil infrastructure. Smart Mater Struct 2024; 33(3):033001.
[42]
Taherkhani R, Ashtari MA, Aziminezhad M. Digital twin-enabled infrastructures: a bibliometric analysis-based review. J Infrastruct Syst 2024; 30(1):03123001.
[43]
Li T, Li X, Rui Y, Ling J, Zhao S, Zhu H. Digital twin for intelligent tunnel construction. Autom Constr 2024; 158:105210.
[44]
Zhao Y, Liu Y, Mu E. A review of intelligent subway tunnels based on digital twin technology. Buildings 2024; 14(8):2452.
[45]
Talaghat MA, Golroo A, Kharbouch A, Rasti M, Heikkilä R, Jurva R. Digital twin technology for road pavement. Autom Construct 2024; 168:105826.
[46]
Jiang F, Ma L, Broyd T, Chen K, Luo H. Underpass clearance checking in highway widening projects using digital twins. Autom Constr 2022; 141:104406.
[47]
Jiang F, Ma L, Broyd T, Chen W, Luo H. Digital twin enabled sustainable urban road planning. Sustain Cities Soc 2022; 78:103645.
[48]
Akbarialiabad H, Pasdar A, Murrell DF. Digital twins in dermatology, current status, and the road ahead. NPJ Digit Med 2024; 7(1):228.
[49]
Tchana Y, Ducellier G, Remy S. Designing a unique digital twin for linear infrastructures lifecycle management. Procedia CIRP 2019; 84:545-549.
[50]
Steyn WJ, Broekman A, Jordaan GJ. Digital twinning of asphalt pavement surfacings using visual simultaneous localization and mapping. K. Anupam, A.T. Papagiannakis, A. Bhasin, D. Little (Eds.), Advances in materials and pavement performance prediction II, CRC Press, London 2020; 97-100.
[51]
Gulisano F, Jimenez-Bermejo D, Castano-Solís S, Sánchez LA Diez, Gallego J. Development of self-sensing asphalt pavements: review and perspectives. Sensors 2024; 24(3):792.
[52]
Ye S, Lai X, Bartoli I, Aktan AE. Technology for condition and performance evaluation of highway bridges. J Civ Struct Health Monit 2020; 10(4):573-594.
[53]
van A Raan. The use of bibliometric analysis in research performance assessment and monitoring of interdisciplinary scientific developments. Tech Theor Prax 2003; 12(1):20-29.
[54]
Cepa JJ, Pavón RM, Alberti MG, Caram Pés. Towards BIM-GIS integration for road intelligent management system. J Civ Eng Manag 2023; 29(7):621-638.
[55]
Wu B, Liu H, Li A, Huang Z. Application and innovation of BIM technology in municipal projects. In: Proceedings of the 2021 5th International Conference on Civil Engineering, Architectural and Environmental Engineering; 2021 Apr 23–25; Chengdu, China. Berlin: Springer; 2021. p. 012182.
[56]
Zhu H, Wei G, Ma D, Yu X, Xu Z, Wang H. 3D digital modelling and identification of pavement typical internal defects based on GPR measured data. Road Mater Pavement Des 2024; 25(10):2283.
[57]
Me Sža, Mauko A Pranjić, Vezo Rčnik, Osmokrovi Ić, Lenart S. Digital twins and road construction using secondary raw materials. J Adv Transp (2021 Jan), pp. 1-12.
[58]
Han T, Ma T, Fang Z, Zhang Y, Han CA. BIM–IoT and intelligent compaction integrated framework for advanced road compaction quality monitoring and management. Comput Electr Eng 2022; 100:107981.
[59]
Hidayat F, Supangkat SH, Hanafi K. Digital twin of road and bridge construction monitoring and maintenance. In: Proceedings of the 2022 8th IEEE International Smart Cities Conference (ISC2 2022); 2022 Sep 26–29; Paphos, Cyprus. New York City: IEEE; 2022. p. 1–7.
[60]
Barisic L, Levenberg E, Skar A, Boyd A, Zoulis P. A thermal digital twin for condition monitoring of asphalt roads. X. Liu, K. Anupam, S. Erkens, L. Sun, J. Ling (Eds.), Green and intelligent technologies for sustainable and smart asphalt pavements, CRC Press, London 2022; 709-713.
[61]
Tang R, Zhu J, Ren Y, Ding Y, Wu J, Guo Y, et al. A knowledge-guided fusion visualisation method of digital twin scenes for mountain highways. ISPRS Int J Geoinf 2023; 12(10):424.
[62]
Steyn WJ. Selected implications of a hyper-connected world on pavement engineering. Int J Pavement Res Technol 2020; 13(6):673-678.
[63]
Rumpa SH, Ishrat S, Reza ST, Shafiqul M Islam Suman, Faysal M Ahmmed, Mansoor N. InfraChain: a sensor-enabled roadway management application using blockchain and digital twin. Lect Notes Netw Syst 2024; 834:457-463.
[64]
Shen K, Wang H. Development of high-efficient asphalt pavement modeling software for digital twin of road infrastructure. Adv Eng Softw 2024; 198:103786.
[65]
Cao T, Wang Y, Liu S. Pavement crack detection based on 3D edge representation and data communication with digital twins. IEEE Trans Intell Transp Syst 2023; 24(7):7697-7706.
[66]
Fox-Ivey R, Laurent J, Petitclerc B. Using 3D pavement surveys to create a digital twin of your runway or highway. In: Proceedings of the 2021 International Airfield and Highway Pavements Conference (IAHPC 2021); 2021 Jun 8–10; online conference. Reston: American Society of Civil Engineers; 2021. p. 180–92.
[67]
Yu G, Zhang S, Hu M, Wang YK. Prediction of highway tunnel pavement performance based on digital twin and multiple time series stacking. Adv Civ Eng 2020; 2020(1):8824135.
[68]
Consilvio A, Hernández JS, Chen W, Brilakis I, Bartoccini L, Gennaro FD, et al. Towards a digital twin-based intelligent decision support for road maintenance. Transp Res Procedia 2023; 69:791-798.
[69]
Gouda M, Pawliuk Z, Mirza J, El-Basyouny K. Using convex hulls with octree/voxel representations of point clouds to assess road and roadside geometric design for automated vehicles. Autom Constr 2023; 154:104967.
[70]
Pan Y, Wang M, Lu L, Wei R, Cavazzi S, Peck M, et al. Scan-to-graph: automatic generation and representation of highway geometric digital twins from point cloud data. Autom Construct 2024; 166:105654.
[71]
Marai OE, Taleb T, Song J. Roads infrastructure digital twin: a step toward smarter cities realization. IEEE Netw 2021; 35(2):136-143.
[72]
Thonhofer E, Sigl S, Fischer M, Heuer F, Kuhn A, Erhart J, et al. Infrastructure-based digital twins for cooperative, connected, automated driving and smart road services. IEEE Open J Intell Transp Syst 2023; 4:311-324.
[73]
Brown LE, Weidner J, Raheem A, Long Cheu R. Vision-based methodology to create a highway asset inventory for integration in a digital twin model. In: Proceedings of the 2022 International Conference on Transportation and Development (ICTD 2022); 2022 May 31–Jun 3; Seattle, WA, USA. Reston: American Society of Civil Engineers; 2022. p. 26–33.
[74]
Ding J, Brilakis I. The potential for creating a geometric digital twin of road surfaces using photogrammetry and computer vision. In: Proceedings of the 2023 European Council on Computing in Construction (2023 EC3); 2023 Jul 10–12; Chania, Greece. Chania: The European Council on Computing in Construction; 2023.
[75]
Crampen D, Hein M, Blankenbach J. A level of as-is detail concept for digital twins of roads—a case study. In: Proceedings of the 2023 18th International 3D GeoInfo Conference (3D GeoInfo 2023); 2023 Sep 12–14; Munich, Germany. Berlin: Springer Nature; 2024. p. 499–515.
[76]
Wang W, Xu X, Peng J, Hu W, Wu D. Fine-grained detection of pavement distress based on integrated data using digital twin. Appl Sci 2023; 13(7):4549.
[77]
Sierra C, Paul S, Rahman A, Kulkarni A. Development of a cognitive digital twin for pavement infrastructure health monitoring. Infrastructures 2022; 7(9):113.
[78]
Peddinti PRT, Kim B. Efficient pavement monitoring for South Korea using unmanned aerial vehicles. In: Proceedings of the 2022 International Conference on Transportation and Development (ICTD 2022); 2022 May 31–Jun 3; Seattle, WA, USA. Reston: American Society of Civil Engineers (ASCE); 2022. p. 61–72.
[79]
Lu L, Dai F. Digitalization of traffic scenes in support of intelligent transportation applications. J Comput Civ Eng 2023; 37(5):04023019.
[80]
Jiang F, Ma L, Broyd T, Chen W, Luo H. Building digital twins of existing highways using map data based on engineering expertise. Autom Construct 2022; 134:104081.
[81]
Fang Z, Jiang F, Yan J, Lu Q, Chen L, Tang J, et al. A novel lightweight CF decision-making approach for highway reconstruction and operation. J Clean Prod 2024; 434:140127.
[82]
Liu H, Ma R. An efficient and automatic method based on monocular camera and GNSS for collecting and updating geographical coordinates of mileage pile in highway digital twin map. Meas Sci Technol 2024; 35(12):126011.
[83]
Lu Q, Chen L, Li S, Pitt M. Semi-automatic geometric digital twinning for existing buildings based on images and CAD drawings. Autom Constr 2020; 115:103183.
[84]
Matthews J, Love PED, Heinemann S, Chandler R, Rumsey C, Olatunj O. Real time progress management: re-engineering processes for cloud-based BIM in construction. Autom Construct 2015; 58:38-47.
[85]
Wang DW, Lyu HT, Tang FJ, Ye CS, Zhang F, Wang Q, et al. Road structural defects detection and digitalization based on 3D ground penetrating radar technology: a state-of-the-art review. China J Highw Transp 2023; 36:1-19.
[86]
Appelt V. Savings potential in highway planning, construction and maintenance using BIM—German experience with PPP. A. Akhnoukh, K. Kaloush, M. Elabyad, B. Halleman, N. Erian, S. Enmon II (Eds.), Advances in Road Infrastructure and Mobility, Springer, Berlin 2022; 365-378.
[87]
Ma T, Tong Z, Zhang YM, Zhang WG. A three-dimensional reconstruction method of pavement macro-texture using a multi-view deep neural network. China J Highw Transp 2023; 36:70-80.
[88]
Davletshina D, Reja VK, Brilakis I. Automating construction of road digital twin geometry using context and location aware segmentation. Autom Construct 2024; 168:105795.
[89]
Davletshina D, Reja VK, Brilakis I. Capturing reality changes from point clouds for updating road geometric digital twins. In: Proceedings of the 2024 European Conference on Computing in Construction; 2024 Jul 14–17; Chania, Greece. Chania: The European Council on Computing in Construction; 2024.
[90]
Yang X, Li Y, Liu WB, Zhao ZY, Guan JC, Liu PF, et al. Digital twin of asphalt pavement surface and internal full-field distress based on BIM + GIS technology. China J Highw Transp 2023; 36:120-135.
[91]
Steyn WJ, Broekman A. Process for the development of a digital twin of a local road—a case study. In: Proceedings of the 2021 6th GeoChina International Conference on Civil & Transportation Infrastructures; 2021 July 19–21; Nanchang, China. Berlin: Springer Nature; 2021. p. 11–22.
[92]
Anantheswar A, Wollny I, Kaliske M. A dynamic ALE framework enabling efficient simulations within a digital twin of the pavement. In: Proceedings of the 2023 7th Chinese–European Workshop on Functional Pavements (CEW 2023); 2023 Jul 2–4; Birmingham, UK. London: CRC Press; 2024. p. 173–6.
[93]
Hildebrandt J, Leibl LM, Habich D, Lehner W. Development and evaluation of a FIWARE-based digital twin prototype for road systems. In: Proceedings of the 1st International Workshop on Distributed Digital Twins; 2024 Jun 17; Groningen, the Netherlands. Aachen: CEUR Workshop Proceedings; 2024.
[94]
Ellul C, Hamilton N, Pieri A, Floros G. Exploring data for construction digital twins: building health and safety and progress monitoring twins using the unreal gaming engine. Buildings 2024; 14(7):2216.
[95]
Chen K, Torbaghan ME, Chu M, Zhang L, Garcia-Hernández A. Identifying the most suitable machine learning approach for a road digital twin. Proc Inst Civ Eng Smart Infrastruct Constr 2022; 174:88-101.
[96]
Liu P, Zhang H, Hu Y, Du K, Guan J, Yordanov V. An efficient conditional GAN-based framework for high-resolution prediction of tyre-pavement contact stresses—a contribution towards a digital twin of the road system. Int J Pavement Eng 2024; 25(1):2414074.
[97]
Siddiqa A, Hashem IAT, Yaqoob I, Marjani M, Shamshirband S, Gani A, et al. A survey of big data management: taxonomy and state-of-the-art. J Netw Comput Appl 2016; 71:151-166.
[98]
Nakashima H, Nakamura T, Hosoi Y, Konno K, Kawamura H. Smart infrastructure asset management system on metropolitan expressway in Japan. A. Akhnoukh, K. Kaloush, M. Elabyad, B. Halleman, N. Erian, S. Enmon II (Eds.), Advances in road infrastructure and mobility, Springer Nature, Berlin 2022; 575-586.
[99]
Chang GK, Gilliland AL. TaghaviGhalesari A. Aggregating high-precision GNSS intelligent construction data for quality asphalt pavements. In: Proceedings of the 2022 3rd ISIC International Conference on Trends on Construction in the Post-Digital Era (ISIC 2022); 2022 Sep 7–9; Guimarães, Portugal. Berlin: Springer Nature; 2023. p. 349–68.
[100]
Fan R, Zhang Y, Guo S, Li J, Feng Y, Su S, et al. Urban digital twins for intelligent road inspection. In: Proceedings of the 2022 IEEE International Conference on Big Data (Big Data); 2022 Dec 17–20; Osaka, Japan. New York City: IEEE; 2022. p. 5110–4.
[101]
D’Amico F, Bertolini L, Napolitano A, Gagliardi V, Bianchini Ciampoli L. A novel BIM approach for supporting technical decision-making process in transport infrastructure management. In: Schulz K, editor. Proceedings Volume 11863, Earth Resources and Environmental Remote Sensing/GIS Applications XII-11863 (2021); 2021 Sep 13–18; online conference. Bellingham: SPIE Digital Library; 2021.
[102]
Steyn WJ, Broekman A. Development of a digital twin of a local road network: a case study. J Test Eval 2022; 50(6):2901-2915.
[103]
Gao Y, Li H, Xiong G, Song H. AIoT-informed digital twin communication for bridge maintenance. Autom Constr 2023; 150:104835.
[104]
Lin K, Xu YL, Lu X, Guan Z, Li J. Digital twin-based collapse fragility assessment of a long-span cable-stayed bridge under strong earthquakes. Autom Constr 2021; 123:103547.
[105]
Yu G, Wang Y, Mao Z, Hu M, Sugumaran V, Wang YK. A digital twin-based decision analysis framework for operation and maintenance of tunnels. Tunn Undergr Space Technol 2021; 116:104125.
[106]
Santero NJ, Masanet E, Horvath A. Life-cycle assessment of pavements. Part I: critical review. Resour Conserv Recycling 2011; 55(9–10):801-809.
[107]
Jiang F, Li J, Ma L, Dong Z, Chen W, Broyd T, et al. Sustainable urban road planning under the digital twin-MCDM-GIS framework considering multidisciplinary factors. J Clean Prod 2024; 469:143097.
[108]
Maserrat Z, Alesheikh AA, Jafari A, Charandabi NK, Shahidinejad J. A Dempster–Shafer enhanced framework for urban road planning using a model-based digital twin and MCDM techniques. ISPRS Int J Geoinf 2024; 13(9):302.
[109]
Yuan Q, Li J, Zhou H, Luo G, Lin T, Yang F, et al. Cross-domain resource orchestration for the edge-computing-enabled smart road. IEEE Netw 2020; 34(5):60-67.
[110]
Fernández-Isabel A, Fuentes-Fernández R, Martín I de Diego. Modeling multi-agent systems to simulate sensor-based smart roads. Simul Model Pract Theory 2020; 99:101994.
[111]
Sun L, Zhao H, Tu H, Tian Y. The smart road: practice and concept. Engineering 2018; 4(4):436-437.
[112]
Fu H, Zhao T, Chen Y, Yao Y, Leng J. Framework and operation of digital twin smart freeway. IET Intell Transp Syst 2023; 17(3):620-629.
[113]
Mao G, Hui Y, Ren X, Li C, Shao Y. The Internet of Things for smart roads: a road map from present to future road infrastructure. IEEE Intell Transp Syst Mag 2022; 14(6):66-76.
[114]
Twinzo. Digital twin of construction site [Internet]. San Bruno: YouTube; 2023 Mar 7 [cited 2024 Nov 13]. Available from: https://www.youtube.com/watch?v=JaVUxATlzFk.
[115]
Zheng X, Lu J, Kiritsis D. The emergence of cognitive digital twin: vision, challenges and opportunities. Int J Prod Res 2022; 60(24):7610-7632.
[116]
Gooneratne CP, Das AN, Mehta YU, Snehita NL, George B. Smartphone-based road condition monitoring: a feasibility study. In: Proceedings of the 2023 16th International Conference on Sensing Technology (ICST 2023); 2023 Dec 17–20; Hyderabad, India. New York City: IEEE; 2023. p. 1–6.
[117]
Mahmudah H, Musyafa A, Aisjah AS, Arifin S, Prastyanto CA. Digital twin: challenge road damage detection on edge device. Chem Eng Trans 2024; 109:601-606.
[118]
Heravi MY, Dola IS, Jang Y, Jeong I. Edge AI-enabled road fixture monitoring system. Buildings 2024; 14(5):1220.
[119]
D F’Amico, Bianchini L Ciampoli, Di A Benedetto, Bertolini L, Napolitano A. Integrating non-destructive surveys into a preliminary BIM-oriented digital model for possible future application in road pavements management. Infrastructures 2022; 7(1):10.
[120]
Ammar A, Nassereddine H, Dadi G. State departments of transportation’s vision toward digital twins: investigation of roadside asset data management current practices and future requirements. ISPRS Ann Photogram Remote Sens Spatial Inf Sci 2022; 4:319-327.
[121]
Ammar A, Maier F, Catchings R, Nassereddine H, Dadi G. Departments of transportation efforts to digitize ancillary transportation asset data: a step toward digital twins. Transp Res Rec 2023; 2677(11):428-445.
[122]
Vieira J, Clara J, Patrício H, Almeida N, Martins JP. Digital twins in asset management: potential application use cases in rail and road infrastructures. In: Conference proceedings info: WCEAM 2021; 2021 Aug 15–18; Campina-Grande, Brazil. Berlin: Springer Nature; 2022. p. 250–60.
[123]
Vieira J, Almeida NMD, Po Jças Martins, Patrício H, Morgado JG. Analysing the value of digital twinning opportunities in infrastructure asset management. Infrastructures 2024; 9(9):158.
[124]
Ammar A, Maier F, Pratt WS, Richard E, Dadi G. Practical application of digital twins for transportation asset data management: case example of a safety hardware asset. Transp Res Rec 2024; 2678(10):114-130.
[125]
Kodikara J, Sounthararajah A, Chen L. Reimagining unbound road pavement technology: integrating testing, design, construction and performance in the post-digital era. Transp Geotechnics 2024; 47:101274.
[126]
Tanne YA, Zultaqawa Z, Aulia.Falderika MD, Farhani S, Rivana D. Integrated system for urban road asset management: conceptual framework. In: Proceeding of the 2023 9th International Conference on Signal Processing and Intelligent Systems (ICSPIS 2023); 2023 Dec 14–15; Bali, Indonesia. New York City: IEEE; 2023. p. 1–8.
[127]
Chen K, Torbaghan ME, Thom N, Garcia-Hernandez A, Faramarzi A, Chapman D. A machine learning based approach to predict road rutting considering uncertainty. Case Stud Constr Mater 2024; 20:e03186.
[128]
Lei B, Li R, Huang R. Embedded highway health maintenance system based on digital twin superposition model. EAI Endorsed Trans Energy Web 2024; 11:1-8.
[129]
Zhu S, Peng B, Li D, Bai Y, Liu X, Li Y. Methods for addressing pavement defects based on digital twin technology—a case study of snow and water accumulation on road surface. In: Proceedings of the 2024 2nd International Conference on Urban Construction and Transportation (UCT 2024); 2024 Jan 19–21; Harbin, China. Cedex: E3S Web of Conferences; 2024. p. 1–10.
[130]
Yin M, Reja VK, Wei R, Sheil B, Brilakis I. How can digital twins be used in highway maintenance? A questionnaire survey for industry practitioners. In: Proceedings of the 2024 European Conference on Computing in Construction; 2024 Jul 14–17; Chania, Greece. Chania: The European Council on Computing in Construction; 2024.
[131]
Liu M, Fang S, Dong H, Xu C. Review of digital twin about concepts, technologies, and industrial applications. J Manuf Syst 2021; 58:346-361.
[132]
Chen W, Brilakis I. Developing digital twin data structure and integrated cloud digital twin architecture for roads. In: Proceedings of the ASCE International Conference on Computing in Civil Engineering 2023; 2023 Jun 25–28; Corvallis, OR, USA. Reston: American Society of Civil Engineers; 2024. p. 424–32.
[133]
ISO/TR 24464: Automation systems and integration—industrial data—visualization elements of digital twins. International standard. Geneva: International Organization for Standardization; 2020.
[134]
NGSI-LD API: Context information management (CI M). European standard. Nice: European Telecommunications Standards Institute; 2021.
[135]
ISO/IEC 30173: Digital twin—concepts and terminology. International standard. Geneva: International Organization for Standardization, International Electrotechnical Commission; 2023.
[136]
G B/T 43441. 1: Information technology—digital twin—part 1: general requirements. CChinese standard. Beijing: National Standardization Administration of the People's Republic of China (SAC); 2023. Chinese.
[137]
IS O 23247–1: Automation systems and integration—digital twin framework for manufacturing—part 1: overview and general principles. International standard. Geneva: International Organization for Standardization; 2021.
[138]
IS O 23247–2: Automation systems and integration—digital twin framework for manufacturing—part 2: reference architecture. International standard. Geneva: International Organization for Standardization; 2021.
[139]
IS O 23247–3: Automation systems and integration—digital twin framework for manufacturing—part 3: digital representation of manufacturing elements. International standard. Geneva: International Organization for Standardization; 2021.
[140]
IS O 23247–4: Automation systems and integration—digital twin framework for manufacturing—part 4: information exchange. International standard. Geneva: International Organization for Standardization; 2021.
[141]
Shokravi H, Vafaei M, Samali B, Bakhary N. In-fleet structural health monitoring of roadway bridges using connected and autonomous vehicles’ data. Comput Aided Civ Infrastruct Eng 2024; 39(14):2122-2139.
AI Summary AI Mindmap
PDF(5630 KB)

Accesses

Citations

Detail

Sections
Recommended

/