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Engineering >> 2023, Volume 24, Issue 5 doi: 10.1016/j.eng.2023.01.011

Advances in Intellectualization of Transportation Infrastructures

a College of Civil and Transportation Engineering, Shenzhen University, Shenzhen 518061, China
b School of Civil Engineering, Dalian University of Technology, Dalian 116023, China
c School of Civil and Transportation Engineering, Beijing University of Civil Engineering and Architecture, Beijing 102616, China
d Department of Geotechnical Engineering, College of Civil Engineering, Tongji University, Shanghai 200092, China
e School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
f School of Resources and Safety Engineering, Central South University, Changsha 410083, China
g School of Transportation Science and Engineering, Harbin Institute of Technology, Harbin 150090, China
h School of Safety Engineering and Emergency Management, Shijiazhuang Tiedao University, Shijiazhuang 050043, China
i Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hong Kong 999077, China

Received: 2022-10-28 Revised: 2022-12-30 Accepted: 2023-01-04 Available online: 2023-04-14

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Abstract

Inspired by state-of-the-art material science, computer techniques, artificial intelligence, and automatic control, new-generation transportation infrastructures are becoming digitalized and intelligent. Many major developed countries around the globe are actively promoting the application of innovative intelligence-based technologies in transportation infrastructures in accordance with local conditions. This review begins with a brief discussion on the basic definition, scientific foundation, and development process for the intellectualization of transportation infrastructures. Then, following the whole life-cycle chain of design, construction, operational maintenance, and elimination, the current research status and major challenges presented by intellectualization technologies are systematically investigated. Subsequently, recent achievements in intellectual technologies are comprehensively presented by selecting the Beijing–Zhangjiakou High-Speed Railway—the world's first railway built based on the concept of intelligent construction—as an example. Finally, a discussion on the future development of the intellectualization of transportation infrastructures is provided from the three dimensions of standard systems, theoretical methods, and talent training.

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References

[ 1 ] Fu W. A closer look at the construction of common terminology. Beijing: China Electric Power Press; 2014. Chinese.

[ 2 ] Du Y, Sun B, Zhang G. Smart materials and structural health monitoring. Wuhan: Huazhong University of Science and Technology Press; 2011. Chinese.

[ 3 ] Chen K, Ding L. Development of key domain-relevant technologies for smart construction in China. Strateg Stud Chin Acad Eng 2021;23(4):64‒70. Chinese. link1

[ 4 ] Liu W. Key Technology System of Intelligent Construction. Constr Archit 2020;421:72‒7. Chinese.

[ 5 ] Yao JTP. Concept of structural control. J Struct Div 1972;98(7):1567‒74. link1

[ 6 ] Ou JP, Guan XC. Research and development on the intelligent structural systems in civil engineering. Earthquake Eng Eng Dyna 1999;2:21‒8. Chinese.

[ 7 ] Bridges S. Assessment for future traffic demands and longer lives [Internet]. Sweden: Community Research and Development Information Service; 2003 Dec [cited 2022 May 25]. Available from: https://cordis.europa.eu/project/id/1653. link1

[ 8 ] Bu GP, Lee JH, Guan H, Loo YC, Blumenstein M. Prediction of long-term bridge performance: integrated deterioration approach with case studies. J Perform Constr Fac 2015;3:04014089. link1

[ 9 ] Government HM. Industrial strategy: construction 2025. London: Government and Industry in Partnership; 2013.

[10] Discussion on the management of road bridges [Internet]. Tokyo: National Institute for Land and Infrastructure Management; 2013 Sep [cited 2022 May 30]. Available from: http://www.nilim.go.jp. Japanese. link1

[11] Oztemel E, Gursev S. Literature review of Industry 4.0 and related technologies. J Intell Manuf 2020;31:127‒82. link1

[12] Yoshioka D. The future of construction drawn by I-Construction. New Archit 2017;92(4):44‒6. Japanese.

[13] Legislative outline for rebuilding infrastructure in America [Internet]. Washington DC: The White House; 2018 Feb 12 [cited 2021 Oct 26]. Available from: https://www.politico.com/f/?id=00000161-8a9d-d53a-a5f5-bffd597b0000. link1

[14] Guiding opinions on promoting the coordinated development of intelligent construction and building industrialization [Internet]. Beijing: Ministry of Housing and Urban-Rural Development; 2020 July 3 [cited 2021 Oct 26]. Available from: http://www.mohurd.gov.cn/wjfb/202007/t20200728_246537.html. Chinese. link1

[15] Ling J, Li X, Li H, Shen Y, Rui Y, Zhu H. Data acquisition-interpretation-aggregation for dynamic design of rock tunnel support. Autom Construct 2022;143:104577. link1

[16] Huang J, Feng X, Zhou Y, Yang C. Stability analysis of deep-buried hard rock underground laboratories based on stereophotogrammetry and discontinuity identification. Bull Eng Geol Environ 2019;78(7):5195‒217. link1

[17] Gore A. The digital earth: understanding our planet in the 21st century. Photogramm Eng Remote Sensing 1999;5:528‒30. link1

[18] Zhu H. From digital earth to digital stratum-new thinking of geotechnical engineering development. Geotech Eng World 1998;12:15‒7. Chinese.

[19] Zhu H, Li X. Underground space and engineering. Chin J Rock Mech Eng 2007;26(11):227788. Chinese.

[20] Miorandi D, Sicari S, Pellegrini FD, Chlamtac I. Internet of things: vision applications and research challenges. Ad Hoc Netw 2012;10(7):1497‒516. link1

[21] Li J, Lu Z. Method for calculating the tunnel convergence based on the locating laser scanning point cloud. Geotech Investig Surv 2016;44(12):52‒5. Chinese.

[22] Leberl F, Irschara A, Pock T, Meixner P, Gruber M, Scholz S, et al. Point clouds: lidar versus 3D Vision. Photogramm Eng Remote Sensing 2010;76(10):1123‒34. link1

[23] Li X, Chen J, Zhu H. A new method for automated discontinuity trace mapping on rock mass 3D surface model. Comput Geosci 2016;89:118‒31. link1

[24] Xin X, Cai H. Ontology and rule-based natural language processing approach for interpreting textual regulations on underground utility infrastructure. Adv Eng Inf 2021;48:101288. link1

[25] Zhu H, Wu W, Li X, Chen J, Huang X. High-precision Acquisition, analysis and service of rock tunnel information based on iS3 platform. Chin J Rock Mech Eng 2017;36(10):2350‒64. Chinese.

[26] Lin S, Yi T, Li H, Chen Y. Damage detection in the cable structures of a bridge using the virtual distortion method. J Bridg Eng 2017;22(8):04017039. link1

[27] Hammad A, Itoh Y, Nishido T. Bridge planning using GIS and expert system approach. J Comput Civ Eng 1993;7(3):278‒95. link1

[28] Rafiq MY, Bugmann G, Easterbook DJ. Neural network design for engineering applications. Comput Struc 2001;79(17):1541‒52. link1

[29] Ballal T, Sher W. Artificial neural network for the selection of buildable structural systems. Eng Construct Architect Manag 2003;10(4):263‒71. link1

[30] Jootoo A, Lattanzi D. Bridge type classification: supervised learning on a modified NBI dataset. J Comput Civ Eng 2017;31(6):04017063. link1

[31] Sigmund O, Maute K. Topology optimization approaches. Struct Multidiscipl Optim 2013;48(6):1031‒55. link1

[32] Yu Y, Hur T, Jung J, Jang IG. Deep learning for determining a near-optimal topological design without any iteration. Struct Multidiscipl Optim 2019;59(3):787‒99. link1

[33] Zhou G, Sun Y, Jia P. Application of genetic algorithm based BP neural network to parameter inversion of surrounding rock and deformation prediction. Mod Tunn Technol 2018;55(1):107‒13. Chinese.

[34] Ling T, Qin J, Song Q, Hua F. Intelligent displacement back-analysis based on improved particle swarm optimization and neural network and its application. J Railw Sci Eng 2020;17(9):2181‒90. Chinese.

[35] Zhu H, Li X, Lin X. Infrastructure Smart Service System (iS3) and its application. China Civ Eng J 2018;51(1):1‒12. Chinese.

[36] Tao F, Liu W, Zhang M, Hu T, Qi Q, Qi Q. Five-dimension digital twin model and its ten applications. Comput Integr Manuf Syst 2019;25(1):1‒18. Chinese.

[37] Sakdirat K, Qiang L. Digital twin aided sustainability-based lifecycle management for railway turnout systems. J Clean Prod 2019;228:1537‒51. link1

[38] Chen J, Zhu H, Li X. Automatic extraction of discontinuity orientation from rock mass surface 3D point cloud. Comput Geosci 2016;95:18‒31. link1

[39] Hwang B, Shan M, Ong J, Krishnankutty P. Mechanization in building construction projects: assessment and views from the practitioners. Prod Plann Contr 2020;31(8):613‒28. link1

[40] Gao S, Jin R, Lu W. Design for manufacture and assembly in construction: a review. Build Res Inform 2020;48(5):538‒50. link1

[41] Pellicer E, Correa CL, Yepes V, Alarcón LF. Organizational improvement through standardization of the innovation process in construction firms. Eng Manag J 2012;24(2):40‒53. link1

[42] Woodhead R, Stephenson P, Morrey D. Digital construction: from point solutions to IoT ecosystem. Autom Construct 2018;93:35‒46. link1

[43] Chayama K, Fujioka A, Kawashima K, Yamamoto H, Nitta Y, Ueki C, et al. Technology of unmanned construction system in Japan. J Robot Mechatron 2014;26(4):403‒17. link1

[44] Forcael E, Ferrari I, Opazo-Vega A, Pulido-Arcas JA. Construction 4.0: a Lterature review. Sustainability 2020;12(22):9755. link1

[45] Niu Y, Lu W, Chen K, Liu D. Smart construction objects. J Comput Civ Eng 2015;30(4):04015070. link1

[46] Liu W. Modernization, informationization, digitalization, intelligentization and their interconnections. Chin Railrays 2011;1:83‒6. Chinese.

[47] Zhang P, Chen R, Dai T, Wang Z, Wu K. An AloT-based system for real-time monitoring of tunnel construction. Tunn Undergr Sp Tech 2021;109:103766. link1

[48] Li J, Jing L, Zheng X, Li P, Yang C. Application and outlook of information and intelligence technology for safe and efficient TBM construction. Tunn Undergr Sp Tech 2019;93:103097. link1

[49] Koopialipoor M, Tootoonchi H, Armaghani DJ, Mohamad ET, Hedayat A. Application of deep neural networks in predicting the penetration rate of tunnel boring machines. Bull Eng Geol Environ 2019;78(8):6347‒60. link1

[50] Wu R, Fujita Y, Soga K. Integrating domain knowledge with deep learning models: an interpretable AI system for automatic work progress identification of NATM tunnels. Tunn Undergr Sp Tech 2020;105:103558. link1

[51] Mahmoodzadeh A, Mohammadi M, Daraei A, Faraj RH, Omer RMD, Sherwani AFH. Decision-making in tunneling using artificial intelligence tools. Tunn Undergr Sp Tech 2020;103:103514. link1

[52] Lu C, Liu J, Liu Y, Liu Y. Intelligent construction technology of railway engineering in China. Front Eng Manag 2019;6(4):503‒16. link1

[53] Wu Z, Wei R, Chu Z, Liu Q. Real-time rock mass condition prediction with TBM tunneling big data using a novel rock‒machine mutual feedback perception method. J Rock Mech Geotech Eng 2021;13(6):1311‒25. link1

[54] Zhao R, Shi S, Li S, Guo W, Zhang T, Li X, et al. Deep learning for intelligent prediction of rock strength by adopting measurement while drilling data. Int J Geomech 2023;4:04023028. link1

[55] Xu Z, Shi H, Lin P, Liu T. Integrated lithology identification based on images and elemental data from rocks. J Pet Sci Eng 2021;205:108853. link1

[56] Xu Z, Liu F, Lin P, Shao R, Shi X. Non-destructive, in-situ, fast identification of adverse geology in tunnels based on anomalies analysis of element content. Tunn Undergr Sp Tech 2021;118:104146. link1

[57] Wang Z. Status and prospect of intelligent construction technology of tunnel of Zhengzhou‒Wanzhou high-speed railway. Tunnel Constr 2021;41(11):1877‒90.

[58] Kong F, Lu D, Ma Y, Li J, Tian T. Analysis and intelligent prediction for displacement of stratum and tunnel lining by shield tunnel excavation in complex geological conditions: a case study. IEEE T Intell Transp 2022;23(11):22206‒16. link1

[59] Zeng L, Shu W, Liu Z, Zou X, Wang S, Xia J, et al. Vision-based high-precision intelligent monitoring for shield tail clearance. Automat Constr 2022;134:104088. link1

[60] Zhang K, Lyu HM, Shen SL, Zhou A, Yin ZY. Data on evolutionary hybrid neural network approach to predict shield tunneling-induced ground settlements. Data Brief 2020;33:106432. link1

[61] Wang F, Lu H, Gou B, Han X, Zhang Q, Qin Y. Modeling of shield-ground interaction using an adaptive relevance vector machine. App Math Model 2016;40(9):5171‒82. link1

[62] Hu M, Wu B, Zhou W, Wu H, Li G, Lu J, et al. Self-driving shield: Intelligent systems, methodologies, and practice. Autom Construct 2022;139:104326. link1

[63] Wang F, Sui H, Kong W, Zhong H. Application of BIM+ VR technology in immersed tunnel construction. IOP Conference Series: Earth and Environmental Science. Bristol: IOP Publishing; 2021. link1

[64] Liu C, Bu Q, Lin S, Li F. BIM-based collaborative management and intelligent manufacturing in the Shenzhong Link Project. E3S Web of Conferences. Essonne: EDP Sciences; 2019. link1

[65] Zhao N, Liu R, Hao S, Li J, Wu K, Jiao C. Research on Shenzhong Link immersed tunnel intelligent traffic control strategies. International Conference on Intelligent Transportation Engineering. Singapore: Springer; 2022. link1

[66] Brownjohn JMW. Structural health monitoring of civil infrastructure. Philos Trans- Royal Soc, Math Phys Eng Sci 1851;2007(365):589‒622.

[67] Yi T. Structural health monitoring. Beijing: Higher Education Press; 2021. Chinese.

[68] Shim CS, Dang NS, Lon S, Jeon CH. Development of a bridge maintenance system for prestressed concrete bridges using 3D digital twin model. Struct Infrastruct Eng 2019;15(10):1319‒32. link1

[69] Gao Z. Design and Application of maintenance management system for Hangzhou Bay Sea-Crossing Bridge. Highw 2013;03:196‒201. Chinese.

[70] Xia Z, Gao W, Jing Q. Application and the prospect of BIM technology in bridge operation and maintenance stage. Guangdong Highw Commun 2022;48(01):32‒7. Chinese.

[71] Gu Y, Shui J, Yu J. Construction and application of intelligent cloud control platform for Hangzhou Bay Sea-Crossing Bridge. China ITS J 2021;05:122‒5. Chinese.

[72] Xiong W, Liu H, Guo J, Wu J. Bridge deck system reconstruction and mechanical behavior analysis of Nanjing Yangtze River Bridge. J Southwest Univ 2018;48(2):350‒6. Chinese.

[73] Sun Y. Reconstruction and reuse of the modern architecture-taking the customhouse cultural relics renovation project as an example. Archit Cult 2017;11:83‒4. Chinese.

[74] Liu H. Research on service life prediction model of concrete structure of sea-crossing. Const Des Proj 2016;10:100‒2. Chinese.

[75] Huang H, Yi T, Li H, Liu H. New representative temperature for performance alarming of bridge expansion joints through temperature-displacement relationship. J Bridg Eng 2018;23(7):04018043. link1

[76] Yi T, Yao X, Qu C, Li H. Clustering number determination for sparse component analysis during output-only modal identification. J Eng Mech 2019;145(1):04018122. link1

[77] Yang X, Yi T, Qu C, Li H, Liu H. Modal identification of high-speed railway bridges through free-vibration detection. J Eng Mech 2020;146(9):04020107. link1

[78] Dong L, Hu Q, Tong X, Liu Y. Velocity-free MS/AE source location method for three-dimensional hole-containing structures. Engineering 2020;6(7):827‒34. link1

[79] Fu Y, Yang Z, Liang Z. Based on research on the application of structural health monitoring of bridge Operation BIM technology. Southwest Highw 2019;1:20‒5. Chinese.

[80] Guo J, Lin J, Han Y, Li X, Liu J. Application study of BIM technology in refined management of project cost. Chongqing Archit 2016;15(8):10‒2. Chinese.

[81] Ministry of science and Technology of the People’s Republic of China. Exposure draft of the Application Guide for 2021 Targeted Projects of the Key Project of ‘Solid Waste Resource Recovery’ 2020 [accessed 10 October 2020]. Chinese.

[82] Lin Z. Study on crystal modulation dynamics and pollutant interface behavior during heavy metal pollution control. In: Proceedings of the 2016 National Symposium on Environmental Nanotechnology and Nanoimpact; Xiamen: Chinese Chemical Society, China Instrument and Control Society; 2016. link1

[83] Zhang J, Chen W, Zhang S, Guo J, Lui S. Multimodal deep neural network for construction waste object segmentation. J Image Graph 2019;24(7):1136‒47.

[84] Zhang S, Chen Y, Yang Z, Gong H. Computer vision based two-stage waste recognition-retrieval algorithm for waste classification. Resour Conserv Recycling 2021;169:105543. link1

[85] Xu Z. Analysis on the technology and innovation development of chinese solid waste resource utilization. Appl Energ Tech 2021;3:19‒21.

[86] Hu K, Chen Y, Naz F, Zeng C, Cao S. Separation studies of concrete and brick from construction and demolition waste. Waste Manag 2019;85:396‒404. link1

[87] Mittal G, Yagnik KB, Garg M, Krishnan NC. SpotGarbage: smartphone app to detect garbage using deep learning. In: Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing; 2016 Sep 12‒16; Heidelberg, Germany: ACM Digital Library; 2016. p. 940‒5. link1

[88] Carlos B, Alejandro R, Manuel E. Automatic waste classification using computer vision as an application in Colombian high schools. In: Proceedings of the 6th Latin-American Conference on Networked and Electronic Media; 2015 Sep 23‒25; Medellin, Colombia: IEEE Xplore; 2015. link1

[89] Lukka TJ, Tossavainen T, Kujala JV, Raiko T. Zenrobotics recycler‒robotic sorting using machine learning. Proceedings of the International Conference on Sensor-Based Sorting (SBS). Citeseer, 2014;1:1.

[90] Wang S, Xu F, He C. Development of industrial sorting robots based on visual analysis. Shandong Ind Tech 2017;24:22. Chinese.

[91] Liu WL, Jia SL, Liu J. Separation of solid waste organic materials and inorganic materials based on Infrared Absorption Spectroscopy. In: Proceedings of the 2019 National Academic Conference on Environmental Engineering (the second volume), Beijing: Industrial Construction Magazine Agency; 2019:298‒301. Chinese.

[92] Kong X. The research of solid waste management and monitoring system based on Internet of Things technology. Shenyang: Shenyang Ligong University; 2012. Chinese.

[93] Liu S. Study on construction waste intelligent management system based on Internet of Things technology. Dalian: Dalian Maritime University; 2015. Chinese.

[94] Wang N. Demonstration and control model of construction waste and demonstration of practical engineering application. Beijing: Beijing Jiaotong University; 2019. Chinese.

[95] Kazmi S, Munir MJ, Wu YF, Patnaikuni I, Zhou YW, Xing F. Effect of recycled aggregate treatment techniques on the durability of concrete: a comparative evaluation. Constr Build Mater 2020;264:120284. link1

[96] Wang X, Yang X, Ren J, Han NX, Xing F. A novel treatment method for recycled aggregate and the mechanical properties of recycled aggregate concrete. J Mater Res Technol 2020;10:1389‒401. link1

[97] Munir MJ, Wu YF, Kazmi S, Patnaikuni I, Zhou YW, Xing F. Stress-strain behavior of spirally confined recycled aggregate concrete: an approach towards sustainable design. Resour Conserv Recycling 2019;146:127‒39. link1

[98] Wu B, Ji M, Zhao X. State-of-the-art of recycled mixed concrete (RMC) and composite structural members made of RMC. Eng Mech 2016;33(1):1‒10.

[99] Ahmed RB, Hossain K. Waste cooking oil as an asphalt rejuvenator: a state-of-the-art review. Constr Build Mater 2020;230:116985. link1

[100] Zhang JH, Ding L, Li F, Peng JH. Recycled aggregates from construction and demolition wastes as alternative filling materials for highway subgrades in China. J Clean Prod 2020;255:120223. link1

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