Digital twin in transportation infrastructure management: a systematic review

Author:

Yan Bin123ORCID,Yang Fan123ORCID,Qiu Shi123ORCID,Wang Jin123ORCID,Cai Benxin123ORCID,Wang Sicheng123ORCID,Zaheer Qasim123ORCID,Wang Weidong123ORCID,Chen Yongjun4ORCID,Hu Wenbo56ORCID

Affiliation:

1. School of Civil Engineering, Central South University , Changsha 410075 , China

2. MOE Key Laboratory of Engineering Structures of Heavy-haul Railway, Central South University , Changsha 410075 , China

3. Center for Railway Infrastructure Smart Monitoring and Management, Central South University , Changsha 410075 , China

4. School of Urban Economics and Management, Beijing University of Civil Engineering and Architecture , Beijing 100871, China

5. Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University , Hung Hom, Kowloon, Hong Kong 999077 , China

6. National Rail Transit Electrification and Automation Engineering Technology Research Center (Hong Kong Branch) , The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong 999077 , China

Abstract

Abstract The concept of digital twin (DT) has emerged as a trend in various industries development, enabling the creation of virtual models of physical objects. We conduct a systematic review of the DT technology in the field of transportation infrastructure management from the aspects of concept definition, whole life cycle application, advanced technology, and equipment utilization, as well as the challenges. We begin with an introduction that defines DT and its components, while also distinguishing it from building information modeling and cyber-physical systems. We explore the diverse applications of DT throughout its lifecycle and highlight the significance of DT in structural monitoring, infrastructure operation and maintenance, and dataset expansion. We further investigate the advanced techniques and equipment associated with DT components, focusing on the importance of virtual parts, data acquisition, transmission, multi-source data fusion processing, and data security as well as dynamic updating of models for effective integration and utilization of DT in transportation infrastructure management. We identify key challenges faced by DT in transportation infrastructure management and propose future trends in the study. This comprehensive review serves as a valuable resource for researchers, practitioners, and decision-makers in understanding the potential of DT technology in transportation infrastructure management.

Funder

National Natural Science Foundation of China

The Hong Kong Polytechnic University’s Postdoc Matching Fund Scheme

Publisher

Oxford University Press (OUP)

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