Technical Papers
Jul 27, 2024

Semiautomated Railway Line Information Modeling Based on Asset Management Data

Publication: Journal of Construction Engineering and Management
Volume 150, Issue 10

Abstract

The operation and maintenance (O&M) period is a critical and resource-intensive phase of the life cycle of railway line infrastructure. The O&M of a railway line during this period requires a large amount of investment and labor. Thus, there is an urgent need for railway managers to seek more efficient management tools and methods to improve management efficiency and reduce maintenance costs. Infrastructure–building information model (I-BIM) technology, characterized by its intuitive representation and high information-integration capabilities, has gained considerable traction with infrastructure industry researchers. In the context of existing railway lines, the absence of corresponding I-BIMs or similar technology owing to the immaturity of technical development at the time led to the use of manual modeling processes, which have limitations such as high cost, slow speed, and low efficiency. This paper presents an innovative, high-speed, semiautomated method for generating I-BIMs for existing railway lines and leveraging asset management data without incurring additional data collection costs. This approach leverages key modules such as line trajectory computation, element model generation, the linking of O&M semantic information, and the visualization of asset health indexes, which can enable the swift and semiautomated creation of an O&M-oriented I-BIM for railway lines. This process elevates traditional two-dimensional database tables into more intuitive, operation-oriented, and semantically rich models. This results in the provision of more efficient support for maintenance management decisions related to railway lines. The proposed method addresses the limitations of manual modeling processes and can pave the way for enhanced O&M efficiency in the railway infrastructure sector.

Practical Applications

The absence of BIMs during the design and construction phase of existing railway lines, coupled with the high costs and error rates associated with manual modeling, constitute substantial barriers to the implementation of BIMs in operation and management. By leveraging the proposed semiautomated modeling approach, which relies on management data, site managers and researchers can rapidly establish basic I-BIMs. Although the asset ledgers and station equipment plans used in this approach originate from National Railway Administration, similar data sets should serve as fundamental inputs for railway line operation and maintenance worldwide. By deploying this method, field managers can enhance traditional two-dimensional (2D) asset ledgers into a three-dimensional (3D) I-BIM, thereby offering a more intuitive understanding of the assets within their jurisdiction. Owing to the integration of geographic information system maps into the model, personnel can directly query asset-related geographic information, potentially reducing the frequency and cost of off-site surveys. For researchers, using this method to establish a real-world model rapidly offers convenient access for further BIM-based applications, such as the integration of real-time data from on-site sensors to formulate digital twins.

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Data Availability Statement

All data used in the study are confidential in nature and may only be provided with restrictions. The railway line equipment ledgers and station equipment plans used in this study are private data belonging to the China Railway Administration, and the authors do not have the right to disclose them publicly.

Acknowledgments

This work was supported by Fundamental Research Funds for Central Universities (Grant No. 2023JBMC007) and the National Natural Science Foundation of China (Grant Nos. 72371018 and 62132003).

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Go to Journal of Construction Engineering and Management
Journal of Construction Engineering and Management
Volume 150Issue 10October 2024

History

Received: Jan 10, 2024
Accepted: May 13, 2024
Published online: Jul 27, 2024
Published in print: Oct 1, 2024
Discussion open until: Dec 27, 2024

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Master’s Student, School of Traffic and Transportation, Beijing Jiaotong Univ., Beijing 100044, China. Email: [email protected]
Associate Professor, Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Beijing Jiaotong Univ., Beijing 100044, China (corresponding author). ORCID: https://orcid.org/0000-0002-3625-1333. Email: [email protected]
Zhiming Zheng [email protected]
Researcher, Beijing International Science and Technology Cooperation Center, Beijing Hong Kong Macao Taiwan Science and Technology Cooperation Center, Beijing 100080, China. Email: [email protected]
Ziteng Wang [email protected]
Assistant Researcher, Locomotive & Car Research Institute, China Academy of Railway Sciences Corporation Limited, Beijing 100044, China. Email: [email protected]
Yong Zhuang [email protected]
Ph.D. Student, School of Traffic and Transportation, Beijing Jiaotong Univ., Beijing 100044, China. Email: [email protected]

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