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Journal of Graphics ›› 2024, Vol. 45 ›› Issue (3): 601-612.DOI: 10.11996/JG.j.2095-302X.2024030601

• BIM/CIM • Previous Articles     Next Articles

An intelligent railway operation and maintenance management approach based on BIM and semantic web

HE Qing1,2(), JING Chuanyu1,2, SUN Huakun1,2, YAO Li3, XU Jingmang1,2, WANG Ping1,2()   

  1. 1. Southwest Jiaotong University, Key Laboratory of High-speed Railway Engineering, Chengdu Sichuan 610031, China
    2. School of Civil Engineering, Southwest Jiaotong University, Chengdu Sichuan 610031, China
    3. China Railway Eryuan Engineering Group CO., LTD., Chengdu Sichuan 610031, China
  • Received:2023-11-08 Accepted:2024-02-27 Online:2024-06-30 Published:2024-06-12
  • Contact: WANG Ping (1969-), professor, Ph.D. His main research interests cover design theory, method and evaluation technology of high speed railway turnout, track irregularity and dynamics. E-mail:wping@home.swjtu.edu.cn
  • About author:

    HE Qing (1982-), professor, Ph.D. His main research interests cover highway, railway intelligent route selection and BIM, traffic big data, analysis and management of track irregularity, rail damage detection and analysis research. E-mail:qhe@swjtu.edu.cn

  • Supported by:
    Key Projects of National Natural Science Foundation of China High-speed Rail Joint Fund(U1934214);General Project of National Natural Science Foundation of China(52372400);Sichuan Provincial Natural Science Foundation Innovation Research Group Project(2023NSFSC1975);Shandong Provincial Department of Transportation Science and Technology Project(2022B30)

Abstract:

The building information modeling (BIM) technology plays a crucial role in enhancing the efficiency of railway operation and maintenance management. However, the heterogeneity of data generated from various inspection and maintenance activities, coupled with the complex spatiotemporal relationships, hinder the process of BIM data interpretation and integration. To address this challenge, a railway maintenance ontology (TOMO) based on the industry foundation classes (IFC) and semantic Web technology was developed. TOMO served three main functions: ① Simplifying BIM model information based on railway maintenance lifecycle requirements. ② Introducing mapping rules and establishing data extraction and transformation modules to integrate heterogeneous data from multiple sources, structurally defining complex spatiotemporal relationships between data. ③ Combining data-driven techniques to study intelligent optimization methods for railway fine-tuning, providing flexible decision support. Finally, using static inspection data from a high-speed railway as an example, the effectiveness and practicality of this framework were verified. This framework held practical engineering significance in promoting data interoperability in the field, reducing the labor intensity of maintenance personnel, and enhancing the intelligence of maintenance management.

Key words: building information modeling, operation and maintenance management, semantic web technology, data-driven, flexible decision

CLC Number: