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).
References
Adibfar, A., and A. M. Costin. 2022. “Creation of a mock-up bridge digital twin by fusing intelligent transportation systems (ITS) data into bridge information model (BrIM).” J. Constr. Eng. Manage. 148 (9): 04022094. https://doi.org/10.1061/(ASCE)CO.1943-7862.0002332.
Bartonek, D., J. Bures, O. Vystavel, and R. Havlicek. 2023. “Case study of remodelling the as-built documentation of a railway construction into the BIM and GIS environment.” Appl. Sci. 13 (9): 5591. https://doi.org/10.3390/app13095591.
Becerik-Gerber, B., F. Jazizadeh, N. Li, and G. Calis. 2012. “Application areas and data requirements for BIM-enabled facilities management.” J. Constr. Eng. Manage. 138 (3): 431–442. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000433.
Bergquist, B., and P. Söderholm. 2015. “Data analysis for condition-based railway infrastructure maintenance.” Qual. Reliab. Eng. Int. 31 (5): 773–781. https://doi.org/10.1002/qre.1634.
Boddupalli, C., A. Sadhu, E. Rezazadeh Azar, and S. Pattyson. 2019. “Improved visualization of infrastructure monitoring data using building information modeling.” Struct. Infrastruct. Eng. 15 (9): 1247–1263. https://doi.org/10.1080/15732479.2019.1602150.
Bosurgi, G., O. Pellegrino, and G. Sollazzo. 2022. “Pavement condition information modelling in an I-BIM environment.” Int. J. Pavement Eng. 23 (13): 4803–4818. https://doi.org/10.1080/10298436.2021.1978442.
Caldera, S., S. Mostafa, C. Desha, and S. Mohamed. 2021. “Exploring the role of digital infrastructure asset management tools for resilient linear infrastructure outcomes in cities and towns: A systematic literature review.” Sustainability 13 (21): 11965. https://doi.org/10.3390/su132111965.
Cheng, J. C. P., Q. Lu, and Y. Deng. 2016. “Analytical review and evaluation of civil information modeling.” Autom. Constr. 67 (4): 31–47. https://doi.org/10.1016/j.autcon.2016.02.006.
Cheng, Y., W. Qiu, and D. Duan. 2019. “Automatic creation of as-is building information model from single-track railway tunnel point clouds.” Autom. Constr. 106 (Oct): 102911. https://doi.org/10.1016/j.autcon.2019.102911.
Costin, A., A. Adibfar, H. Hu, and S. S. Chen. 2018. “Building information modeling (BIM) for transportation infrastructure—Literature review, applications, challenges, and recommendations.” Autom. Constr. 106 (8): 102911. https://doi.org/10.1016/j.autcon.2019.102911.
Dell’Acqua, G., S. G. De Oliveira, and S. A. Biancardo. 2018. “Railway-BIM: Analytical review, data standard and overall perspective.” Ing. Ferrov. 73 (11): 901–923.
EU (European Union). 2014. Directive 2014/24/EU of the European parliament and of the council of 26 February 2014 on public procurement and repealing directive 2004/18/EC. Luxembourg, Luxembourg: Publications Office of the European Union.
Fabozzi, S., S. A. Biancardo, R. Veropalumbo, and E. Bilotta. 2021. “I-BIM based approach for geotechnical and numerical modelling of a conventional tunnel excavation.” Tunnelling Underground Space Technol. 108 (Jun): 103723. https://doi.org/10.1016/j.tust.2020.103723.
Garramone, M., and N. Scaioni. 2022. “Ifalignent for raster-to-vector GIS Railway centreline: A case study in the south of Italy.” Int. Ann. Photogramm. Remote Sens. Spatial Inf. Sci. XLIII-B4-2022 (Jun): 39–45. https://doi.org/10.5194/isprs-archives-XLIII-B4-2022-39-2022.
Garramone, M., E. Tonelli, and M. Scaioni. 2022. “A multi-scale BIM/GIS framework for railways asset management.” Int. Photogramm. Remote Sens. Spatial Inf. Sci. 5 (Jun): 95–102. https://doi.org/10.5194/isprs-archives-XLVI-5-W1-2022-95-2022.
Girardet, A., and C. Boton. 2021. “A parametric BIM approach to foster bridge project design and analysis.” Autom. Constr. 126 (4): 103679. https://doi.org/10.1016/j.autcon.2021.103679.
Gouda Mohamed, A., M. R. Abdallah, and M. Marzouk. 2020. “BIM and semantic web-based maintenance information for existing buildings.” Autom. Constr. 116 (5): 103209. https://doi.org/10.1016/j.autcon.2020.103209.
Gragnaniello, C., G. Mariniello, T. Pastore, and D. Asprone. 2024. “BIM-based design and setup of structural health monitoring systems.” Autom. Constr. 158 (Feb): 105245. https://doi.org/10.1016/j.autcon.2023.105245.
Hagedorn, P., L. Liu, M. König, R. Hajdin, T. Blumenfeld, M. Stöckner, M. Billmaier, K. Grossauer, and K. Gavin. 2023. “BIM-enabled infrastructure asset management using information containers and semantic web.” J. Comput. Civ. Eng. 37 (1): 04022041. https://doi.org/10.1061/(ASCE)CP.1943-5487.0001051.
Heaton, J., A. K. Parlikad, and J. Schooling. 2019. “Design and development of BIM models to support operations and maintenance.” Comput. Ind. 111 (Oct): 172–186. https://doi.org/10.1016/j.compind.2019.08.001.
Jiang, F., L. Ma, T. Broyd, K. Chen, H. Luo, and M. Du. 2022b. “Building demolition estimation in urban road widening projects using as-is BIM models.” Autom. Constr. 144 (Dec): 104601. https://doi.org/10.1016/j.autcon.2022.104601.
Jiang, F., L. Ma, T. Broyd, W. Chen, and H. Luo. 2022a. “Building digital twins of existing highways using map data based on engineering expertise.” Autom. Constr. 134 (Feb): 104081. https://doi.org/10.1016/j.autcon.2021.104081.
Jovanovic, S. 2019. “Railway infrastructure asset management system (RI AMS) in the West Balkans Region (WBR).” Accessed December 1, 2019. https://www.transport-community.org/wp-content/uploads/2019/12/Railway-Infrastructure-Asset-Management-System-RI-AMS-CONNECTA.pdf.
Justo, A., M. Soilán, A. Sánchez-Rodríguez, and B. Riveiro. 2021. “Scan-to-BIM for the infrastructure domain: Generation of IFC-compliant models of road infrastructure assets and semantics using 3D point cloud data.” Autom. Constr. 127 (Jul): 103703. https://doi.org/10.1016/j.autcon.2021.103703.
Kremer, J., and A. Grimm. 2018. “The Railmapper—A dedicated mobile lidar mapping system for railway networks.” Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci. 39 (Jul): 477–482. https://doi.org/10.5194/isprsarchives-XXXIX-B5-477-2012.
Kwon, T. H., S. I. Park, Y. Jang, and S. Lee. 2020. “Design of railway track model with three-dimensional alignment based on extended industry foundation classes.” Appl. Sci. 10 (10): 3649. https://doi.org/10.3390/app10103649.
Macário, R., and C. Marques. 2004. “Information systems for railway infrastructure management.” In Proc., 9th Int. Conf. on Computer-Aided Design, Manufacture and Operation in the Railway and Other Advanced Transit Systems. Ashurst, England: WIT Press.
MIT (Ministero delle Infrastrutture e dei Trasporti). 2017. Modalità e i tempi di progressiva introduzione dei metodi e degli strumenti elettronici di modellazione per l’edilizia e le infrastrutture. DM 560/2017. Rome: MIT.
Neves, J., Z. Sampaio, and M. Vilela. 2019. “A case study of BIM implementation in rail track rehabilitation.” Infrastructures 4 (1): 8. https://doi.org/10.3390/infrastructures4010008.
Panah, R. S., and M. Kioumarsi. 2021. “Application of building information modelling (BIM) in the health monitoring and maintenance process: A systematic review.” Sensors 21 (3): 837. https://doi.org/10.3390/s21030837.
Poku-Agyemang, K. N., and A. Reiterer. 2023. “3D reconstruction from 2D plans exemplified by bridge structures.” Remote Sens. 15 (3): 677. https://doi.org/10.3390/rs15030677.
Qingchao, W. 2016. Design of railway line. Beijing: China Railway Publishing House.
Sadhu, A., J. E. Peplinski, A. Mohammadkhorasani, and F. Moreu. 2023. “A review of data management and visualization techniques for structural health monitoring using BIM and virtual or augmented reality.” J. Struct. Eng. 149 (1): 03122006. https://doi.org/10.1061/(ASCE)ST.1943-541X.0003498.
Shekargoftar, A., H. Taghaddos, A. Azodi, A. Nekouvaght Tak, and K. Ghorab. 2022. “An integrated framework for operation and maintenance of gas utility pipeline using BIM, GIS, and AR.” J. Perform. Constr. Facil. 36 (3): 04022023. https://doi.org/10.1061/(ASCE)CF.1943-5509.0001722.
Soilán, M., A. Nóvoa, A. Sánchez-Rodríguez, A. Justo, and B. Riveiro. 2021. “Fully automated methodology for the delineation of railway lanes and the generation of IFC alignment models using 3D point cloud data.” Autom. Constr. 126 (4): 103684. https://doi.org/10.1016/j.autcon.2021.103684.
Sollazzo, G., K. C. P. Wang, G. Bosurgi, and J. Q. Li. 2016. “Hybrid procedure for automated detection of cracking with 3D pavement data.” J. Comput. Civ. Eng. 30 (6): 04016032. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000597.
Sresakoolchai, J., and S. Kaewunruen. 2021. “Integration of building information modeling and machine learning for railway defect localization.” IEEE Access 9 (3): 166039–166047. https://doi.org/10.1109/ACCESS.2021.3135451.
Sresakoolchai, J., and S. Kaewunruen. 2022. “Track geometry prediction using three-dimensional recurrent neural network-based models cross-functionally co-simulated with BIM.” Sensors 23 (1): 391. https://doi.org/10.3390/s23010391.
Steyn, W. J., and A. Broekman. 2022. “Development of a digital twin of a local road network: A case study.” J. Test. Eval. 50 (6): 2901–2915. https://doi.org/10.1520/JTE20210043.
Tang, F., T. Ma, Y. Guan, and Z. Zhang. 2020. “Parametric modeling and structure verification of asphalt pavement based on BIM-ABAQUS.” Autom. Constr. 111 (Aug): 103066. https://doi.org/10.1016/j.autcon.2019.103066.
Tang, L., C. Chen, H. Li, and D. Y. Y. Mak. 2022. “Developing a BIM GIS–integrated method for urban underground piping management in China: A case study.” J. Constr. Eng. Manage. 148 (9): 05022004. https://doi.org/10.1061/(ASCE)CO.1943-7862.0002323.
Tongjun, W. 2021. “Research on life-cycle management framework of intelligent high-speed railway infrastructure.” J. China Rail. Soc. 43 (11): 1–7. https://doi.org/10.3969/j.issn.1001-8360.2021.11.001.
Vignali, V., E. M. Acerra, C. Lantieri, F. Di Vincenzo, G. Piacentini, and S. Pancaldi. 2021. “Building information modelling (BIM) application for an existing road infrastructure.” Autom. Constr. 128 (Aug): 103752. https://doi.org/10.1016/j.autcon.2021.103752.
Volk, R., J. Stengel, and F. Schultmann. 2014. “Building information modeling (BIM) for existing buildings—Literature review and future needs.” Autom. Constr. 128 (Mar): 103752. https://doi.org/10.1016/j.autcon.2021.103752.
Yang, B., B. Liu, D. Zhu, B. Zhang, Z. Wang, and K. Lei. 2020. “Semiautomatic structural BIM-model generation methodology using CAD construction drawings.” J. Comput. Civ. Eng. 34 (3): 04020006. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000885.
Yu, G., Z. Mao, M. Hu, Z. Li, and V. Sugumaran. 2019. “BIM+ topology diagram-driven multiutility tunnel emergency response method.” J. Comput. Civ. Eng. 33 (6): 04019038. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000851.
Zhang, G., P. A. Vela, P. Karasev, and I. Brilakis. 2015. “A sparsity-inducing optimization-based algorithm for planar patches extraction from noisy point-cloud data.” Comput.-Aided Civ. Infrastruct. Eng. 30 (2): 85–102. https://doi.org/10.1111/mice.12063.
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© 2024 American Society of Civil Engineers.
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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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