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On Applying Machine Learning and Simulative Approaches to Railway Asset Management: The Earthworks and Track Circuits Case Studies

Author

Listed:
  • Alice Consilvio

    () (Department of Mechanical, Energy, Management and Transportation Engineering, University of Genoa, Via Montallegro 1, 16145 Genoa, Italy)

  • José Solís-Hernández

    () (Centro de Estudios de Materiales y Control de Obra S.A., Calle Benaque 9, 29004 Málaga, Spain)

  • Noemi Jiménez-Redondo

    () (Centro de Estudios de Materiales y Control de Obra S.A., Calle Benaque 9, 29004 Málaga, Spain)

  • Paolo Sanetti

    () (Hitachi Rail STS, Via P. Mantovani 3-5, 16151 Genova, Italy)

  • Federico Papa

    () (Hitachi Rail STS, Via P. Mantovani 3-5, 16151 Genova, Italy)

  • Iñigo Mingolarra-Garaizar

    () (Centro de Estudios de Materiales y Control de Obra S.A., Calle Benaque 9, 29004 Málaga, Spain)

Abstract

The objective of this study is to show the applicability of machine learning and simulative approaches to the development of decision support systems for railway asset management. These techniques are applied within the generic framework developed and tested within the In2Smart project. The framework is composed by different building blocks, in order to show the complete process from data collection and knowledge extraction to the real-world decisions. The application of the framework to two different real-world case studies is described: the first case study deals with strategic earthworks asset management, while the second case study considers the tactical and operational planning of track circuits’ maintenance. Although different methodologies are applied and different planning levels are considered, both the case studies follow the same general framework, demonstrating the generality of the approach. The potentiality of combining machine learning techniques with simulative approaches to replicate real processes is shown, evaluating the key performance indicators employed within the considered asset management process. Finally, the results of the validation are reported as well as the developed human–machine interfaces for output visualization.

Suggested Citation

  • Alice Consilvio & José Solís-Hernández & Noemi Jiménez-Redondo & Paolo Sanetti & Federico Papa & Iñigo Mingolarra-Garaizar, 2020. "On Applying Machine Learning and Simulative Approaches to Railway Asset Management: The Earthworks and Track Circuits Case Studies," Sustainability, MDPI, Open Access Journal, vol. 12(6), pages 1-24, March.
  • Handle: RePEc:gam:jsusta:v:12:y:2020:i:6:p:2544-:d:336274
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    References listed on IDEAS

    as
    1. Kristina Mjörnell & Anna Boss & Markus Lindahl & Stefan Molnar, 2014. "A Tool to Evaluate Different Renovation Alternatives with Regard to Sustainability," Sustainability, MDPI, Open Access Journal, vol. 6(7), pages 1-19, July.
    2. Baldi, Mauro M. & Heinicke, Franziska & Simroth, Axel & Tadei, Roberto, 2016. "New heuristics for the Stochastic Tactical Railway Maintenance Problem," Omega, Elsevier, vol. 63(C), pages 94-102.
    3. Sang-Ho Lee & Sanghoon Park & Jong Myung Kim, 2015. "Suggestion for a Framework for a Sustainable Infrastructure Asset Management Manual in Korea," Sustainability, MDPI, Open Access Journal, vol. 7(11), pages 1-26, November.
    4. Izquierdo, J. & Crespo Márquez, A. & Uribetxebarria, J., 2019. "Dynamic artificial neural network-based reliability considering operational context of assets," Reliability Engineering and System Safety, Elsevier, vol. 188(C), pages 483-493.
    5. Yifan Yang & S. Thomas Ng & Frank J. Xu & Martin Skitmore & Shenghua Zhou, 2019. "Towards Resilient Civil Infrastructure Asset Management: An Information Elicitation and Analytical Framework," Sustainability, MDPI, Open Access Journal, vol. 11(16), pages 1-24, August.
    6. Zhang Chen & Yuanlu Liang & Yangyang Wu & Lijun Sun, 2019. "Research on Comprehensive Multi-Infrastructure Optimization in Transportation Asset Management: The Case of Roads and Bridges," Sustainability, MDPI, Open Access Journal, vol. 11(16), pages 1-12, August.
    7. Damjan Maletič & Matjaž Maletič & Basim Al-Najjar & Boštjan Gomišček, 2018. "Development of a Model Linking Physical Asset Management to Sustainability Performance: An Empirical Research," Sustainability, MDPI, Open Access Journal, vol. 10(12), pages 1-20, December.
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    More about this item

    Keywords

    railway infrastructure; asset management; decision support system; predictive maintenance; data analytics; artificial intelligence;

    JEL classification:

    • Q - Agricultural and Natural Resource Economics; Environmental and Ecological Economics
    • Q0 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - General
    • Q2 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Renewable Resources and Conservation
    • Q3 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Nonrenewable Resources and Conservation
    • Q5 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics
    • Q56 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Environment and Development; Environment and Trade; Sustainability; Environmental Accounts and Accounting; Environmental Equity; Population Growth
    • O13 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Agriculture; Natural Resources; Environment; Other Primary Products

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