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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, 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

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    Cited by:

    1. Sedghi, Mahdieh & Kauppila, Osmo & Bergquist, Bjarne & Vanhatalo, Erik & Kulahci, Murat, 2021. "A taxonomy of railway track maintenance planning and scheduling: A review and research trends," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    2. Ágota Bányai & Tamás Bányai, 2022. "Real-Time Maintenance Policy Optimization in Manufacturing Systems: An Energy Efficiency and Emission-Based Approach," Sustainability, MDPI, vol. 14(17), pages 1-15, August.
    3. Rocio de la Torre & Canan G. Corlu & Javier Faulin & Bhakti S. Onggo & Angel A. Juan, 2021. "Simulation, Optimization, and Machine Learning in Sustainable Transportation Systems: Models and Applications," Sustainability, MDPI, vol. 13(3), pages 1-21, February.

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