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

Alice Consilvio, José Solís-Hernández, Noemi Jiménez-Redondo, Paolo Sanetti, Federico Papa and Iñigo Mingolarra-Garaizar
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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

Sustainability, 2020, vol. 12, issue 6, 1-24

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.

Keywords: railway infrastructure; asset management; decision support system; predictive maintenance; data analytics; artificial intelligence (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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