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Intelligent Assets Life-Cycle Management: Railway and Rolling-Stock Whole-Life Asset Approach for a Future Global Sustainable Transport Model

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Energy Sustainability".

Deadline for manuscript submissions: closed (1 June 2020) | Viewed by 4722

Special Issue Editors

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Guest Editor
Department of Industrial Management, School of Engineering, University of Seville, Camino de los Descubrimientos s/n, 41092 Sevilla, Spain
Interests: intelligent assets management systems; reliability and maintenance engineering and management; advance optimization techniques
Special Issues, Collections and Topics in MDPI journals

Guest Editor
Department of Industrial Management at Universidad de Sevilla and member of the SIM research group of this University
Interests: Asset and maintenance management; Condition Based Maintenance Management; prognosis and health management

Special Issue Information

Dear Colleagues,


The recent report “Rail 2050 Vision”, (The European Railway Technology Platform, 2017) concludes that the railway is the cornerstone of future global European sustainable transport models. In addition, it identifies “Intelligent Assets Life-Cycle Management: Whole-Life Asset Approach” as a key research line, and a “Zero Casualties” scenario, directly linked with predictive maintenance development, as a main objective of the 2050 railway vision.

The Shift2Rail JU multi-annual action plan (S2R JU, 2018) has given high priority to the development of reliability and lifetime predictions, as well as to real-time diagnosis and the predictive maintenance of critical components. These developments aim to both increase the capacity and service quality of assets (through the reduction of failure-based disturbances in operations), and to reduce their life-cycle cost, while increasing their lifetime.

Asset Management has been introduced as a principal requirement of Safety Regulations (CSM on Safety Management Systems Requirements (EU), 2018/762) and is expected to gain more and more importance in future scenarios in the railway sector. In these scenarios, the integration of different emerging technologies is expected. BIM tools will serve as a work and management interface throughout the life cycle of the assets; IoT networks will allow for interaction between digital assets and those with human resources; Artificial Intelligence will be used in predictive and optimization models for digital decision-making; and the introduction of robotic elements is expected to provide a degree of self-maintenance.

Objective of the Special Issue

This Special Issue seeks to address the development of new methods and tools that will integrate the future vision of intelligent assets life-cycle management with the management of safety requirements within the railway sector. It aims to be applied to both trains and railway infrastructure maintenance, and to asset management.

Target Audience

Engineers, academicians, researchers, advanced-level students (both postgraduate and doctoral), technology-developers, and managers who make decisions in this field will find this text useful in furthering their research into pertinent topics in asset management. Specific international interest groups for this Special Issue are as follows:

Recommended topics include, but are not limited to, the following:

Contributors are welcome to submit papers on the following topics, relating to the development of intelligent asset management platforms and their implementation in industrial environments, for the improvement of energy production, storage and transportation asset management:

  • Assets’ life cycle and eco-efficiency
  • Review of intelligent asset management platforms
  • Emerging technologies and their implications in asset management
  • Emerging asset management techniques and processes
  • Case studies on digital asset management, and their implementations
  • Advanced intelligent asset management systems for conventional and high-speed trains, signaling, rail networks, etc.
  • Railway and rolling stock safety and sustainability
  • Intelligent asset management platforms
  • Asset management and resilience modelling
  • Adapting critical infrastructure asset management to climate change
  • Safety culture and asset management
  • Predictive maintenance solutions and use cases
  • Complex assets’ systems’ representation models
  • IoT platform, as applied to railway systems, and system integration strategies
  • BIM for railway infrastructure asset management
  • Self-maintenance solutions

This Special Issue also intends to provide a practical perspective, so case studies based in real contexts will be especially welcomed.

Prof. Dr. Adolfo Crespo Márquez
Dr. Antonio J. Guillén López
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (1 paper)

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24 pages, 3375 KiB  
On Applying Machine Learning and Simulative Approaches to Railway Asset Management: The Earthworks and Track Circuits Case Studies
by Alice Consilvio, José Solís-Hernández, Noemi Jiménez-Redondo, Paolo Sanetti, Federico Papa and Iñigo Mingolarra-Garaizar
Sustainability 2020, 12(6), 2544; - 24 Mar 2020
Cited by 18 | Viewed by 4052
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 [...] Read more.
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. Full article
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