Special Issue "Rail Infrastructures"

A special issue of Infrastructures (ISSN 2412-3811).

Deadline for manuscript submissions: closed (30 June 2021) | Viewed by 3833

Special Issue Editors

Prof. Dr. Alireza Ahmadi
E-Mail Website
Guest Editor
Luleå tekniska Universitet, Lulea, Sweden
Interests: railway track geometry
Dr. Iman Soleimanmeigouni
E-Mail Website
Co-Guest Editor
Luleå tekniska Universitet, Lulea, Sweden
Interests: railway infrastructure; transportation; operation and maintenance engineering
Prof. Dr. Francesco Flammini
E-Mail Website
Guest Editor
School of Engineering, Design, and Technology, Division of Product Realization, Mälardalen University, 72123 Eskilstuna, Sweden
Interests: resilient cyber-physical systems; homeland security; performability modeling; safe autonomy; intelligent transportation
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Special Issue Information

Dear Colleagues,

Railway Transport infrastructures are one of the most important factors for a country's progress, and is considered as the “lifeline” of a nation, and is proven as a key element for adding speed and efficiency to a country's progress. Railways are currently experiencing higher demands on infrastructure performance, capacity and service quality. As a result, higher level of resilience against failure, robustness and availability at reduced cost are expected, to meet the planned capacity utilization and safety criteria.

Track is the fundamental part of railway infrastructure and represents a significant part of maintenance effort and cost. Track system (e.g. rails, wheels, switches and crossings) degrades with age and usage; and loses its functionality over time. Poor quality of track, may result in e.g. safety problems, speed reduction, traffic disruption, greater maintenance cost.

Hence, it is of outmost importance to develop methodologies and tools for accurate prediction of track condition, as well as selection and implementation of an effective health management strategy.

The aim of this special issue is to publish state-of-the-art research papers focused on the topics associated with data quality, fault diagnosis, prognosis and health management, as well as Life cycle assessment, maintenance planning and scheduling of railway track system.

Analytical models, empirical studies, and case studies are all welcomed as long as an article provides new insights and implications to the field of Railway track life cycle and health management.

Prof. Dr. Alireza Ahmadi
Dr. Iman Soleimanmeigouni
Prof. Dr. Francesco Flammini
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com 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. Infrastructures is an international peer-reviewed open access monthly 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 1600 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.

Keywords

  • Railway track
  • Machine learning
  • Predictive analytics
  • Data quality
  • Fault diagnosis, prognosis and health management
  • Life cycle assessment
  • Maintenance planning and scheduling

Published Papers (2 papers)

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Research

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Article
Evaluation of Numerical Simulation Approaches for Simulating Train–Track Interactions and Predicting Rail Damage in Railway Switches and Crossings (S&Cs)
Infrastructures 2021, 6(5), 63; https://doi.org/10.3390/infrastructures6050063 - 22 Apr 2021
Cited by 5 | Viewed by 1898
Abstract
Switch and crossing (S&C) faults are a major cause of track-related delays and account for a significant proportion of maintenance and renewal budgets for railway infrastructure managers around the world. Although various modelling approaches have been proposed in the literature for the simulation [...] Read more.
Switch and crossing (S&C) faults are a major cause of track-related delays and account for a significant proportion of maintenance and renewal budgets for railway infrastructure managers around the world. Although various modelling approaches have been proposed in the literature for the simulation of vehicle–track dynamic interaction, wheel–rail contact and damage prediction, there is a lack of evaluation for combining these approaches to effectively predict the failure mechanism. An evaluation of S&C modelling approaches has therefore been performed in this article to justify their selection for the research interests of predicting the most dominant failure mechanisms of wear, rolling contact fatigue (RCF) and plastic deformation in S&C rails by recognising the factors that influence the accuracy and efficiency of the proposed modelling approaches. A detailed discussion of the important modelling aspects has been carried out by considering the effectiveness of each individual approach and the combination of different approaches, along with a suggestion of appropriate modelling approaches for predicting the dominant failure mechanisms. Full article
(This article belongs to the Special Issue Rail Infrastructures)
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Review

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Review
A Systematic Review of Artificial Intelligence Public Datasets for Railway Applications
Infrastructures 2021, 6(10), 136; https://doi.org/10.3390/infrastructures6100136 - 22 Sep 2021
Cited by 6 | Viewed by 1365
Abstract
The aim of this paper is to review existing publicly available and open artificial intelligence (AI) oriented datasets in different domains and subdomains of the railway sector. The contribution of this paper is an overview of AI-oriented railway data published under Creative Commons [...] Read more.
The aim of this paper is to review existing publicly available and open artificial intelligence (AI) oriented datasets in different domains and subdomains of the railway sector. The contribution of this paper is an overview of AI-oriented railway data published under Creative Commons (CC) or any other copyright type that entails public availability and freedom of use. These data are of great value for open research and publications related to the application of AI in the railway sector. This paper includes insights on the public railway data: we distinguish different subdomains, including maintenance and inspection, traffic planning and management, safety and security and type of data including numerical, string, image and other. The datasets reviewed cover the last three decades, from January 1990 to January 2021. The study revealed that the number of open datasets is very small in comparison with the available literature related to AI applications in the railway industry. Another shortcoming is the lack of documentation and metadata on public datasets, including information related to missing data, collection schemes and other limitations. This study also presents quantitative data, such as the number of available open datasets divided by railway application, type of data and year of publication. This review also reveals that there are openly available APIs—maintained by government organizations and train operating companies (TOCs)—that can be of great use for data harvesting and can facilitate the creation of large public datasets. These data are usually well-curated real-time data that can greatly contribute to the accuracy of AI models. Furthermore, we conclude that the extension of AI applications in the railway sector merits a centralized hub for publicly available datasets and open APIs. Full article
(This article belongs to the Special Issue Rail Infrastructures)
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