Digitalization as an Enabler in Railway Maintenance: A Review from “The International Union of Railways Asset Management Framework” Perspective

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

This paper reviews the role of digitalization in railway maintenance management through the lens of the UIC Asset Management Framework. It examines how technologies like Big Data, IoT, and AI can enhance maintenance practices and decision-making. Using bibliometric analysis, the study identifies trends and gaps in integrating digital tools into maintenance frameworks. It highlights the need for a more integrated approach aligned with railway organizations' strategic goals. The paper also proposes future research directions, emphasizing the development of a global framework that combines technological advancements with organizational change to achieve sustainable and safe railway operations. Before the final publication, the following issues should be addressed.

1. The paper broadly discusses digitalization but could benefit from a more precise definition of what constitutes digitalization in the context of railway maintenance. For example, distinguishing between emerging technologies (e.g., AI, IoT) and established digital practices (e.g., basic data analytics) would help readers better understand the scope and impact.

2. Given the increasing reliance on digital technologies, data privacy and cybersecurity are critical concerns. The paper should include a dedicated section discussing how railway organizations can address these challenges, especially with the integration of IoT and cloud computing.

3. The literature review can further be improved. It could benifit from including the recent literatures on railway maintenance, e.g., SHM deformation monitoring for high-speed rail track slabs and Bayesian change point detection for the measurements, SHM deformation monitoring for high-speed rail track slabs and Bayesian change point detection for the measurements, Measurement and Forecasting of High-Speed Rail Track Slab Deformation under Uncertain SHM Data Using Variational Heteroscedastic Gaussian Process. 

4. The paper focuses on the potential benefits of digitalization but does not provide a cost-benefit analysis. Including an evaluation of the economic feasibility of implementing these technologies would help railway organizations make informed decisions about digital investments.

5. The paper proposes future research directions but could be more specific about the methodologies and potential outcomes. For example, suggesting collaborative projects between academia and industry or highlighting specific gaps that need to be addressed would provide clearer guidance for future studies.

Comments on the Quality of English Language

Minor revision for English language.

Author Response

1. The paper broadly discusses digitalization but could benefit from a more precise definition of what constitutes digitalization in the context of railway maintenance.

Response: We have included a precise definition of digitalization in the introduction (Section 1, page 2), distinguishing between established digital practices (e.g., condition-based maintenance, basic data collection and analytics) and emerging technologies (e.g., IoT, AI, digital twins). This clarification provides readers with a structured understanding of the scope and impact of digitalization in railway maintenance.

2. Include a dedicated section on data privacy and cybersecurity.

Response: A new dedicated subsection has been added (Section 4.2, titled Data Privacy and Cybersecurity in Railway Digitalization). This section discusses critical cybersecurity risks, data privacy concerns, and mitigation strategies relevant to IoT and cloud-based systems in railway environments. Additionally, Section 4.2.1 incorporates regulatory frameworks (GDPR, ISO 27001, IEC 62443) and references recent studies such as Kasraei et al. (2024), providing a robust discussion on cybersecurity and risk mitigation strategies in railway digital environments

 

3. Improve the literature review by including recent work on SHM deformation monitoring and Bayesian change point detection.

Response: We have expanded the literature review (Section 2.3.1) by integrating recent references focused on deformation monitoring in high-speed railway infrastructures, Bayesian change point detection, and applications of Variational Heteroscedastic Gaussian Process (VHGP) models in SHM. These additions are explicitly cited and discussed between pages 10–12.

4. Add a cost-benefit analysis to support the feasibility of digital technologies.

Response: A new section (4.3, Cost-Benefit Analysis of Digitalization in Railway Maintenance) has been incorporated. It presents an evidence-based discussion on the economic feasibility of digital technologies, referencing validated case studies and aligning these benefits with the structured approach recommended by the UIC Asset Management Framework.

5. Be more specific in future research directions, suggesting collaborative projects and clear expected outcomes.

Response: In the conclusions chapter (final section, pages 28–29), we have extended the discussion on future research directions, specifying methodologies, expected deliverables, and the need for collaborative projects between academia, industry stakeholders, and railway operators. A structured framework is also presented for future research development.

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

This paper provides a review of how current digital technologies—such as big data, the Internet of Things (IoT), and artificial intelligence (AI)—are applied to railway maintenance management, highlighting their potential to enhance operational efficiency and decision-making. By integrating the UIC asset management framework, the authors illustrate the potential of digitization in risk prediction, resource scheduling, and organizational transformation. They also reference recent studies (see 10.1016/j.ress.2024.110596, 10.1016/j.ress.2024.110493) that underscore the importance of data-driven decision-making and predictive maintenance. Overall, this research is comprehensive and forward-looking, but it can still be improved in the following ways:

It is recommended to make a more direct comparison with the latest research findings, thereby clarifying the paper’s unique contributions in terms of digital maintenance management models or risk prediction frameworks.

Please include concise real-world examples or case studies demonstrating how digital technologies have been effectively implemented, thereby confirming their feasibility in various railway assets and operational scenarios.

In addition to technical aspects, the paper should focus more on employee training, cross-departmental collaboration, and other human factors, emphasizing how organizational change and digital technology adoption can mutually reinforce each other.

Comments on the Quality of English Language

The manuscript is generally clear and understandable; however, there are occasional grammatical errors and awkward phrasing that slightly affect readability. I suggest a careful proofreading or a professional editing service to further improve the overall quality of the English language.

Author Response

1. Make a more direct comparison with recent research to clarify the unique contributions of the paper.

Response: The discussion section (Section 4.1, pages 19–20) now includes an explicit comparative analysis of our framework versus the latest research findings. We highlight how previous studies address isolated technological aspects, while our contribution integrates these technologies within a structured management framework aligned with UIC guidelines and validated through practical application.

2. Include concise real-world examples or case studies demonstrating the effective implementation of digital technologies.

Response: A dedicated case study has been incorporated (Section 4.4, Implementation of Digital Twin Technology: The AZVI Case). This section describes a real-world application supported by publications presented at AMEST 2024, demonstrating the feasibility and measurable impact of digitalization in railway maintenance environments. The AZVI case study has been enriched with quantitative results demonstrating a 25% reduction in unplanned downtime, a 20% reduction in corrective maintenance costs, and a 15% increase in asset useful life, confirming the practical impact of the proposed digital framework

3. Emphasize employee training, cross-departmental collaboration, and human factors in organizational change.

Response: A new subsection (Section 4.5, Human and Organizational Factors in Digital Adoption) has been added. It discusses workforce training requirements, the importance of cross-departmental alignment, and leadership involvement to ensure successful digital transformation processes. Section 4.5 now integrates the Industry 5.0 vision (European Commission, 2021), emphasizing human-centric, resilient, and sustainable approaches in digital transformation.

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

This paper reviews the role of digitalization in railway maintenance management through the lens of the UIC Asset Management Framework. It examines how technologies like Big Data, IoT, and AI can enhance maintenance practices. The study identifies current trends, challenges, and gaps in integrating digital tools into maintenance frameworks and suggests future research directions for sustainable railway operations. Before the final pulication, the following issues need further be improved.

1. The paper could improve its organization by consolidating the discussion of digitalization technologies and their applications into a dedicated section. Currently, these topics are scattered across different parts of the paper, making it difficult for readers to grasp the full scope of digitalization's impact. A cohesive section would allow for a more systematic comparison of technologies like Big Data, IoT, and AI, and their specific contributions to railway maintenance management. This would enhance clarity and provide a stronger foundation for the subsequent analysis and recommendations.
2.  The study aims to assess how digital technologies such as Big Data, the Internet of Things (IoT), and Artificial Intelligence (AI) serve as enablers for more efficient and effective maintenance practices in the railway sector. The review part on IoT technology should be supplemented, especially RFID technlology as an important part for IoT, which can refer to “Towards long-transmission-distance and semi-active wireless strain sensing enabled by dual-interrogation-mode RFID technology“, ”Review of wireless RFID strain sensing technology in structural health monitoring“, ”A concise state-of-the-art review of crack monitoring enabled by RFID technology.”
3. The paper can benefit from a more in-depth analysis of the specific challenges and solutions related to data privacy and cybersecurity in railway digitalization. While the study mentions these issues, a detailed examination of potential vulnerabilities, regulatory compliance, and best practices for securing digital railway systems would provide valuable insights for practical implementation.
4. The paper can further benefit from enhancing the resolution and clarity of its figures and diagrams. For instance, Figure 4 and Figure 5 appear somewhat pixelated and difficult to read. Using vector graphics or higher-resolution images would improve the visual quality and make it easier for readers to understand the co-word analysis and time evolution results.

Author Response

Dear Editors,

We are pleased to resubmit the second revised version of our manuscript titled "Digitalization as an Enabler in Railway Maintenance: A Review from the UIC Asset Management Framework Perspective" for consideration in the Special Issue “The Resilience of Railway Networks: Enhancing Safety and Robustness” of Infrastructures. This article is a comprehensive review that combines bibliometric analysis with a structured evaluation of the UIC Asset Management Framework. It identifies critical research gaps and proposes an integrated approach to leveraging digital technologies—including IoT, Big Data, AI, and digital twins—to support smarter, safer, and more sustainable maintenance strategies in the railway sector. Following the valuable feedback received during the second review round, we have implemented the following targeted revisions to enhance the clarity, depth, and structure of the paper:

Responses to Reviewer 1 – Second Round Comments:

1. Request to consolidate the discussion on digital technologies (Big Data, IoT, AI).

Response: A new section titled “2.4 Key Enabling Technologies in Railway Digitalization” has been created. This section consolidates and synthesizes content previously distributed in Sections 2.3.1–2.3.4, presenting a cohesive comparative overview of enabling technologies and their contributions to railway maintenance.

2. Enhance the IoT subsection by addressing RFID applications.

Response: A new dedicated subsection 2.4.1 RFID Applications in Railway Maintenance and SHM has been added. It discusses the role of RFID technology in wireless sensing, infrastructure monitoring, and predictive maintenance, citing recent research (e.g., Wang et al., 2022; Liu et al., 2023; Ran et al., 2024) to strengthen the scientific basis of the review.

3. Provide a more detailed analysis of data privacy and cybersecurity challenges and best practices.

Response: Section 4.2 has been expanded to include a new subsection titled 4.2.2 Regulatory Frameworks and Cybersecurity Best Practices, offering a detailed analysis of vulnerabilities (e.g., threats to SCADA and cloud platforms), compliance frameworks (GDPR, ISO 27001, IEC 62443), and mitigation strategies including AI-based anomaly detection, zero-trust architectures, and personnel training.

4. Improve resolution and clarity of Figures 4 and 5.

Response: Figures 4 and 5 have been reinserted using higher-resolution exports and enlarged in the manuscript. They now appear clearly legible, allowing readers to accurately interpret the co-word analysis and temporal keyword evolution results.

We believe that the current version of the manuscript fully addresses all reviewer concerns and has been significantly strengthened in both academic rigor and practical relevance. Thank you for the opportunity to revise and improve this work. We remain available for any further clarification you may require.

Sincerely,

Mauricio Rodríguez-Hernández

Corresponding Author

Author Response File: Author Response.docx

Round 3

Reviewer 1 Report

Comments and Suggestions for Authors

No further comment