The ongoing digital transformation is changing asset management in the railway industry. Emerging digital technologies and Artificial Intelligence is expected to facilitate decision-making in management, operation, and maintenance of railway by providing an integrated data-driven and model-driven solution. An important aspect when developing decision-support solutions based on AI and digital technology is the users’ experience. User experience design process aims to create relevance, context-awareness, and meaningfulness for the end-user. In railway contexts, it is believed that applying a human-centric design model in the development of AI-based artefacts, will enhance the usability of the solution, which will have a positive impact on the decision-making processes. In this research, the applicability of such advanced technologies i.e., Virtual Reality, Mixed Reality, and AI have been reviewed for the railway asset management. To carry out this research work, literature review has been conducted related to available Virtual Reality/Augmented Reality/Mixed Reality technologies and their applications within railway industry. It has been found that these technologies are available, but not applied in railway asset management. Thus, the aim of this paper is to propose a human-centric design model for the enhancement of railway asset management using Artificial Intelligence, Virtual Reality, and Mixed Reality technologies. The practical implication of the findings from this work will benefit in increased efficiency and effectiveness of the operation and maintenance processes in railway.

The railway is a complex technical system of systems in a multi-stakeholder environment. The implementation of digital technologies is essential for achieving operational excellence and addressing stakeholders’ needs and requirements in relation to the railways. Digitalization is highly dependent on an appropriate digital infrastructure provided through proper information logistics, whereas cybersecurity is critical for the overall security and safety of the railway systems. However, it is important to understand the various issues and challenges presented by governance, business, and technical requirements. Hence, this paper is the first link in the chain to explore, understand, and address such requirements. The purpose of this paper is to identify aspects of distributed ledgers and to provide a taxonomy of issues and challenges to develop a secure and resilient data sharing framework for railway stakeholders.

Safety in underground mining operations relies on understanding the geological and geotechnical properties of the site. The creation of an underground void for mining induces instability in the rock structure, resulting in deformation. The compressive strength of the rocks is maintained by the tensioning of ground support, such as rockbolts. Monitoring and predicting the condition of the mining ground support system is crucial for ensuring the safety of operations. Inspecting the mining tunnels poses challenges due to their large span and ongoing production activities. Light Detection and Ranging (LiDAR) technology can scan physical structures and generate point cloud data, which is valuable for creating applications like topographic mapping and spatial models. Extracting rockbolt information from point cloud data from underground mines can offer comprehensive mine coverage. This information can be utilised to monitor the condition of the rockbolts over time. Extracted rockbolt data can assist in the health monitoring of ground support, indicating deformation due to geostatic pressure. This paper proposes a method for extracting rockbolt spatial information from point cloud datasets collected via LiDAR technology to facilitate Prognostics and Health Management for ground support in underground mining.

Purpose: In underground mining, geostatic pressure results in drift surface deformation. To ensure the stability and safety of the mining environment, a so-called ground support systems are commonly used. Today, there are various types of ground support systems, such as rockbolts, steel sets, mesh and shotcrete, cable bolts, ground anchors, etc. Ground support systems based on rockbolts are used to provide the tensioning support to the compressive strength of the rocks. Surface deformation occurring in the drift brings changes in the rockbolt protruding from the surface. Monitoring and predicting the health of the ground support would assist the decision process for rehabilitation of the ground support in the drift to maintain safe working conditions. Design/methodology/approach: In this article, a methodology has been proposed aimed at the quantification of deformation of underground mining drifts through point cloud processing (PCD) and other data sources to support health monitoring, development of decision support tools and integration with the mine Trigger, Action, Response Plans (TARPs). Findings: Quantification and comparison of rockbolt protruding length is indicative of the surface deformation, which can be visualised to access regions requiring rehabilitation. Research limitations/implications: The results of the developed algorithms are dependent on the quality of the data governed by human process during data collection. Recommendations have been made to the industries to improve the practices. Practical implications: The results were used to augment the PCD to create interactive visualisations on virtual reality (VR) and augmented reality (AR) systems for off-site or on-site inspections. Social implications: The presented work improves the safety of the working environment in deep underground mines by providing quantifiable results and useable visualisations. Originality/value: The proposed methodology processes point cloud data, collected in underground mining drifts, in a campaign-based manner. PCD processing focuses on (1) comparison of PCD quality, (2) computation of deformation while compensating for imperfect registration and (3) comparison of rockbolt regions using Wasserstein distance.