Artificial Intelligence Methods in Railway Infrastructure Systems: Application of Data Centric Engineering

Diogo Ribeiro · Araliya Mosleh · Andreia Meixedo · Abdollah Malekjafarian · Ramin Ghiasi · Meisam Gordan

Aug 2025 · Academic Press

Ebook

500

Pages

Eligible

This book will become available on August 1, 2025. You will not be charged until it is released.

About this ebook

With the rapid recent advances in the field of railway systems and infrastructure construction, and the evolution of AI tools that have enormous potential for application to railway design, maintenance and operations, industry professionals and researchers need an up-to-date resource on these developments. 'Artificial Intelligence Methods in Railway Infrastructure Systems: Application of Data Centric Engineering' addresses this need. The book encapsulates the latest breakthroughs and contributions in these pivotal areas, providing readers with comprehensive insights into the cutting-edge methodologies and approaches shaping the field of railway infrastructure management. For engineers and researchers, the book provides a focused explanation of AI methodologies such as machine learning, computer vision and predictive analytics and their implementation to railway infrastructure development, tools that are new to this field. It combines theory with practical examples of the application of data centric engineering in structural health monitoring of monitoring of railway systems, thus enabling early anomaly detection and empowering infrastructure managers to address potential issues before they escalate. Given the expansive scope of research driving technological advancements in railway infrastructure management, this book serves as a reference for readers seeking to explore novel AI-based methodologies and harness their potential in the field. Readers will benefit from insights into how AI innovations can streamline their operations and enhance network safety across multiple dimensions. By providing a comprehensive overview of the subject matter, this book guides anticipatory strategies and shape future trends in railway infrastructure management.• From advanced machine learning algorithms to predictive analytics and computer vision techniques this book covers the diverse array of Artificial Intelligence (AI) tools that can address the complex challenges associated with railway infrastructure management.• Explores AI capabilities in the continuous monitoring of railway infrastructure, providing real-time insights into the condition of tracks, bridges, tunnels, and other critical assets.• Leverages the potential of AI in the automatization of inspection processes, reducing the need for manual intervention and improving the efficiency and accuracy of assessments.• Presents AI algorithms for early anomaly detection or deviations from normal operating conditions, alerting infrastructure managers to potential issues before they escalate.• Endorses the role of AI in enhancing the accuracy of damage identification by analyzing data from multiple sources, such as sensors and computer vision systems, allowing for precise localization and characterization of defects.• Presents AI-powered predictive maintenance models used in forecasting potential failures and recommending proactive maintenance actions, minimizing downtime, and optimizing resource allocation.

About the author

Dr Ribeiro is Professor at Instituto Superior de Engenharia do Porto in Portugal. He is a Member of the Institute of R&D in Structures and Construction (CONSTRUCT), coordinator or researcher on more than 20 R&D projects funded by industry, FCT and EU programs in the field of railway infrastructures and digital construction

Araliya Mosleh is a senior researcher at the Faculty of Civil Engineering, University of Porto. She obtained her PhD degree in 2016 from the University of Aveiro, Portugal. Since then she has actively engaged in 9 national and international projects in the field of railway infrastructure. She was a visiting researcher at Bundeswehr University (2015), Wollongong University (2017), and Evoleo Company (2019)

Andreia Meixedo holds a Master in Structural Engineering (2012) and a PhD in Civil Engineering (2021), all from the University of Porto. Her main research experience is related to damage identification, structural health monitoring, machine learning, railway infrastructures, wayside and onboard condition monitoring; weigh-in-motion; advanced models for analysis of the bridge-track-train dynamic interaction, structural testing and experimentation, model calibration and validation

Dr Abdollah Malekjafarian is an Assistant Professor in the School of Civil Engineering, University College Dublin, Ireland. His main areas of research interest are structural dynamics and random vibrations for civil infrastructure including "transport Infrastructure" and "offshore wind turbines"

Dr Ramin Ghiasi is a Postdoctoral Research Fellow at the School of Civil Engineering, University College Dublin, Ireland. His research interests encompass civil structure and infrastructure health monitoring (including transport infrastructure, offshore wind turbines, and tall buildings), the application of AI and optimization methods in civil engineering, and the creation of IoT-based monitoring systems

Dr Meisam Gordan is currently a Postdoctoral Research Fellow at University College Dublin, working on the Di-Rail project, which focuses on automated and rapid fault diagnosis of railway tracks using in-service train measurements. His research interests include: structural health monitoring, data mining, critical infrastructure resilience, Industry 4.0, big data and smart cities

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