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