Railway Track Structural Health Monitoring: Identifying Emerging Trends and Research Agendas Using Bibliometric and Topic Modeling

While railways are critical for transportation, their expansive networks spanning thousands of kilometers pose significant challenges for conventional structural health inspection and maintenance. Recent advancements in sensors and artificial intelligence technologies have led to a substantial growth in the body of research proposing innovative approaches for Railway Track Structural Health Monitoring (RTSHM) to enhance safety and operational efficiency. This work aims to synthesize the current RTSHM research landscape to explore mainstream and emerging directions and identify advancements, challenges, and opportunities in this field. Through the hybrid systematic review using bibliometrics analysis and topic modeling, core research themes emerged, from developing sensor and data acquisition techniques as the foundation, to be combined with AI-based methods for fault detection and prediction. These predictions are leveraged for predictive maintenance through degradation modeling, supplemented with information from dynamic response assessment and performance optimization, and the ultimate goal is integration of RTSHM for operational safety assessments and risk-based decision-making. While technologically advanced, current research predominantly focuses on detecting discrete defects, thereby neglecting the holistic management of the track system. This fragmentation contributes to a complex and often siloed landscape for infrastructure management, emphasizing that RTSHM remains in a critical developmental stage. Consequently, the development of smart railway, integrated with intelligent data collection devices, deep learning technologies, and innovative operational platforms, represents a challenging yet promising direction for future research. These advancements are anticipated to foster safer, more efficient, and sustainable railway systems worldwide.