Ballasted Railway Track–Bridge Transition Zone Monitoring Methods: Recent Developments, Challenges, and Prospects.
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| Title: | Ballasted Railway Track–Bridge Transition Zone Monitoring Methods: Recent Developments, Challenges, and Prospects. |
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| Authors: | Siahkouhi, Mohammad1 (AUTHOR) m.siahkouhi@westernsydney.edu.au, Rashidi, Maria2 (AUTHOR) m.rashidi@westernsydney.edu.au, Miri, Amin3 (AUTHOR) amin.miri.iust@gmail.com, Ghiasi, Alireza4 (AUTHOR) ghiasi_ar@yahoo.com, Paixão, André5 (AUTHOR) apaixao@lnec.pt |
| Source: | Journal of Transportation Engineering. Part B. Pavements. Sep2025, Vol. 151 Issue 3, p1-18. 18p. |
| Subjects: | Optical fiber detectors, Intelligent sensors, Sensor networks, Optical radar, LIDAR |
| Abstract: | Many researchers have studied the dynamic response of ballasted railway track–bridge transition zones (RTBTZs) using different instrumentation systems. The primary factor contributing to the frequent abnormal dynamic behavior of the RTBTZ is differential settlement between the earthworks and structures, which is the main target for RTBTZ monitoring. These settlements result in an uneven longitudinal rail profile in the foundation and embankment soils, and in the upper layers (ballast, subballast, and form layer). This paper provides a comprehensive review based on potential new technologies for railway infrastructure monitoring. Limitations of different monitoring systems such as point scale monitoring, the sensor's long-term performance, different sensors for different output responses, and developing simplified numerical modeling based on recorded data are discussed. In addition, opportunities to conduct efficient monitoring using a new generation of sensors such as fiber optic sensors, smart self-sensing sensors, microelectromechanical system (MEMS) sensors, wireless sensing ballast particles (SmartRock), large-scale sensoring, and remote sensing techniques such as drones and light detection and ranging (LiDar) are presented. Practical Applications: A practical application of this review is in the field of railway maintenance and infrastructure management. Specifically, the implementation of advanced measurement methods in RTBTZs can help improve the monitoring and assessment of railway tracks, ensuring their safety, reliability, and optimal performance. For example, the use of new generation fiber optic sensors, smart self-sensing concrete sensors, MEMS sensors, and wireless sensing ballast particles can provide continuous monitoring of various parameters such as strain, temperature, vibration, and settlement. These sensors can be embedded within the track structure or placed strategically in the RTBTZ to gather real-time data on the condition and behavior of the track. By utilizing sensor networks and large-scale deployment, railway operators can create a comprehensive monitoring system that covers a vast network of tracks. This enables efficient data collection from multiple points. Inspection machines and drones equipped with advanced measurement technologies, such as LiDAR, can be deployed to perform automated inspections of the tracks, capturing high-resolution data of track geometry, alignment, and overall condition. The data collected using these advanced measurement methods can be processed and analyzed using specialized software and algorithms. This allows for the identification of trends, patterns, and anomalies, aiding in predictive maintenance planning, timely repairs, and optimization of track maintenance schedules. It also provides valuable insights for decision-making regarding track maintenance strategies and resource allocation. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
| Abstract: | Many researchers have studied the dynamic response of ballasted railway track–bridge transition zones (RTBTZs) using different instrumentation systems. The primary factor contributing to the frequent abnormal dynamic behavior of the RTBTZ is differential settlement between the earthworks and structures, which is the main target for RTBTZ monitoring. These settlements result in an uneven longitudinal rail profile in the foundation and embankment soils, and in the upper layers (ballast, subballast, and form layer). This paper provides a comprehensive review based on potential new technologies for railway infrastructure monitoring. Limitations of different monitoring systems such as point scale monitoring, the sensor's long-term performance, different sensors for different output responses, and developing simplified numerical modeling based on recorded data are discussed. In addition, opportunities to conduct efficient monitoring using a new generation of sensors such as fiber optic sensors, smart self-sensing sensors, microelectromechanical system (MEMS) sensors, wireless sensing ballast particles (SmartRock), large-scale sensoring, and remote sensing techniques such as drones and light detection and ranging (LiDar) are presented. Practical Applications: A practical application of this review is in the field of railway maintenance and infrastructure management. Specifically, the implementation of advanced measurement methods in RTBTZs can help improve the monitoring and assessment of railway tracks, ensuring their safety, reliability, and optimal performance. For example, the use of new generation fiber optic sensors, smart self-sensing concrete sensors, MEMS sensors, and wireless sensing ballast particles can provide continuous monitoring of various parameters such as strain, temperature, vibration, and settlement. These sensors can be embedded within the track structure or placed strategically in the RTBTZ to gather real-time data on the condition and behavior of the track. By utilizing sensor networks and large-scale deployment, railway operators can create a comprehensive monitoring system that covers a vast network of tracks. This enables efficient data collection from multiple points. Inspection machines and drones equipped with advanced measurement technologies, such as LiDAR, can be deployed to perform automated inspections of the tracks, capturing high-resolution data of track geometry, alignment, and overall condition. The data collected using these advanced measurement methods can be processed and analyzed using specialized software and algorithms. This allows for the identification of trends, patterns, and anomalies, aiding in predictive maintenance planning, timely repairs, and optimization of track maintenance schedules. It also provides valuable insights for decision-making regarding track maintenance strategies and resource allocation. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 25735438 |
| DOI: | 10.1061/JPEODX.PVENG-1608 |
