TY - JOUR
T1 - Advancing railway track health monitoring
T2 - Integrating GPR, InSAR and machine learning for enhanced asset management
AU - Koohmishi, Mehdi
AU - Kaewunruen, Sakdirat
AU - Chang, Ling
AU - Guo, Yunlong
PY - 2024
Y1 - 2024
N2 - Railway track health monitoring and maintenance are crucial stages in railway asset management, aiming to enhance the train operation quality and service life. For this aim, various inspection means (using diverse non-destructive testing techniques) have been applied, however, these means are mostly not able to monitor whole railway track network or track underlying layers (e.g., ballast and subgrade). The use of remote sensing techniques, such as Interferometric Synthetic Aperture Radar (InSAR), can expedite the defect diagnosis process for railway tracks, elevating the scope of health monitoring to a network-wide level. The Ground Penetrating Radar (GPR) has emerged as a particularly reliable method, especially for detecting structural deficiencies in underlying layers. As a result, combining the two distinct non-destructive testing techniques – GPR and InSAR – presents a promising strategy for efficient railway asset management. Recognizing the significance of embracing newer and more advanced monitoring strategies, this paper reviews the fusion of GPR and InSAR methodologies, and explores the potential integration of machine learning models to develop a predictive health monitoring and condition-based maintenance approach for railway tracks.
AB - Railway track health monitoring and maintenance are crucial stages in railway asset management, aiming to enhance the train operation quality and service life. For this aim, various inspection means (using diverse non-destructive testing techniques) have been applied, however, these means are mostly not able to monitor whole railway track network or track underlying layers (e.g., ballast and subgrade). The use of remote sensing techniques, such as Interferometric Synthetic Aperture Radar (InSAR), can expedite the defect diagnosis process for railway tracks, elevating the scope of health monitoring to a network-wide level. The Ground Penetrating Radar (GPR) has emerged as a particularly reliable method, especially for detecting structural deficiencies in underlying layers. As a result, combining the two distinct non-destructive testing techniques – GPR and InSAR – presents a promising strategy for efficient railway asset management. Recognizing the significance of embracing newer and more advanced monitoring strategies, this paper reviews the fusion of GPR and InSAR methodologies, and explores the potential integration of machine learning models to develop a predictive health monitoring and condition-based maintenance approach for railway tracks.
KW - Condition-based railway maintenance
KW - GPR
KW - Ground-penetrating radar
KW - InSAR
KW - Inspection
KW - Machine learning
KW - Non-destructive testing
KW - Railway ballasted track
KW - SAR interferometry
UR - http://www.scopus.com/inward/record.url?scp=85188711100&partnerID=8YFLogxK
U2 - 10.1016/j.autcon.2024.105378
DO - 10.1016/j.autcon.2024.105378
M3 - Review article
AN - SCOPUS:85188711100
SN - 0926-5805
VL - 162
JO - Automation in Construction
JF - Automation in Construction
M1 - 105378
ER -