Probabilistic Approach for Development of Track Geometry Defects as a Function of Ground Penetrating Radar Measurements
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Recent research has shown a relationship between track geometry defects and track subsurface conditions as measured by Ground Penetrating Radar (GPR). This paper presents the results of a comprehensive study looking at the development of a probabilitic model for the prediction of track geometry defects as a function of key subgrade parameters as measured by GPR. Specifically, several Logistical Regression (LR) analyses were performed, to include conventional LR modeling as well as a hybrid LR modeling approach based on Hierarchical Clustering Analysis with Histogram Data. The result was a higher order polynomial Logistic Regression model for determination of the probability of a track geometry surface defect occurring at locations with measured ballast fouling and measured ballast thickness. The results showed that there was a statistically significant relationship between high rates of geometry degradation and poor subsurface condition as defined by the GPR parameters: Ballast Fouling Index (BFI) and Ballast Layer Thickness (BLT). Furthermore, a predictive model was developed to determine the probability of a high rate of geometry degradation as a function of these key GPR parameters.
KeywordsRailways Railway track Track geometry Track deterioration Ground penetrating radar Hierarchical clustering analysis Logistic regression
The authors would like to acknowledge and thank the Federal Railroad Administration, U.S. Department of Transportation for their support of this research. The authors would also like to thank Gary Carr of the FRA and Ted Sussman of the Volpe National Transportation Systems Center for their support of this project. The authors would also like to acknowledge and thank Mike Trosino and Amanda Kessler of Amtrak for providing the geometry and GPR data used in this analysis.
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