Numerical parametric study for ballast assessment using SVM applied to GPR data

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Abstract This study explores novel radar-based methodologies for ballast fouling evaluation and structural anomalies in railway structures. Ground Penetrating Radar (GPR), coupled with numerical modeling, is utilized to provide insights into ballast condition and structure. Various data processing methods, including the Matrix Pencil Method (MPM) and Full Waveform Inversion (FWI), are investigated for their effectiveness in detecting fouling. Support Vector Machine (SVM)-based machine learning approaches are employed to enhance classification accuracy. The results demonstrate that integrating raw GPR signals with MPM yields the most accurate classification results, facilitating efficient assessment of ballast condition and contributing to improved railway maintenance strategies.