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This is a Experimental Study study (also classified as: Quantitative Study).

The Vibration Analysis on Various Railway Damages Using Support Vector Machine Method

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The approach achieved an accuracy of 88.19% for training and 82.61% for testing using machine learning.
Measured vibrations were categorized based on conditions of railway tracks, showcasing effective damage detection.
Data processing applied Fast Fourier Transform (FFT) and Power Spectral Density (PSD) for accurate signal assessment.
Machine learning models can enhance real-time monitoring of railway track integrity in operational environments.

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Observational analysis predicts railway track damage using machine learning, indicating improved monitoring methods.

Cahya et al. (2025) studied this question.

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