KHEERAN Corporation transforms raw Ground Penetrating Radar signals into precise maintenance intelligence using AI-powered ballast investigation data assessment. Our analytics platform automatically classifies ballast condition across every metre of surveyed track. As a result, railway maintenance teams receive clear, actionable condition maps — not raw data files requiring manual interpretation.
Ballast Investigation Data Assessment with AI: The Process
KHEERAN’s assessment pipeline begins with multi-frequency GPR survey data. Furthermore, our AI platform applies Short-Time Fourier Transform (STFT) analysis to each radar trace. This decomposes the waveform into frequency-domain representations. Consequently, subtle changes in ballast dielectric properties — caused by fouling, moisture, or voids — are revealed and classified automatically. Additionally, machine learning classifiers trained on validated datasets categorise each metre of track. Therefore, the output is a continuous, georeferenced condition map showing clean ballast zones, fouled sections, and moisture accumulation.
From Raw Signal to Maintenance Priority Map
The Selig Fouling Index is assigned to each section automatically. Moreover, sections are ranked by maintenance urgency. As a result, your planning team receives a prioritised action list — ready to support tamping, undercutting, cleaning, or ballast renewal decisions without additional interpretation.
Continuous Improvement Through Every Survey
Furthermore, every KHEERAN survey adds to our growing validation dataset. Consequently, our AI models improve with every project — delivering progressively higher accuracy for all clients. Contact KHEERAN today to discover how AI-powered ballast investigation data assessment can optimise your maintenance program.
Standards and Validation Behind Our Ballast Investigation Data Assessment AI
KHEERAN’s ballast investigation data assessment AI platform is validated against field-verified condition data from hundreds of kilometres of surveyed railway track. Furthermore, our classification methodology aligns with the Selig Fouling Index — the internationally recognised standard published by AREMA for ballast condition assessment. Consequently, AI-generated condition maps and maintenance priority rankings are grounded in established engineering science — not proprietary black-box outputs. Additionally, this standards alignment ensures that KHEERAN’s AI data assessment results are defensible for regulatory compliance and capital planning purposes.
