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Assessment of physio-mechanical properties of rocks using ultrasonic pulse velocity tests- a case study

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Non-destructive techniques measure physicomechanical properties in various rock types, suggesting machine learning can enhance estimation accuracy.

Use of ultrasonic pulse velocity tests revealed strong correlations between uniaxial compressive strength and other rock properties.
The analysis involved rock samples from multiple locations and under varying conditions, enhancing the study's reliability.
Machine learning methods like GRNN and BPNN were employed to improve predictive modeling of rock strength parameters.
Results indicated that GRNN significantly outperformed BPNN, highlighting its potential for efficient rock property evaluation.

Gulzar et al. (2025) studied this question.

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Non-destructive techniques measure physicomechanical properties in various rock types, suggesting machine learning can enhance estimation accuracy.