What type of study is this?

This is a Simple Linear Regression Analysis study (also classified as: Multiple Linear Regression Analysis).

Predictive Modeling of Drillability and Bit Wear Index for Efficient Drilling and Tunneling Operations

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This study demonstrates the prediction of bit wear and drillability indices in tunneling, highlighting the impact of rock properties.

Experimental analysis shows that rock properties notably influence drillability and bit wear indices.
Predictive models attained high accuracy (R² ≥ 0.83) for estimating both drilling rate and bit wear indices.
Utilizing linear regression models, the study identifies key geo-mechanical parameters affecting drilling efficiency.
Optimizing drilling operations can improve cost-effectiveness and performance in the mining and tunneling industries.

Kayani et al. (2025) studied this question.

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View Full Paper

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Authors

This study demonstrates the prediction of bit wear and drillability indices in tunneling, highlighting the impact of rock properties.

Experimental analysis shows that rock properties notably influence drillability and bit wear indices.
Predictive models attained high accuracy (R² ≥ 0.83) for estimating both drilling rate and bit wear indices.
Utilizing linear regression models, the study identifies key geo-mechanical parameters affecting drilling efficiency.
Optimizing drilling operations can improve cost-effectiveness and performance in the mining and tunneling industries.

Kayani et al. (2025) studied this question.

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