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A new approach to rock mass classification using machine learning algorithms.

Allan Erlikhman M Santos¹, Milene Sabino Lana¹, Tiago M Pereira²

¹Federal University of Ouro Preto - UFOP, Mining Engineering Department - DEMIN, Campi Morro do Cruzeiro, s/n, Bauxita, 35400-000 Ouro Preto, MG, Brazil.

Anais Da Academia Brasileira De Ciencias

|June 3, 2026

Summary

This study introduces a novel machine learning model for rock mass classification, reducing subjectivity and optimizing geotechnical databases. The new approach uses k-medoid clustering and decision trees for easier application in rock engineering.

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Area of Science:

Geotechnical Engineering
Machine Learning Applications
Data Science in Geology

Background:

Traditional rock mass classification systems can be subjective and complex.
Geotechnical databases often contain high dimensionality, posing challenges for analysis.
Existing methods may not fully leverage the potential of machine learning for geological data.

Purpose of the Study:

To develop a novel, data-driven rock mass classification model using machine learning.
To reduce subjectivity and enhance parameter selectivity in rock mass assessment.
To create an accessible classification system for practical engineering applications.

Main Methods:

Factor analysis was employed to reduce the dimensionality of geomechanical variables.
K-medoid clustering (Partitioning Around Medoids algorithm) was utilized for unsupervised classification into distinct groups.
A decision tree was developed to translate the clustering results into an easily applicable classification scheme.

Main Results:

Factor analysis successfully identified significant variables influencing rock mass quality.
The k-medoid technique resulted in seven distinct geomechanical classes based on variable interpretation.
A decision tree was successfully generated, providing a user-friendly tool for applying the new classification system.

Conclusions:

The proposed machine learning model offers a more objective and efficient approach to rock mass classification.
The integration of clustering and decision trees optimizes the application of classification systems in geotechnics.
This method enhances the analysis of geotechnical databases and aids in rock engineering decision-making.