Izvorni znanstveni članak

https://doi.org/10.17559/TV-20250507002651

Estimation of Rock Brittleness from Point Load Strength Index Data Using Machine Learning Methods

Deniz Akbay orcid.org/0000-0002-7794-5278 ; Department of Mining and Mineral Extraction, Çan Vocational School, Çanakkale Onsekiz Mart University, 17020, Çanakkale, Türkiye *
Gökhan Ekincioglu orcid.org/0000-0001-9377-6817 ; Department of Mining and Mineral Extraction, Kaman Vocational School, Kırşehir Ahi Evran University, 40100, Kırşehir, Türkiye
Murat Isik ; Department of Computer Engineering, Faculty of Engineering and Architecture, Kırşehir Ahi Evran University, 40100, Kırşehir, Türkiye
Mehmet Ali Yalcinkaya ; Department of Computer Engineering, Faculty of Engineering and Architecture, Kırşehir Ahi Evran University, 40100, Kırşehir, Türkiye

* Dopisni autor.

Sažetak

Brittleness is a vital mechanical property that characterizes a rock's tendency to fracture under applied stress without significant deformation, which is particularly significant in mining, tunnelling, and other geotechnical engineering applications. The accurate prediction of rock brittleness is essential for optimizing excavation strategies, ensuring operational safety, and improving the cost-efficiency of resource extraction processes. However, conventional brittleness assessment techniques-such as those based on uniaxial compressive strength (UCS) and tensile strength-can be labour-intensive, time-consuming, and expensive. This study introduces a predictive framework based on machine learning algorithms using Point Load Strength Index (PLI) values as the sole input variable. A comprehensive dataset comprising sedimentary, igneous, and metamorphic rocks was compiled from both literature sources and laboratory experiments. Multiple regression models were applied and compared, including traditional linear methods and advanced ensemble learners. Among these, the Gradient Boosting Regressor delivered the highest predictive accuracy, achieving an (R²) value of 0.96 for metamorphic rocks. The results demonstrate that even a single indirect measurement like PLI can serve as an effective predictor of rock brittleness when coupled with robust machine learning techniques. The findings highlight the potential of integrating AI-based models into rock mechanics workflows to streamline brittleness estimation and support sustainable mining practices.

Ključne riječi

geotechnical engineering; machine learning; non-destructive testing; point load strength index; rock brittleness

Hrčak ID:

345011

URI

https://hrcak.srce.hr/345011

Datum izdavanja:

28.2.2026.

Posjeta: 252 *