Estimation of Rock Brittleness from Point Load Strength Index Data Using Machine Learning Methods
Yazarlar (4)
Dr. Öğr. Üyesi Deniz Akbay Çanakkale Onsekiz Mart Üniversitesi, Türkiye
Doç. Dr. Gökhan EKİNCİOĞLU Kırşehir Ahi Evran Üniversitesi, Türkiye
Dr. Öğr. Üyesi Murat IŞIK Kırşehir Ahi Evran Üniversitesi, Türkiye
Dr. Öğr. Üyesi Mehmet Ali YALÇINKAYA Kırşehir Ahi Evran Üniversitesi, Türkiye
| Makale Türü |
Özgün Makale
(SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale)
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| Dergi Adı | Tehnicki Vjesnik (Q3) | ||
| Dergi ISSN | 1330-3651 Dergi Bilgileri (2026) | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | Türkçe | Basım Tarihi | 04-2026 |
| Cilt / Sayı / Sayfa | 33 / 2 / 863–875 | DOI | 10.17559/TV-20250507002651 |
| Makale Linki | https://doi.org/10.17559/tv-20250507002651 | ||
| UAK Araştırma Alanları |
Makine Öğrenmesi
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| Özet |
| 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. |
| Anahtar Kelimeler |
| geotechnical engineering | machine learning | non-destructive testing | point load strength index | rock brittleness |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Web of Science | 1 |
| Scopus | 1 |
| Google Scholar | 1 |