EVALUATION OF THE RELATIONSHIP BETWEEN THE SURFACE HARDNESS OF MAGMATIC BUILDING BLOCKS AND UNIAXIAL COMPRESSIVE STRENGTH VALUES WITH REGRESSION ANALYSIS AND ARTIFICIAL NEURAL NETWORKS

İnce İ., Balci M. C., Barstuğan M., FENER M., Bozdağ A.

Acta Geodynamica et Geomaterialia, cilt.22, sa.2 (218), ss.213-224, 2025 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 22 Sayı: 2 (218)
  • Basım Tarihi: 2025
  • Doi Numarası: 10.13168/agg.2025.0014
  • Dergi Adı: Acta Geodynamica et Geomaterialia
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.213-224
  • Anahtar Kelimeler: Artificial neural network, Leeb hardness, Multiple regressions, Schmidt hammer rebound hardness, Simple regression, Uniaxial compressive strength
  • Ankara Üniversitesi Adresli: Evet

Özet

Uniaxial compressive strength (UCS) values of rocks are the most important input parameter in rock mechanics and engineering applications. This parameter can be determined by laboratory tests and indirect methods. This study aimed to predict the UCS value with two different non-destructive testing techniques. To this end, the uniaxial compressive strength (UCS) and the values of Leeb hardness (HL) with low application energy and Schmidt hammer rebound hardness (SHR) with high application energy, which are among non-destructive testing techniques, of 95 different magmatic rocks (plutonic, volcanic, and pyroclastic) were determined. Simple regression (SR), multiple regression (MR), and artificial neural network (ANN) methods were employed to predict the UCS value. The models obtained using these methods were compared with each other. It was revealed that the model developed by ANN had the highest correlation number.