Comparative evaluation of different statistical tools for the prediction of uniaxial compressive strength of rocks
Küçük Resim Yok
Tarih
2020
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Elsevier
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
In this study, uniaxial compressive strength (UCS), unit weight (UW), Brazilian tensile strength (BTS), Schmidt hardness (SHH), Shore hardness (SSH), point load index (Is(50)) and P-wave velocity (V-p) properties were determined. To predict the UCS, simple regression (SRA), multiple regression (MRA), artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS) and genetic expression programming (GEP) have been utilized. The obtained UCS values were compared with the actual UCS values with the help of various graphs. Datasets were modeled using different methods and compared with each other. In the study where the performance indice PIat was used to determine the best performing method, MRA method is the most successful method with a small difference. It is concluded that the mean PIat equal to 2.46 for testing dataset suggests the superiority of the MRA, while these values are 2.44, 2.33, and 2.22 for GEP, ANFIS, and ANN techniques, respectively. The results pointed out that the MRA can be used for predicting UCS of rocks with higher capacity in comparison with others. According to the performance index assessment, the weakest model among the nine model is P7, while the most successful models are P2, P9, and P8, respectively. (C) 2020 Published by Elsevier B.V. on behalf of China University of Mining & Technology.
Açıklama
Anahtar Kelimeler
Uniaxial compressive strength, Adaptive neuro-fuzzy inference system, Multiple regression, Artificial neural network, Genetic expression programming
Kaynak
International Journal of Mining Science and Technology
WoS Q Değeri
Q1
Scopus Q Değeri
Q1
Cilt
30
Sayı
6
