Estimation of uniaxial compressive strength of pyroclastic rocks (Cappadocia, Turkey) by gene expression programming


Ince I., Bozdag A., FENER M., KAHRAMAN S.

ARABIAN JOURNAL OF GEOSCIENCES, cilt.12, sa.24, 2019 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 12 Sayı: 24
  • Basım Tarihi: 2019
  • Doi Numarası: 10.1007/s12517-019-4953-4
  • Dergi Adı: ARABIAN JOURNAL OF GEOSCIENCES
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Anahtar Kelimeler: Uniaxial compressive strength (UCS), Pyroclastic rocks, Gene expression programming (GEP), Multiple linear regression (MLR), Construction materials, POINT-LOAD STRENGTH, OPEN-PIT MINE, TENSILE-STRENGTH, NEURAL-NETWORKS, PREDICTION, FUZZY, FLOW, DETERIORATION, PARAMETERS, CRITERION
  • Hacettepe Üniversitesi Adresli: Evet

Özet

Compressive strength of rocks is an important factor in structural design in rock engineering. Compressive strength can be determined in the laboratory by means of the uniaxial compressive strength (UCS) test, or it can be estimated indirectly by simple experiments such as point load strength (PLT) test and Schmidt hammer rebound test. Although the UCS test method is time-consuming and expensive, it is simple when compared to other methods. Therefore, many studies have been performed to estimate UCS values of rocks. Studies indicated that correlation coefficient of rock groups is low unless they are classified as metamorphic, sedimentary, or volcanic. Pyroclastic rocks are widely used as construction materials because of the fact that they crop out over extensive areas in the world. To estimate the UCS values of pyroclastic rocks in Central and Western Anatolia region, Turkey, multiple linear regression (MLR) analysis and gene expression programming (GEP) were employed and during the analysis, and PLT, rho(d), rho(s), and n were used as the independent variables. Based on the analysis results, it was detected that the GEP methods gave better results than MLR method. Additionally, the correlation coefficient (R-2) values of training and sets of validation of the GEP-I model are 0.8859 and 0.9325, respectively, and this model, thereby, is detected the best of generation individuals for prediction of the UCS.