Deformation Modulus of Rock Masses: An Assessment of the Existing Empirical Equations


Creative Commons License

Kayabaşı A., Gökçeoğlu C.

GEOTECHNICAL AND GEOLOGICAL ENGINEERING, cilt.36, ss.2683-2699, 2018 (ESCI) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 36
  • Basım Tarihi: 2018
  • Doi Numarası: 10.1007/s10706-018-0491-1
  • Dergi Adı: GEOTECHNICAL AND GEOLOGICAL ENGINEERING
  • Derginin Tarandığı İndeksler: Emerging Sources Citation Index (ESCI), Scopus
  • Sayfa Sayıları: ss.2683-2699
  • Anahtar Kelimeler: Deformation modulus, Rock mass, Regression, Empirical equation, Prediction error, NEURAL-NETWORK, STRENGTH, TESTS, GSI
  • Eskişehir Osmangazi Üniversitesi Adresli: Evet

Özet

Rock mass deformation modulus is an important parameter for all geotechnical applications. However, the determination of rock mass deformation modulus with in situ tests are highly expensive and time consuming. For this reason, rock engineers and engineering geologists have proposed numerous empirical equations based on various rock mass and intact rock properties to estimate the deformation modulus of rock masses. In the present study, an assessment of the existing empirical equations was undertaken. For the purpose of the study, the data obtained from four investigation galleries opened during a dam construction (Artvin Dam, Turkey) were used. A total of 34 plate loading tests were employed in these galleries. The tested rock mass is poor quality tuff. Rock mass rating (RMR89), rock tunnelling quality index (Q) and geological strength index of each test levels were determined. The empirical deformation modulus values of rock mass were calculated by the 26 most cited empirical equations proposed by various researchers. The cross-checks between the measured rock mass deformation modulus and the empirically calculated rock mass modulus values were performed by simple regression analyses. The empirical equations with higher prediction capacity were also examined with root mean square error, values account for and prediction error evaluations. Among the empirical equations compared in this study, two empirical equation giving best performance and other four empirical equations providing acceptable results were determined.