A fuzzy model to predict the uniaxial compressive strength and the modulus of elasticity of a problematic rock


Gokceoglu C. , ZORLU K.

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, cilt.17, sa.1, ss.61-72, 2004 (SCI İndekslerine Giren Dergi) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 17 Konu: 1
  • Basım Tarihi: 2004
  • Doi Numarası: 10.1016/j.engappai.2003.11.006
  • Dergi Adı: ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
  • Sayfa Sayıları: ss.61-72

Özet

Although the uniaxial compressive strength and modulus of elasticity of intact rocks are highly important parameters for rock engineering and engineering geology projects, the necessary core samples cannot always be obtained from weak, highly fractured, thinly bedded, or block-in-matrix rocks. For this reason, the predictive models are often employed for the indirect estimation of mechanical parameters. However, to obtain the realistic values is very important for a predictive model. In this study, some predictive models using regression analysis and fuzzy inference system have been developed for the greywackes cropping out in the city of Ankara and its close vicinity. For this purpose, a series of rock mechanics tests were applied and the relevant intact rock parameters were obtained. Following the tests, descriptive statistical studies on the parameters, regression analyses and construction of fuzzy inference system studies were carried out. While meaningful relationships were not obtained from the simple regression analyses, both multiple regression analyses and the fuzzy inference system exhibited good predictive performance. In addition to the coefficient of correlation, the values account for (VAF) and the root mean square error indices were also calculated to check the prediction performance of the obtained models. The VAF and root mean square error indices were calculated as 41.49% and 15.62 for the uniaxial compressive strengths obtained from the multiple regression model; 64.02% and 8.85 for the modulus of elasticity values obtained from the multiple regression model; 81.24% and 13.06 for uniaxial compressive strengths obtained from the fuzzy inference system; and 78.64% and 6.87 for the modulus of elasticity values obtained from the fuzzy inference system. As a result, these indices revealed that the prediction performances of the fuzzy model are higher than those of multiple regression equations. (C) 2004 Elsevier Ltd. All rights reserved.