Application of fuzzy inference system and nonlinear regression models for predicting rock brittleness

Saffet Yagiz, Candan Gokceoglu

Research output: Contribution to journalArticlepeer-review

90 Citations (Scopus)


Brittleness is one of the most crucial rock features for underground excavation and design considerations in rock mass. Direct standard testing method for measuring rock brittleness, the combination of rock properties rather than only one rock parameter have not available yet. Therefore, it is indirectly calculated as a function of some rock properties such as rock strength by using various ratios and prediction tools. The aim of this study is to estimate the rock brittleness by constructing fuzzy inference system and nonlinear regression analysis. For this purpose, a dataset established by utilizing the relevant laboratory rock tests (i.e., punch penetration, uniaxial compressive strength, Brazilian tensile strength and unit weight of rock) at the Earth Mechanics Institute of Colorado School of Mines in the USA on the rock samples assembled from 48 tunnels projects throughout the world. Running the established models, the performance values such as RMSE, VAF, absolute error and coefficient of cross-correlation were computed for developed models. The VAF and RMSE indices were calculated as 89.8% and 2.97 for the nonlinear multiple regression model and 83.1% and 3.82 for fuzzy model, respectively. As a result, these indices revealed that the prediction performance of the nonlinear multiple regression model is higher than that of the fuzzy inference system model. However, it is concluded that both constructed models exhibited a high performance according to the obtained prediction values.

Original languageEnglish
Pages (from-to)2265-2272
Number of pages8
JournalExpert Systems with Applications
Issue number3
Publication statusPublished - Mar 15 2010
Externally publishedYes


  • Brittleness
  • Fuzzy inference system
  • Nonlinear regression

ASJC Scopus subject areas

  • Engineering(all)
  • Computer Science Applications
  • Artificial Intelligence

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