A neuro-fuzzy model for modulus of deformation of jointed rock masses

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Gokceoglu C., Yesilnacar E., Sonmez H., Kayabaşı A.

COMPUTERS AND GEOTECHNICS, vol.31, pp.375-383, 2004 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 31
  • Publication Date: 2004
  • Doi Number: 10.1016/j.compgeo.2004.05.001
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.375-383
  • Keywords: modulus of deformation, rock mass, neuro-fuzzy model, prediction method, UNCONFINED COMPRESSIVE STRENGTH, PREDICTION, IDENTIFICATION
  • Eskisehir Osmangazi University Affiliated: No


Use of indirect estimation methods for some rock mass parameter is considered traditionally in the rock mechanics applications. Generally, the regression based-statistical methods are used to develop an empirical equation. However, new techniques such as artificial neural networks, fuzzy inference systems or neuro-fuzzy systems were employed in recent years. In this study, construction of a neuro-fuzzy system to estimate the deformation modulus of rock masses is aimed, because this modulus has a crucial importance for many design approaches in rock engineering. For the purpose, a database including 115 data sets was employed and a neuro-fuzzy system consisting of two inputs, one output and three layers was constructed. After learning process, total 18 if-then fuzzy rules were obtained. The performance values such as RMSE, VAF, absolute error and coefficient of cross-correlation were calculated and, the constructed neuro-fuzzy model exhibited a high performance according to the performance indices. (C) 2004 Elsevier Ltd. All rights reserved.