Welcome to International Network for Natural Sciences | INNSpub

Paper Details

Research Paper | August 1, 2014

| Download 2

Modeling of uniaxial compressive strength by genetic programming and neuro-fuzzy

Dadkhah Rasool, Madani Esfahani Nasser, Hoseeinmirzaee Zahra

Key Words:

J. Bio. Env. Sci.5(2), 604-611, August 2014


JBES 2014 [Generate Certificate]


Uniaxial Compressive Strength (UCS) is the most important rock parameter required and determined for rock mechanical studies in most civil and mining projects. In this study, two soft computing approaches, which are known as neuro-fuzzy inference system (ANFIS) and Genetic Programming (GP), are used in strength prediction of uniaxial compressive strength (UCS). Block Punch Index (BPI), porosity (n), P-wave velocity (Vp), Density () were used as inputs for both methods and were analyzed to obtain training and testing data. All of 130 data sets, the training and testing sets consisted of randomly selected 110 and 20 sets, respectively. Results showed that the ANFIS and GP models are capable of accurately predicting the uniaxial compressive strength (UCS) used in the training and testing phase of the study. The GP model results better prediction compared to ANFIS model.


Copyright © 2014
By Authors and International Network for
Natural Sciences (INNSPUB)
This article is published under the terms of the Creative
Commons Attribution Liscense 4.0

Modeling of uniaxial compressive strength by genetic programming and neuro-fuzzy

Guven A. Azamathulla HM, Zakaria NA. 2009. Linear genetic programming for prediction of circular pile scour. Ocean Engineering 36, 985–991.

Kayadelen C, Günaydın O, Fener M, Demir A, Zvan A. 2009. Modeling of the angle of shearing resistance of soils using soft computing systems. Expert Systems with Applications 36, 11814 -11826.

Dehghan S. 2010. Prediction of uniaxial compressive strength and modulus of elasticity for Travertine samples using regression and artificial neural networks. Journal of Mining Science and Technology 20, 41–46.

Demuth H, Beale M. 2001. Neural network toolbox for use with MATLAB. The MathWorks Inc.

Dubois D, Prade H. Fuzzy, 1980. Sets and systems. New York: Academic Press.

Fener M, Kahraman S, Bilgil A, Gunaydin O. 2005. A comparative evaluation of indirect methods to estimate the compressive strength of rocks. Journal of Rock Mechanic. 38(4), 329.

Gokceoglu C. 2002. A fuzzy triangular chart to predict the uniaxial compressive strength of Ankara agglomerates from their petrographic composition. Journal of Engineering Geology, 66 (39).

Gray G, Murray-Smith D, Sharman K, Weinbrenner T. 1998. Nonlinear model structure identification using genetic programming. Control Eng Pract; 6, 1341–52.

Hanifi C¸ 2009. anakcı. Prediction of compressive and tensile strength of Gaziantep basalts via neural networks and gene expression programming. Neural Comput & Applic 18, 1031–1041.

Jang J. 1993. ANFIS: adaptive-network-based fuzzy inference systems. IEEE Trans Syst Man Cybern; 23(3), 665–85.

Kahraman S. 2001. Evaluation of simple methods for assessing the uniaxial compressive strength of rock. Journal of Rock Mechanic, 38, 981.

Karakus M, Tutmez B. 2006. Fuzzy and multiple regression modeling for evaluation of intact rock strength based on point load, schmidt hammer and sonic velocity. Journal of Rock Mechanic, 39(1), 45.

Koza J. 1992. Genetic programming: on the programming of computers by means of natural selection. Cambridge, MA: MIT Press.

Mehmet M, Kayadelen C. 2010. Modeling of transfer length of prestressing strands using genetic programming and neuro-fuzzy. Advances in Engineering Software 41, 315–322.

Togun N, Baysec S. 2010. Genetic programming approach to predict torque and brake specific fuel consumption of a gasoline engine. Applied Energy. ARTICLE IN PRESS.

Padmini D, Ilamparuthi K, Sudheer K. 2008. Ultimate bearing capacity prediction of shallow foundations on cohesionless soils using neurofuzzy models. Computer Geotechnic; 35, 33–46.

Shahin M, Maier H, Jaksa M. 2003. Settlement prediction of shallow foundations on granular soils using B-spline neurofuzzy models. Computer Geotechnic; 30, 637–47.

Sonmez H, Tuncay E, Gokceoglu C. 2004. Models to predict the uniaxial compressive strength and the modulus of elasticity for Ankara Agglomerate. Journal of Rock Mechanic, 41, 717.

Takagi T, Sugeno M. 1985. Fuzzy identification of systems and its applications to modeling and control. IEEE Trans Syst Man Cybern;15, 116–32.

Tiryaki B. 2008. Predicting intact rock strength for mechanical excavation using multivariate statistics, artificial neural networks and regression trees. Journal of Engineering Geology, 99, 51.

Topcu IB, Saridemir M. 2008. Prediction of rubberized concrete properties using artificial neural network and fuzzy logic. Constraction Build Material; 22, 532–40.

Tsiambaos G, Sabatakakis N. 2004. Considerations on strength of intact sedimentary rocks. Journal of Engineering Geology, 72, 261.

Tutmez B, Tercan A. 2007. Spatial estimation of some mechanical properties of rocks by fuzzy modeling. Journal of Computer Geotechnic;34, 10–8.

Yasar E, Erdogan Y. 2004. Correlating sound velocity with the density, compressive strength and Young’s modulus of carbonate rocks. Journal of Rock Mechanic, 41, 871.

Yilmaz I, Sendir H. 2002. Correlation of schmidt hardness with unconfined compressive strength and young modulus in gypsum from Sivas (Turkey). Journal of Engineering Geology, 66, 211.

Yılmaz I, Yuksek A. 2007. An example of artificial neural network (ANN) application for indirect estimation of rock parameters. Journal of Rock Mechanic, 41(5), 781.