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Sayed Rahim Moeinossadat, Kaveh Ahangari, Danial Behnia,
Volume 9, Issue 1 (6-2015)

The present study aims to employ intelligent methods to predict shear wave velocity (Vs) in limestone. Shear wave velocity is one of the most important rock dynamic parameters. Direct determination of this parameter takes time, cost and requires accuracy as well. On the other hand, there is no precise equation for indirect determination. This research attempts to provide some simulations to predict Vs using the information obtained several dams located in Iran, using different approaches, including adaptive neuro-fuzzy inference system (ANFIS) and gene expression programming (GEP). 136 datasets were utilized for modeling and 34 datasets were used for evaluating its performance. Parameters such as Compressional wave velocity (Vp), density (g) and porosity (n) were considered as input parameters. The values of R2 and RMSE were 0.958 and 113.620 for ANFIS, where they were 0.928 and 110.006 for GEP respectively. With respect to the accuracy of the intelligent methods, they can be recommended for future studies
Seyed Hamed Moosavi, M Sharifzadeh ,
Volume 10, Issue 4 (5-2017)

Combination of Adoptive Network based Fuzzy Inference System (ANFIS) and subtractive clustering (SC) has been used for estimation of deformation modulus (Em) and rock mass strength (UCSm) considering depth of measurement. To do this, learning of the ANFIS based subtractive clustering (ANFISBSC) was performed firstly on 125 measurements of 9 variables such as rock mass strength (UCSm), deformation modulus (Em), depth, spacing, persistence, aperture, intact rock strength (UCSi), geomechanical rating (RMR) and elastic modulus (Ei). Then, at second phase, testing the trained ANFISBSC structure has been perfomed on 40 data measurements. Therefore, predictive rock mass models have been developed for 2-6 variables where model complexity influences the estimation accuracy. Results of multivariate simulation of rock mass for estimating UCSm and Em have shown that accuracy of the ANFISBSC method increases coincident with development of model from 2 variables to 6 variables. According to the results, 3-variable model of ANFISBSC method has general estimation of both UCSm and Em corresponding with 20% to 30% error while the results of multivariate analysis are successfully improved by 6-variable model with error of less than 3%. Also, dip of the fitted line on data point of measured and estimated UCSm and Em for 6-variable model approaches about 1 respect to 0.94 for 3- variable model. Therefore, it can be concluded that 6-variable model of ANFISBSC gives reasonable prediction of UCSm and Em.

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