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Manuscript ID : IJES-1808-1241 (R2) Visit : 82 Page: 277 - 289

10.30495/ijes.2019.669403

Article Type: Original Research

Prediction model of limestone rock mass quality, using seismic wave velocity (Case study: Sarvak formation in Bakhtiari dam site)

Subject Areas : Mineralogy

Mehdi  Kianpour 1 (Faculty of Erath Science, Kharazmi University, Tehran, Iran)
Seyed Mahmoud  Fatemi Aghda 2 (Faculty of Erath Science, Kharazmi University, Tehran, Iran)
مهدی  تلخابلو 3 (ایران، تهران، دانشگاه خوارزمی، دانشکده علوم زمین)

Received: 2018-08-12 Accepted : 2019-07-02 Published : 2019-10-01

Keywords: P-Wave Velocity, Sarvak Limestone, Rock Mass Quality, Fuzzy Inference System, Empirical Equations,

Abstract :

The purpose of this study was to develop a model for the estimation of rock mass classification of Sarvak limestone in the Bakhtiari dam site, south-west (SW) Iran. Q system had been used as the starting point for the rock mass classification. This method was modified for sedimentary rock mass which is known as Qsrm. Because Qsrm considers a wide range of rock mass properties, it has become a tool for rock mass classification that more correlates with geophysical parameters. This study tried to revise and empower the correlation between P-wave velocity (Vp) with Q and Qsrm in Sarvak limestone. By using data sets of Bakhtiari Dam Site (BDS) in SW Iran and multivariate regression and the Fuzzy Inference System (FIS), models were rendered for prediction of Q and Qsrm. About 700 sets of data were used for modeling and Vp was considered as the input parameter. The regression equations showed the relationship between Vp with Q and Qsrm,under conditions of quadratic relations, obtained coefficients of determination (R2) of 0.49 and 0.66, respectively. The correlation coefficient was calculated as 0.82 for the Qsrm obtained from FIS models. Also, Variance Accounted For (VAF) and Root Means Square Error (RMSE) indexes were also used for evaluation of prediction accuracy of models. Results showed that Vp has better performance in prediction of Qsrm than Q and theFIS model showed the best prediction results. Because these models have accuracy, they could be used in similar conditions.

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