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

Iranian Journal of Earth Sciences

مقاله 5، دوره 11، شماره 4، دی 2019، صفحه 277-289   

نوع مقاله: Original Research Paper

شناسه دیجیتال (DOI): 10.30495/ijes.2019.669403

نویسندگان

Mehdi Kianpour؛ Seyed Mahmoud Fatemi Aghda^* ؛ Mehdi Talkhablou

Faculty of Erath Science, Kharazmi University, Tehran, Iran

چکیده

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 Q_srm. Because Q_srm 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 (V_p) with Q and Q_srm 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 Q_srm. About 700 sets of data were used for modeling and V_p was considered as the input parameter. The regression equations showed the relationship between V_p with Q and Q_srm,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 Q_srm 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 V_p has better performance in prediction of Q_srm than Q and theFIS model showed the best prediction results. Because these models have accuracy, they could be used in similar conditions.

کلیدواژه‌ها

Rock Mass Quality؛ Sarvak Limestone؛ P-Wave Velocity؛ Empirical Equations؛ Fuzzy Inference System

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