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