fa Optimal design Optimal design پژوهشي Research <p>Horizontal Directional Drilling (HDD) is extensively used in geothechnical engineering. In a variety of conditions it is essential to predict the torque required for performing the reaming operation. Nevertheless, there is presently not a convenient method to accomplish this task. To overcome this problem, in this research, the application of computational intelligence methods for data analysis named Support Vector Regression (SVR) optimized&nbsp; by&nbsp; differential evolution algorithm (DE) and Adaptive Neuro-Fuzzy Inference System (ANFIS) to estimate of the required rotational torque to operate horizontal directional drilling is demonstrated. Three ANFIS models were implemented, ANFIS&ndash;subtractive clustering method (ANFIS-SCM), ANFIS&ndash;grid partitioning (ANFIS-GP) and ANFIS&ndash;fuzzy c&ndash;means clustering method (ANFIS-FCM). The estimation abilities offered using SVR-DE, ANFIS-FCM, ANFIS-SCM, ANFIS-GP were presented by using field data given in open source literatures. In these models,&nbsp; the rotational torque (M) is used as the output parameter, while the length of drill string in the borehole (L), axial force on the cutter/bit (P), rotational speed (revolutions per minute) of the bit (N), the radius for the i^th reaming operation (D_i), the mud flow rate (W), the total angular change of the borehole (K_L), and the mud viscosity (V) are the input parameters. To compare the performance of models for rotational torque to operate horizontal directional drilling prediction, the coefficient of correlation (R²) and mean square error (MSE) of the models were calculated, indicating the good performance of the ANFIS-SCM model.</p> 45 70 http://ijoce.iust.ac.ir/browse.php?a_code=A-10-66-125&slc_lang=fa&sid=1 H. Fattahi 180031947532846001059 180031947532846001059 Yes Z. Bayatzadehfard 180031947532846001060 180031947532846001060 No