TY - JOUR ID - 686 TI - Application of Soft Computing Methods for the Estimation of Roadheader Performance from Schmidt Hammer Rebound Values JO - روش های تحلیلی و عددی در مهندسی معدن JA - ANM LA - fa SN - 2251-6565 AU - Fattahi, Hadi AD - Dept. of Mining, Arak University of Technology, Iran Y1 - 2017 PY - 2017 VL - 6 IS - ویژه نامه انگلیسی SP - 11 EP - 24 KW - Roadheader performance KW - Schmidt hammer rebound values KW - ANFIS-Subtractive clustering method KW - Artificial neural network DO - N2 - Estimation of roadheader performance is one of the main topics in determining the economics of underground excavation projects. The poor performance estimation of roadheader scan leads to costly contractual claims. In this paper, the application of soft computing methods for data analysis called adaptive neuro-fuzzy inference system- subtractive clustering method (ANFIS-SCM) and artificial  neural  network  (ANN) optimized  by  hybrid  particle  swarm  optimization  and  genetic  algorithm  (HPSOGA) to estimate roadheader performance is demonstrated. The data to show the applicability of these methods were collected from tunnels for Istanbul’s sewage system, Turkey. Two estimation models based on ANFIS-SCM and ANN-HPSOGA were developed. In these models, Schmidt hammer rebound values and rock quality designation (RQD) were utilized as the input parameters, and net cutting rates constituted the output parameter. Various statistical performance indices were used to compare the performance of those estimation models. The results indicated that the ANFIS-SCM model has strong potentials to estimate roadheader performance with high degrees of accuracy and robustness. UR - https://anm.yazd.ac.ir/article_686.html L1 - https://anm.yazd.ac.ir/article_686_01f800424d82bca5ed11e0d9fd7bc1b0.pdf ER -