Prediction of abrasiveness index of some Indian rocks using soft computing methods

被引：45
|
作者：
Tripathy, Ashutosh [1 ]
Singh, T. N. [1 ]
Kundu, Jagadish [1 ]
机构：
[1] Indian Inst Technol, Dept Earth Sci, Bombay 400076, Maharashtra, India
来源：
MEASUREMENT | 2015年 / 68卷
关键词：
Cerchar abrasivity index; Penetration rate; Artificial neural networking; Multi linear regression analysis; Soft computing; CERCHAR ABRASIVITY INDEX; ARTIFICIAL NEURAL-NETWORKS; STRENGTH; CAI; WEAR;
D O I：
10.1016/j.measurement.2015.03.009
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
The present paper mainly describes the prediction methodology to determine the Cerchar Abrasiveness Index and Penetration Rate related to rock excavation using simple geomechanical parameters as predictors. As abrasiveness of rocks is influenced by many geomechanical parameters, an attempt is made to use these parameters for its prediction using Multivariate Regression Analysis and Artificial Neural Networking. Abrasiveness Index as well as Penetration Rate are very vital in deciding the economics of the excavations as they directly govern the wear and tear of drill bit. It was observed that ANN shows a better prediction capability than MVRA using UCS, Point load index, P wave velocity and Young's modulus as predictors. (C) 2015 Elsevier Ltd. All rights reserved.
引用
收藏
页码：302 / 309
页数：8
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