- Title
- Prediction of drillability of rocks with strength properties using a hybrid GA-ANN technique
- Creator
- Khandelwal, Manoj; Armaghani, Danial
- Date
- 2016
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/99335
- Identifier
- vital:10347
- Identifier
-
https://doi.org/10.1007/s10706-015-9970-9
- Identifier
- ISSN:0960-3182
- Abstract
- The purpose of this paper is to provide a proper, practical and convenient drilling rate index (DRI) prediction model based on rock material properties. In order to obtain this purpose, 47 DRI tests were used. In addition, the relevant strength properties i.e. uniaxial compressive strength and Brazilian tensile strength were also used and selected as input parameters to predict DRI. Examined simple regression analysis showed that the relationships between the DRI and predictors are statistically meaningful but not good enough for DRI estimation in practice. Moreover, multiple regression, artificial neural network (ANN) and hybrid genetic algorithm (GA)-ANN models were constructed to estimate DRI. Several performance indices i.e. coefficient of determination (R2), root mean square error and variance account for were used for evaluation of performance prediction the proposed methods. Based on these results and the use of simple ranking procedure, the best models were chosen. It was found that the hybrid GA-ANN technique can performed better in predicting DRI compared to other developed models. This is because of the fact that the proposed hybrid model can update the biases and weights of the network connection to train by ANN.
- Publisher
- Springer International Publishing
- Relation
- Geotechnical and Geological Engineering Vol. 34, no. 2 (2016), p. 605-620
- Rights
- Copyright © 2016 Springer International Publishing Switzerland
- Rights
- Open Access
- Rights
- This metadata is freely available under a CCO license
- Subject
- Artificial neural network; Drilling rate index; Hybrid model; Rock material properties; Compressive strength; Forecasting; Mean square error; Neural networks; Regression analysis; Rock drilling; Tensile strength; Brazilian tensile strengths; Coefficient of determination; Drilling rates; Hybrid genetic algorithms; Rock materials; Simple regression analysis; Uniaxial compressive strength; Genetic algorithms; 0905 Civil Engineering
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