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Prediction of Drillability of Rocks with Strength Properties Using a Hybrid GA-ANN Technique

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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.

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Authors and Affiliations

  1. Faculty of Science and Technology, Federation University Australia, P.O. Box 663, Ballarat, VIC, 3353, Australia

    Manoj Khandelwal

  2. Department of Geotechnics and Transportation, Faculty of Civil Engineering, Universiti Teknologi Malaysia, 81310, UTM, Skudai, Johor, Malaysia

    Danial Jahed Armaghani

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  1. Manoj Khandelwal
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  2. Danial Jahed Armaghani
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Correspondence to Manoj Khandelwal.

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Khandelwal, M., Armaghani, D.J. Prediction of Drillability of Rocks with Strength Properties Using a Hybrid GA-ANN Technique. Geotech Geol Eng 34, 605–620 (2016). https://doi.org/10.1007/s10706-015-9970-9

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