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A comparative study of generalized regression neural network approach and adaptive neuro-fuzzy inference systems for prediction of unconfined compressive strength of rocks

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Abstract

The engineering properties of rocks play a significant role in planning and designing of mining and civil engineering projects. A laboratory database of mechanical and engineering properties of rocks is always required for site characterization and mineral exploitation. Due to discontinuous and variable nature of rock masses, it is difficult to obtain all physicomechanical properties of rocks precisely. Prediction of unconfined compressive strength from seismic wave velocities (Compressional wave, Shear wave) and density of rock using generalized regression neural network (GRNN) and adaptive neuro-fuzzy inference systems (ANFIS) can be appropriate and alternate methods to minimize the time and cost of tests. GRNN and ANFIS models were trained with 41 data sets using conjugate gradient descent algorithms and hybrid learning algorithm, respectively. Performance of both the models was examined with 15 testing data sets. In the present study, obtained network performance indices such as correlation coefficient, mean absolute percentage error, root mean square error and variance account for indicate high performance of predictive capability of GRNN system and closer to actual data over the ANFIS.

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Authors and Affiliations

  1. Department of Earth Sciences, Indian Institute of Technology Bombay, Mumbai, 400076, India

    Rajesh Singh, V. Vishal & T. N. Singh

  2. IITB Monash Research Academy, Indian Institute of Technology Bombay, Mumbai, 400076, India

    V. Vishal

  3. Department of Civil Engineering, Monash University, Clayton, VIC, 3800, Australia

    V. Vishal & P. G. Ranjith

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  1. Rajesh Singh
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  2. V. Vishal
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  3. T. N. Singh
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  4. P. G. Ranjith
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Correspondence to T. N. Singh.

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Singh, R., Vishal, V., Singh, T.N. et al. A comparative study of generalized regression neural network approach and adaptive neuro-fuzzy inference systems for prediction of unconfined compressive strength of rocks. Neural Comput & Applic 23, 499–506 (2013). https://doi.org/10.1007/s00521-012-0944-z

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  • DOI: https://doi.org/10.1007/s00521-012-0944-z

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