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Prediction of the strength and elasticity modulus of granite through an expert artificial neural network

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Abstract

The uniaxial compressive strength (UCS) and Young’s modulus (E) are important parameters in designing solutions to rock engineering problems. However, determination of these properties in the laboratory is expensive and time consuming. Therefore, many attempts have been made to estimate these properties indirectly by defining various correlations. These correlations often relate UCS and E to some basic rock index tests. Nevertheless, in this study, using an artificial neural network (ANN) enhanced with the imperialist competitive algorithm (ICA), a hybrid model is developed for predicting the UCS and E of granite samples. The samples used in this study were taken from the face of the Pahang–Selangor raw water transfer tunnel in Malaysia. To train the aforementioned model, the results of the laboratory tests, including porosity (n), P wave velocity (V P), point load strength index (I s(50)) and the Schmidt hammer rebound number (R n), were used as model inputs. For the sake of comparison, the performance of the hybrid model was checked against a conventional ANN predictive model with similar architecture. Value account for (VAF), root mean square error (RMSE) and coefficient of determination (R 2) were used to control the capacity performance of the predictive models. The performance indices obtained using the ICA-ANN approach show that the proposed model can predict UCS and E with a high degree of accuracy. The results of sensitivity analysis reveal that V P is the most influential parameter, compared to the other input parameters, on UCS and E.

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Acknowledgments

The authors would like to extend their sincere gratitude to the Pahang–Selangor Raw Water Transfer Project Team especially to Ir. Dr. Zulkeflee Nordin, Ir. Arshad, the contractor and consultant groups for facilitating this study. Also, the authors wish to express their appreciation to the Universiti Teknologi Malaysia for supporting this study and making it possible.

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Authors and Affiliations

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

    Danial Jahed Armaghani, Edy Tonnizam Mohamad & Ehsan Momeni

  2. Islamic Azad University, South Tehran Branch, Tehran, Iran

    Masoud Monjezi

  3. Aurecon Pty Ltd., Brisbane, Australia

    Mogana Sundaram Narayanasamy

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  1. Danial Jahed Armaghani
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  2. Edy Tonnizam Mohamad
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Correspondence to Danial Jahed Armaghani.

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Armaghani, D., Tonnizam Mohamad, E., Momeni, E. et al. Prediction of the strength and elasticity modulus of granite through an expert artificial neural network. Arab J Geosci 9, 48 (2016). https://doi.org/10.1007/s12517-015-2057-3

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