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Application of several non-linear prediction tools for estimating uniaxial compressive strength of granitic rocks and comparison of their performances

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Abstract

Uniaxial compressive strength (UCS) of rock is crucial for any type of projects constructed in/on rock mass. The test that is conducted to measure the UCS of rock is expensive, time consuming and having sample restriction. For this reason, the UCS of rock may be estimated using simple rock tests such as point load index (I s(50)), Schmidt hammer (R n) and p-wave velocity (V p) tests. To estimate the UCS of granitic rock as a function of relevant rock properties like R n, p-wave and I s(50), the rock cores were collected from the face of the Pahang–Selangor fresh water tunnel in Malaysia. Afterwards, 124 samples are prepared and tested in accordance with relevant standards and the dataset is obtained. Further an established dataset is used for estimating the UCS of rock via three-nonlinear prediction tools, namely non-linear multiple regression (NLMR), artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS). After conducting the mentioned models, considering several performance indices including coefficient of determination (R 2), variance account for and root mean squared error and also using simple ranking procedure, the models were examined and the best prediction model was selected. It is concluded that the R 2 equal to 0.951 for testing dataset suggests the superiority of the ANFIS model, while these values are 0.651 and 0.886 for NLMR and ANN techniques, respectively. The results pointed out that the ANFIS model can be used for predicting UCS of rocks with higher capacity in comparison with others. However, the developed model may be useful at a preliminary stage of design; it should be used with caution and only for the specified rock types.

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Acknowledgments

The authors would like to extend their sincere gratitude to the Pahang–Selangor fresh water tunnel project team, especially to Ir. Dr. Zulkeflee Nordin, Ir. Arshad, the contractor and consultant groups for facilitating this study. Further, the authors wish to express their appreciation to Universiti Teknologi Malaysia for supporting this research.

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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

  2. Construction Research Alliance, Universiti Teknologi Malaysia (UTM), 81310, Skudai, Johor, Malaysia

    Mohsen Hajihassani

  3. Department of Geological Engineering, Engineering Faculty, Pamukkale University, 20020, Denizli, Turkey

    Saffet Yagiz

  4. Department of Civil Engineering, Islamic Azad University, Qaemshahr Branch, Qaemshahr, Iran

    Hossein Motaghedi

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  1. Danial Jahed Armaghani
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Jahed Armaghani, D., Tonnizam Mohamad, E., Hajihassani, M. et al. Application of several non-linear prediction tools for estimating uniaxial compressive strength of granitic rocks and comparison of their performances. Engineering with Computers 32, 189–206 (2016). https://doi.org/10.1007/s00366-015-0410-5

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