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An adaptive neuro-fuzzy inference system for predicting unconfined compressive strength and Young’s modulus: a study on Main Range granite

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Abstract

Engineering properties of rocks such as unconfined compressive strength (UCS) and Young’s modulus (E) are among the essential parameters for the design of tunnel excavations. Many attempts have been made to develop indirect methods of estimating UCS and E. This is generally attributed to the difficulty of preparing and conducting the aforementioned tests in a laboratory. In essence, this study aims to present two predictive models of UCS and E for granite using an adaptive neuro-fuzzy inference system (ANFIS). The required rock samples for model development (45 granite sample sets) were obtained from site investigation work at the Pahang-Selangor raw water transfer tunnel, which was excavated across the Main Range of Peninsular Malaysia. In developing the predictive models, dry density, ultrasonic velocity, quartz content and plagioclase were set as model inputs. These parameters were selected based on simple and multiple regression analyses presented in the article. However, for the sake of comparison, the prediction performances of the ANFIS models were checked against multiple regression analysis (MRA) and artificial neural network (ANN) predictive models of UCS and E. The capacity performances of the predictive models were assessed based on the value account for (VAF), root mean squared error (RMSE) and coefficient of determination (R 2). It was found that the ANFIS predictive model of UCS, with R 2, RMSE and VAF equal to 0.985, 6.224 and 98.455 %, respectively, outperforms the MRA and ANN models. A similar conclusion was drawn for the ANFIS predictive model of E where the values of R 2, RMSE and VAF were 0.990, 3.503 and 98.968 %, respectively.

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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 and contractor and consultant groups for facilitating this study. The authors would also like to express their appreciation to Universiti Teknologi Malaysia for support and making this study possible. Also, the authors are grateful to the reviewers for their constructive comments.

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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 & Mohd For Mohd Amin

  2. 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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Jahed Armaghani, D., Tonnizam Mohamad, E., Momeni, E. et al. An adaptive neuro-fuzzy inference system for predicting unconfined compressive strength and Young’s modulus: a study on Main Range granite. Bull Eng Geol Environ 74, 1301–1319 (2015). https://doi.org/10.1007/s10064-014-0687-4

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