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Development of a new empirical model and adaptive neuro-fuzzy inference systems in predicting unconfined compressive strength of weathered granite grade III

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Abstract

The present paper has developed a new multivariate linear regression and adaptive neuro-fuzzy inference processing to predict UCS for weathered granite grade III based on simple input data from point load index, Schmidt rebound hardness, and P wave velocity. Data from 85 rock core samples of this granite type have been selected. By using multivariate regression analysis, three models with two independent variables and one model with three independent variables have been developed. Furthermore, another model has been obtained by using a neuro-fuzzy logic analysis. The root means square error, RMSE, coefficient of determination, R2, and the mean absolute percentage error, MAPE, were used as the evaluation criteria of the accuracy of the models. The results have indicated that the regression-based and neuro-fuzzy models are effective, but the accuracy of the neuro-fuzzy model is in good agreement with the realistic data from the direct test.

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Acknowledgments

The authors would like to thank Dr. H. Shahriari for their critical reviews of this manuscript.

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Authors and Affiliations

  1. Mining Engineering Department, Vali-e-Asr University of Rafsanjan, Rafsanjan, Iran

    Seyed Amin Moosavi & Mehdi Mohammadi

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  1. Seyed Amin Moosavi
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  2. Mehdi Mohammadi
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Correspondence to Seyed Amin Moosavi.

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Moosavi, S.A., Mohammadi, M. Development of a new empirical model and adaptive neuro-fuzzy inference systems in predicting unconfined compressive strength of weathered granite grade III. Bull Eng Geol Environ 80, 2399–2413 (2021). https://doi.org/10.1007/s10064-020-02071-8

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