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Bulletin of Engineering Geology and the Environment

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13-01-2021 | Original Paper

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

Authors: Seyed Amin Moosavi, Mehdi Mohammadi

Published in: Bulletin of Engineering Geology and the Environment | Issue 3/2021

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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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Title: Development of a new empirical model and adaptive neuro-fuzzy inference systems in predicting unconfined compressive strength of weathered granite grade III
Authors: Seyed Amin Moosavi
Mehdi Mohammadi
Publication date: 13-01-2021
Publisher: Springer Berlin Heidelberg
Published in: Bulletin of Engineering Geology and the Environment / Issue 3/2021
Print ISSN: 1435-9529
Electronic ISSN: 1435-9537
DOI: https://doi.org/10.1007/s10064-020-02071-8

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