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Geomechanics and Engineering Volume 25, Number 4, May25 2021 , pages 317-330 DOI: https://doi.org/10.12989/gae.2021.25.4.317 |
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Predicting the unconfined compressive strength of granite using only two non-destructive test indexes |
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Danial J. Armaghani, Anna Mamou, Chrysanthos Maraveas, Panayiotis C. Roussis, Vassilis G. Siorikis, Athanasia D. Skentou and Panagiotis G. Asteris
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| Abstract | ||
| This paper reports the results of advanced data analysis involving artificial neural networks for the prediction of the unconfined compressive strength of granite using only two non-destructive test indexes. A data-independent site-independent unbiased database comprising 182 datasets from non-destructive tests reported in the literature was compiled and used to train and develop artificial neural networks for the prediction of the unconfined compressive strength of granite. The results show that the optimum artificial network developed in this research predicts the unconfined compressive strength of weak to very strong granites (20.3-198.15MPa) with less than ±20% deviation from the experimental data for 70% of the specimen and significantly outperforms a number of available models available in the literature. The results also raise interesting questions with regards to the suitability of the Pearson correlation coefficient in assessing the prediction accuracy of models. | ||
| Key Words | ||
| unconfined compressive strength; rocks; non-destructive testing; effective porosity, pulse velocity, artificial neural networks; machine learning | ||
| Address | ||
| Danial J. Armaghani: Department of Civil Engineering, Faculty of Engineering, University of Malaya, 50603, Kuala Lumpur, Malaysia Anna Mamou,Vassilis G. Siorikis, Athanasia D. Skentou and Panagiotis G. Asteris: Computational Mechanics Laboratory, School of Pedagogical and Technological Education, 15122 Marousi, Greece Chrysanthos Maraveas: Department of Civil Engineering, University of Patras, Greece Panayiotis C. Roussis: Department of Civil and Environmental Engineering, University of Cyprus, 1678 Nicosia, Cyprus | ||