D. J. Armaghani, A. Mamou, Chrysanthos Maraveas, P. Roussis, Vassilis G. Siorikis, A. Skentou, P. G. Asteris
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Predicting the unconfined compressive strength of granite using only two non-destructive test indexes
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.
期刊介绍:
The Geomechanics and Engineering aims at opening an easy access to the valuable source of information and providing an excellent publication channel for the global community of researchers in the geomechanics and its applications.
Typical subjects covered by the journal include:
- Analytical, computational, and experimental multiscale and interaction mechanics-
Computational and Theoretical Geomechnics-
Foundations-
Tunneling-
Earth Structures-
Site Characterization-
Soil-Structure Interactions