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Author: search.filters.author.Esamaldeen Ali, Guang WuHas files: Yes

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    Prediction of compressive strength from microfabrics properties of banded amphibolite rocks using artificial neural networks and multivariate regression techniques
    (2014-04) Esamaldeen Ali, Guang Wu; Zhiming Zhao
    In complex inherent characteristics of certain rocks, especially anisotropic rock, it may be difficult to measure the uniaxial compressive strength (UCS). However, the use of empirical relationships to estimate UCS of rocks can be more practical and economical. In this study, the prediction capability of the Artificial Neural Networks (ANNs) and multivariate regression methods has been carried out to predict UCS from microfabrics properties of banded amphibolite rocks. Based on statistical analysis, microfabrics parameters including grain Size, shape factor and quartz content that adequately affect the values of UCS have been adopted in this study. The ANNs model was performed using the same input variables as multivariate regression model. To assess the models performances, some performance indices such as correlation coefficient (R), variance account for (VAF) and root mean square error (RMSE) were calculated and compared for the two models. The results show that even though the developed two models are reliable to predict the UCS, the study clearly indicates the superiority of the ANN model based on the model performance indices. This approach can be easily extended to the modeling of rock strength and deformation parameters in the absence of adequate geological information or abundant data.
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    Selection of Influential Microfabric Properties of Anisotropic Amphibolite Rocks on Its Uniaxial Compressive Strength (UCS): A Comprehensive Statistical Study
    (Scientific Research, 2014-11) Esamaldeen Ali, Guang Wu
    Occasionally, in complex inherent characteristics of certain rocks, especially anisotropic rocks it may be difficult to measure the uniaxial compressive strength UCS. However, the use of empirical relationships to evaluate the UCS of rock can be more practical and economical. Consequently, this study carried out to predict UCS from microfabrics properties of banded amphibolite rocks using multiple regression analysis. Based on statistical results, rock microfabric parameters, which adequately represent the UCS of a given rock type have been selected. The results show that grain size, shape factor and quartz content have high significant correlation with UCS at 95% confidence level. From multiple regression model, approximately 84% of the variance of the UCS can be estimated by the linear combination of these three parameters. However, according to model performance criteria: correlation coefficient (R = 0.919), variance account for (VAF = 97%) and root mean square error (RMSE = 4.16) the study clearly indicates that the developed model is reliable to predict the UCS. Finally, this approach can be easily extended to the modeling of rock strength in the absence of adequate geological information or abundant data.