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Subject: search.filters.subject.Banded amphibolites

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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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    Assessments of Strength Anisotropy and Deformation Behavior of Banded Amphibolite Rocks
    (Springer, 2014-04) Esamaldeen Ali, Wu Guang; Zhao zhiming, Jiang Weixue
    Assessment of strength anisotropy in transversely isotropic rocks has been one of the most challenging subjects in rock engineering. However, far too little attention has been paid to banded amphibolite rocks. This study aim to evaluate strength and deformation anisotropy behavior of banded amphibolite rocks. The dynamic mechanical tests including ultrasonic pulse test, uniaxial compressive strength, Brazilian test and deformability test were performed on drilled rock samples as a function of foliation plane angle (b = 0 , 30 , 60 and 90 ). The results obtained have shown that the dynamic mechanical properties of amphibolite rocks have different values concerning banding plane. Compression and shear waves taken parallel to the foliation plane show highest values than those obtained in the other directions. Under uniaxial test, the banded amphibolite has a U-shaped anisotropy with maximum strength at b = 90 and minimum strength is obtained when b = 30 . Strength anisotropic index ranges between 0.96 and 1.47. It seems that the high range value of anisotropic index is mainly due to slight undulation of foliation planes, that being not perfectly straight. The results of elastic deformation test show that there is no clear dependence on microstructures characteristics of subtypeamphibolite rocks that controlling modulus ‘‘shapeanisotropy’’. However, in this study, Young modulus values of amphibolite rocks with b follow both types of shape-anisotropy, ‘‘U-shape’’ and ‘‘decreased order-shaped’’. Thus, this study recommended that further research be undertaken regarding the role of modulus ‘‘shape-anisotropy’’ within the same lithotype.
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    Empirical relations between compressive strength and microfabric properties of amphibolites using multivariate regression, fuzzy inference and neural networks: A comparative study
    (Elsevier, 2014) Esamaldeen Ali, Wu Guang; Abdelazim Ibrahim
    In this paper, microfabric properties including grain size, shape factor and quartz content are tailored to the specific evaluation of UCS of banded amphibolite rocks. However, the predicting capabilities of Artificial Neural Networks (ANNs) and Fuzzy Inference System (FIS) as well as the Multivariate Regression (MR) techniques have been evaluated and compared using the same input variables. To assess the model 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 three models. The study revealed that even though the developed three models are reliable to predict the UCS, the presented ANN method displays an obvious potential for the reliable assessment of UCS according to model performance criterion. However, the outcomes of this study are quite satisfactory, which may serve microfabric characterization to be easily extended to the modeling of strength and deformation behavior of rocks in the absence of adequate budget and facility of testing UCS.