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Subject: search.filters.subject.Microfabrics

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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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    Microfabrics-Based Approach to Predict Uniaxial Compressive Strength of Selected Amphibolites Schists Using Fuzzy Inference and Linear Multiple Regression Techniques
    (the geological society of america, 2015-08) ESAMALDEEN ALI, WU GUANG; ABDELAZIM IBRAHIM
    In this paper, we established the prediction capability for uniaxial compressive strength (UCS) from microfabric characterization of banded amphibolite schists using fuzzy inference system (FIS) and multiple linear regression (MLR) techniques. In this study, the method of semi-automatic petrographic image analysis (PIA) was adopted to calculate and measure the microfabric parameters. Based on statistical analysis, more influential microfabrics parameters that affect the UCS more than the others have been selected to predict UCS, which include grain size, shape factor, and quartz content. Multi-variate regression relations were established using the same input variables as the FIS model. To assess the performance of both models, some performance indices such as correlation coefficient (R), variance accounted for (VAF), and root mean square error (RMSE) were calculated and compared for the two models. The results show that both models reliably predict the UCS, with the multiple regression model being better based on the performance indices criteria. One of the most significant findings to emerge from this study is that the microfabrics-based PIA approach can be easily extended to the modeling of strength and deformation behavior of rocks in the absence of adequate geological information or abundant data.
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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.