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Arabian Journal of Geosciences - The uniaxial compressive and tensile strength of rocks are crucial parameters in the design stage of geotechnical structures. However, direct determination of these... 相似文献
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Measuring unconfined compressive strength (UCS) using standard laboratory tests is a difficult, expensive, and time-consuming task, especially with highly fractured, highly porous, weak rock. This study aims to establish predictive models for the UCS of carbonate rocks formed in various facies and exposed in Tasonu Quarry, northeast Turkey. The objective is to effectively select the explanatory variables from among a subset of the dataset containing total porosity, effective porosity, slake durability index, and P-wave velocity in dry samples and in the solid part of samples. This was based on the adjusted determination coefficient and root-mean-square error values of different linear regression analysis combinations using all possible regression methods. A prediction model for UCS was prepared using generalized regression neural networks (GRNNs). GRNNs were preferred over feed-forward back-propagation algorithm-based neural networks because there is no problem of local minimums in GRNNs. In this study, as a result of all possible regression analyses, alternative combinations involving one, two, and three inputs were used. Through comparison of GRNN performance with that of feed-forward back-propagation algorithm-based neural networks, it is demonstrated that GRNN is a good potential candidate for prediction of the unconfined compressive strength of carbonate rocks. From an examination of other applications of UCS prediction models, it is apparent that the GRNN technique has not been used thus far in this field. This study provides a clear and practical summary of the possible impact of alternative neural network types in UCS prediction. 相似文献
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Prediction of unconfined compressive strength of carbonate rocks using artificial neural networks 总被引：3，自引：2，他引：1
The unconfined compressive strength (UCS) of intact rocks is an important geotechnical parameter for engineering applications. Determining UCS using standard laboratory tests is a difficult, expensive and time consuming task. This is particularly true for thinly bedded, highly fractured, foliated, highly porous and weak rocks. Consequently, prediction models become an attractive alternative for engineering geologists. The objective of study is to select the explanatory variables (predictors) from a subset of mineralogical and index properties of the samples, based on all possible regression technique, and to prepare a prediction model of UCS using artificial neural networks (ANN). As a result of all possible regression, the total porosity and P-wave velocity in the solid part of the sample were determined as the inputs for the Levenberg–Marquardt algorithm based ANN (LM-ANN). The performance of the LM-ANN model was compared with the multiple linear regression (REG) model. When training and testing results of the outputs of the LM-ANN and REG models were examined in terms of the favorite statistical criteria, which are the determination coefficient, adjusted determination coefficient, root mean square error and variance account factor, the results of LM-ANN model were more accurate. In addition to these statistical criteria, the non-parametric Mann–Whitney U test, as an alternative to the Student’s t test, was used for comparing the homogeneities of predicted values. When all the statistics had been investigated, it was seen that the LM-ANN that has been developed, was a successful tool which was capable of UCS prediction. 相似文献
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The main objective of this study is to evaluate the influence of blast-induced acceleration on the stability of slopes at
Arakli-Tasonu limestone quarry where a planar shear failure has recently been observed. The planar failure occurred within
rock layers consisting of limestone and clayey limestone. The triggering mechanism of the planar shear failure was investigated
using blast-induced acceleration values obtained from 73 shots, field measurements, laboratory assessment of rock material
properties and utilizing limit equilibrium analysis. From the analysis of slope stability, the safety factor at a magnitude
of 0.106g was found to be lower than a minimum value of 1.2 in the case of a water-filled tension crack of a slope, 15 m depth. These
findings indicate that the planar shear failure was strongly influenced by the acceleration of uncontrolled blasting operations
as well as heavy rainfall. 相似文献
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