Nurcihan Ceryan

Prediction of unconfined compressive strength of carbonate rocks using artificial neural networks

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.

Application of Generalized Regression Neural Networks in Predicting the Unconfined Compressive Strength of Carbonate Rocks

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.

Probabilistic Analysis Applied to Rock Slope Stability: A Case Study From Northeast Turkey

Abstract In the evaluation of rock slope stability, there are many kinds of uncertainties including the input parameters uncertainty, the calculations uncertainty, and the procedure. Slope stability analysis can be classified into deterministic analysis or probabilistic analysis depending on how uncertainty is incorporated and evaluated. Probabilistic method takes into consideration the inherent variability and uncertainties in the analysis parameter. In this study, rock slope stability analysis, especially probability analyzes will be reviewed. In addition, probabilistic slope stability analysis and probabilistic back-analysis was applied to landslides which occurred at the Arakli-Tasonu quarry, NE Turkey, on October 3, 2005, March 20, 2006, and October 19, 2006. The sliding plane of the landslides passed through the thick clay layer. The tension cracks developed behind and are parallel to these landslides scarps. Considering these information, it is thought that the new failure can develop in these slopes. For this, no technical entry was allowed for this part of the quarry area. Therefore, the probabilistic stability analysis of these new slopes was performed. The values input parameters, shear strength parameters, and depth of the water in the tension crack used in these analyses are obtained by probabilistic back-analyses.