Prediction of strength properties of some schistose rocks from petrographic properties using artificial neural networks

Singh, Vimal; Singh, Divya; Singh, T. N.

doi:10.1016/s1365-1609(00)00078-2

Cited by 241 publications

(67 citation statements)

References 5 publications

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“…The network was trained to predict the uniaxial compressive strength, tensile strength and axial point load strength from the mineralogical composition, grain size, aspect ratio, form factor, area weighting and orientation of foliation planes (planes of weakness). It was found out that the neural network models of all three strengths were simulated simultaneously, but this is not possible in statistical predictions [4].…”

Section: Introductionmentioning

confidence: 99%

Relations between Texture Coefficient and Energy Consumption of Gang Saws in Carbonate Rock Cutting Process

et al. 2018

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Texture coefficient is one of the most influential parameters in rock engineering specifications in various projects including drilling, cutting, permeability of all-section drilling devices, etc. Meanwhile, investigating and forecasting the energy consumption of saw cutters are one of the most important factors in estimating the cutting costs. The present study aims to investigate the relationship between rock texture characteristics and the amount of energy consumption of the gang saw machine in the process of cutting carbonate rocks. To evaluate the effects of texture on the rocks' engineering specifications, 14 carbonate rock samples were studied. A microscopic thin section was made from each rock specimen. Then, five digital images were taken from each section under a microscope and the values of area, environment, the largest diameter and the smallest diameter of all grains in each image were determined. Using these specifications, the coefficient of texture of all rock samples was calculated and the relationship between the texture coefficient and the rate of energy consumption of the gang saw machine was investigated for the studied samples. The study results indicated that there was a significant relation between the texture coefficient and energy consumption rate in the three groups of carbonate rocks.

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Section: Introductionmentioning

confidence: 99%

Relations between Texture Coefficient and Energy Consumption of Gang Saws in Carbonate Rock Cutting Process

et al. 2018

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“…In recent years, artificial neural networks (ANN) have been applied to many studies in engineering geology and geomorphology (Meulenkamp and Alvarez Grima, 1999;Singh et al, 2001;Zhang and Govindaraju, 2003;Lee et al, 2003aLee et al, , b, 2004Gomez and Kavzoglu, 2005;Ermini et al, 2005;Sarangi and Bhattacharya, 2005;Yesilnacar and Topal, 2005;Sonmez et al, 2006;Lee and Evangelista, 2006) because an ANN based prediction model has a high prediction capacity due to its high performance in the modeling of non-linear multivariate problems. For this reason, ANN has become an attractive and important tool for engineering geologists and geomorphologists because both engineering geological and geomorphological problems are generally non-linear and multivariate problems.…”

Section: Introductionmentioning

confidence: 99%

An artificial neural network application to produce debris source areas of Barla, Besparmak, and Kapi Mountains (NW Taurids, Turkey)

Tunusluoglu

Gökçeoğlu

Sönmez

et al. 2007

Nat. Hazards Earth Syst. Sci.

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Abstract. Various statistical, mathematical and artificial intelligence techniques have been used in the areas of engineering geology, rock engineering and geomorphology for many years. However, among the techniques, artificial neural networks are relatively new approach used in engineering geology in particular. The attractiveness of ANN for the engineering geological problems comes from the information processing characteristics of the system, such as nonlinearity, high parallelism, robustness, fault and failure tolerance, learning, ability to handle imprecise and fuzzy information, and their capability to generalize. For this reason, the purposes of the present study are to perform an application of ANN to a engineering geology problem having a very large database and to introduce a new approach to accelerate convergence. For these purposes, an ANN architecture having 5 neurons in one hidden layer was constructed. During the training stages, total 40 000 training cycles were performed and the minimum RMSE values were obtained at approximately 10 000th cycle. At this cycle, the obtained minimum RMSE value is 0.22 for the second training set, while that of value is calculated as 0.064 again for the second test set. Using the trained ANN model at 10 000th cycle for the second random sampling, the debris source area susceptibility map was produced and adjusted. Finally, a potential debris source susceptibility map for the study area was produced. When considering the field observations and existing inventory map, the produced map has a high prediction capacity and it can be used when assessing debris flow hazard mitigation efforts.

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“…In the last few years, several scholars have used different techniques such as statistical regression and computational intelligent techniques to establish predictive models for predicting UCS from texture characteristics of rocks [11]- [15]. However, one of the main disadvantages of previous models is that they use less effective input parameters for predicting rock strength such as grain area weighting as well as the secondary mineral contents (e.g.…”

mentioning

confidence: 99%

“…However, one of the main disadvantages of previous models is that they use less effective input parameters for predicting rock strength such as grain area weighting as well as the secondary mineral contents (e.g. [11]). Hence, choosing a relatively smaller number of variables, but more influential which adequately represent the strength properties of a given rock type is necessary.…”

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confidence: 99%

Selection of Influential Microfabric Properties of Anisotropic Amphibolite Rocks on Its Uniaxial Compressive Strength (UCS): A Comprehensive Statistical Study

Ali¹,

Wu²

2014

JAMP

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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.

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Prediction of strength properties of some schistose rocks from petrographic properties using artificial neural networks

Cited by 241 publications

References 5 publications

Relations between Texture Coefficient and Energy Consumption of Gang Saws in Carbonate Rock Cutting Process

Relations between Texture Coefficient and Energy Consumption of Gang Saws in Carbonate Rock Cutting Process

An artificial neural network application to produce debris source areas of Barla, Besparmak, and Kapi Mountains (NW Taurids, Turkey)

Selection of Influential Microfabric Properties of Anisotropic Amphibolite Rocks on Its Uniaxial Compressive Strength (UCS): A Comprehensive Statistical Study

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