Application of artificial neural networks and multivariate statistics to predict UCS and E using physical properties of Asmari limestones

Using artificial neural network to predict dry density of soil from thermal conductivity

“…The deviation of the simulated results from the actual measurements is within the acceptable limit, which we set at maximum of 5%. Root-mean-square error (RMSE) was estimated for the predicted and measured dry densities using Equation 2 in order to check the performance of the model developed in this study [21]. The RMSE was found to be 0.50, which indicates good prediction performance of the model [21].…”

“…Root-mean-square error (RMSE) was estimated for the predicted and measured dry densities using Equation 2 in order to check the performance of the model developed in this study [21]. The RMSE was found to be 0.50, which indicates good prediction performance of the model [21]. Therefore, we could say that based on the high values of R and the low value of RMSE, our model shows high prediction performance and that the generalised model is efficient in predicting the dry density using thermal conductivity.…”

Sensitivity analysis of effective parameters on water curtain performance for crude oil storage in Iranian URC using the 2 k factorial design and numerical modeling

“…In the equivalent continuum approach no distinction is made between the water-bearing fractures and the matrix blocks and water is assumed to flow through the whole system (Samardzioska and Popov, 2005). The selection of levels of equivalent permeability and porosity is carried out on the basis of rock samples from different dam sites located in limestone from Asmari formation in Khozestan, Iran, such as Karun 4, Khersan 1 and 3, Seymareh (Torabi-Kaveh et al, 2014). Borehole spacing, pressure and elevation are selected according to suggestion range given by preliminary trials and previous publishing results (Froise, 1987;Kjørholt and Broch, 1992;Liang and Lindblom, 1994).…”

Prediction of Uniaxial Compressive Strength and Modulus of Elasticity in Calcareous Mudstones Using Neural Networks, Fuzzy Systems, and Regression Analysis

“…Furthermore, block punch and cylinder punch tests have been used for predicting uniaxial compressive strength of different types of rocks (Van der Schrier [41]; Ulusay and Gokceoglu [39]; Gokceoglu and Aksoy [14]; Ulusay et al [40]; Sonmez et al [34][35]; Sonmez and Tunusluoglu [36]; Aksoy [3]; Aksoy et al [4][5]; Karakul et al [22]; Jafari et al [19]; Mishra and Basu [32]; Khanlari et al [24][25][26]; Abatan et al [1]; Khanlari and Naseri [27]; Heidari et al [18]). More recently, a wide variety of statistical methods have been utilized for developing a proper correlation between UCS index and other engineering properties of rocks, among which different statistics analysis models, multiple regression analysis, ANN model, fuzzy models, and ANFIS models have received a greater attention (Alvarez and Babuska [6]; Sonmez et al [34]; Yilmaz and Yuksek [44]; Kahraman et al [21]; Heidari et al [16][17]; Manouchehrian et al [29]; Mishra and Basu [32]; Torabi-Kaveh et al [38]; Armaghani et al [2]; Jalali [20]).…”