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41.
The strength of anisotropic rock masses can be evaluated through either theoretical or experimental methods. The latter is more precise but also more expensive and time-consuming especially due to difficulties of preparing high-quality samples. Numerical methods, such as finite element method (FEM), finite difference method (FDM), distinct element method (DEM), etc. have been regarded as precise and low-cost theoretical approaches in different fields of rock engineering. On the other hand, applicability of intelligent approaches such as fuzzy systems, neural networks and decision trees in rock mechanics problems has been recognized through numerous published papers. In current study, it is aimed to theoretically evaluate the strength of anisotropic rocks with through-going discontinuity using numerical and intelligent methods. In order to do this, first, strength data of such rocks are collected from the literature. Then FlAC, a commercially well-known software for FDM analysis, is applied to simulate the situation of triaxial test on anisotropic jointed specimens. Reliability of this simulation in predicting the strength of jointed specimens has been verified by previous researches. Therefore, the few gaps of the experimental data are filled by numerical simulation to prevent unexpected learning errors. Furthermore, a sensitivity analysis is carried out based on the numerical process applied herein. Finally, two intelligent methods namely feed forward neural network and a newly developed fuzzy modeling approach are utilized to predict the strength of above-mentioned specimens. Comparison of the results with experimental data demonstrates that the intelligent models result in desirable prediction accuracy. 相似文献
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Field observations of flows in coastal zone are scarce, but important for understanding the spatial variability of currents. The design of small, low-cost GPS drifters for collecting accurate Lagrangian data in the coastal zone is described. The drifters are intended for using in nearshore environments, lakes and estuaries over timescales of a few minutes up to several hours and are a low-cost alternative for applications which do not require drifter’s sea-going capability. Two field tests of GPS drifters in the south coast of Caspian Sea in Anzali port, Iran, in November 2008 and July 2009 were successful. 相似文献
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Parisa Bagheripour Mojtaba Asoodeh Ayoob Nazarpour 《Central European Journal of Geosciences》2013,5(3):450-456
Nuclear Magnetic Resonance (NMR) logging provides priceless information about hydrocarbon bearing intervals such as free fluid porosity and permeability. This study focuses on using geostatistics from NMR logging instruments at high depths of investigation to enhance vertical resolution for better understanding of reservoirs. In this study, a NMR log was used such that half of its midpoint data was used for geostatistical model construction using an ordinary kriging technique and the rest of the data points were used for assessing the performance of the constructed model. This strategy enhances the resolution of NMR logging by twofold. Results indicated that the correlation coefficient between measured and predicted permeability and free fluid porosity is equal to 0.976 and 0.970, respectively. This means that geostatistical modeling is capable of enhancing the vertical resolution of NMR logging. This study was successfully applied to carbonate reservoir rocks of the South Pars Gas Field. 相似文献
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Parisa Bagheripour Mojtaba Asoodeh Ali Asoodeh 《Central European Journal of Geosciences》2013,5(4):508-513
Oil formation volume factor (FVF) is considered as relative change in oil volume between reservoir condition and standard surface condition. FVF, always greater than one, is dominated by reservoir temperature, amount of dissolved gas in oil, and specific gravity of oil and dissolved gas. In addition to limitations on reliable sampling, experimental determination of FVF is associated with high costs and time-consumption. Therefore, this study proposes a novel approach based on hybrid genetic algorithm-pattern search (GA-PS) optimized neural network (NN) for fast, accurate, and cheap determination of oil FVF from available measured pressure-volume-temperature (PVT) data. Contrasting to traditional neural network which is in danger of sticking in local minima, GA-PS optimized NN is in charge of escaping from local minima and converging to global minimum. A group of 342 data points were used for model construction and a group of 219 data points were employed for model assessment. Results indicated superiority of GA-PS optimized NN to traditional NN. Oil FVF values, determined by GA-PS optimized NN were in good agreement with reality. 相似文献
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Tayeb Sadeghifar Maryam Nouri Motlagh Massoud Torabi Azad Mahdi Mohammad Mahdizadeh 《Marine Geodesy》2017,40(6):454-465
The prediction of wave parameters has a great significance in the coastal and offshore engineering. For this purpose, several models and approaches have been proposed to predict wave parameters, such as empirical, soft computing, and numerical based approaches. Recently, soft computing techniques such as recurrent neural networks (RNN) have been used to develop sea wave prediction models. In this study, the RNN for wave prediction based on the data gathered and the measurement of the sea waves in the Caspian Sea, in the north of Iran is used for this study. The efficiency of RNNs for 3, 6, and 12 hourly and diurnal wave prediction using correlation coefficients is calculated to be 0.96, 0.90, 0.87, and 0.73, respectively. This indicates that wave prediction by using RNNs yields better results than the previous neural network approaches. 相似文献
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Monitoring the concentration of radon gas is an established method for geophysical analyses and research, particularly in earthquake studies. A continuous radon monitoring station was implemented in Jooshan hotspring, Kerman province, south east Iran. The location was carefully chosen as a widely reported earthquake-prone zone. A common issue during monitoring of radon gas concentration is the possibility of noise disturbance by different environmental and instrumental parameters. A systematic mathematical analysis aiming at reducing such noises from data is reported here; for the first time, the Kalman filter (KF) has been used for radon gas concentration monitoring. The filtering is incorporated based on several seismic parameters of the area under study. A novel anomaly defined as “radon concentration spike crossing” is also introduced and successfully used in the study. Furthermore, for the first time, a mathematical pattern of a relationship between the radius of potential precursory phenomena and the distance between epicenter and the monitoring station is reported and statistically analyzed. 相似文献
49.
Mojtaba Naeej Mohamad Reza Naeej Jafar Salehi Reyhaneh Rahimi 《Geomechanics and Geoengineering》2017,12(2):107-114
The hydraulic conductivity, Ks, is one of the most important hydraulic properties which controls the water and solute movement into the soil. It is measured on soil specimens in the laboratory. On the other hand, sometimes it is obtained by tests carried out in the field by a number of researchers. Therefore, several experimental formulas have developed to predict it. Recently, soft computing tools have been used to evaluate the hydraulic conductivity. However, these tools are not as transparent as empirical formulas. In this study, another soft computing approach, i.e. model trees, have been used for predicting the hydraulic conductivity. The main advantage of model trees is that, unlike the other data learning tools, they are easier to use and represent understandable mathematical rules more clearly. In this paper, a new formula that includes some parameters is derived to estimate the hydraulic conductivity. To develop the new formulas, experimental data sets of hydraulic conductivity were used. A comparison is made between the estimated hydraulic conductivity by this new formula and formulas given by other’s researches. 相似文献
50.
Naeim Ghaeini Mojtaba Mousakhani Hassan Bakhshandeh Amnieh Ahmad Jafari 《Arabian Journal of Geosciences》2017,10(18):409
One of the most important aims of blasting in open pit mines is to reach desirable size of fragmentation. Prediction of fragmentation has great importance in an attempt to prevent economic drawbacks. In this study, blasting data from Meydook mine were used to study the effect of different parameters on fragmentation; 30 blast cycles performed in Meydook mine were selected to predict fragmentation where six more blast cycles are used to validate the results of developed models. In this research, mutual information (MI) method was employed to predict fragmentation. Ten parameters were considered as primary ones in the model. For the sake of comparison, Kuz-Ram empirical model and statistical modeling were also used. Coefficient of determination (R 2), root mean square error (RMSE), and mean absolute error (MAE) were then used to compare the models. Results show that MI model with values of R 2, RMSE, and MAE equals 0.81, 10.71, and 9.02, respectively, is found to have more accuracy with better performance comparing to Kuz-Ram and statistical models. 相似文献