Showing papers by "Trilok Singh published in 2010"

Textural identification of basaltic rock mass using image processing and neural network

Abstract: A new approach to identify the texture based on image processing of thin sections of different basalt rock samples is proposed here. This methodology uses RGB or grayscale image of thin section of rock sample as an input and extracts 27 numerical parameters. A multilayer perceptron neural network takes as input these parameters and provides, as output, the estimated class of texture of rock. For this purpose, we have use 300 different thin sections and extract 27 parameters from each one to train the neural network, which identifies the texture of input image according to previously defined classification. To test the methodology, 90 images (30 in each section) from different thin sections of different areas are used. This methodology has shown 92.22% accuracy to automatically identify the textures of basaltic rock using digitized image of thin sections of 140 rock samples. Therefore, present technique is further promising in geosciences and can be used to identify the texture of rock fast and accurate.

Estimation of strength parameters of rock using artificial neural networks

Abstract: The accurate determination of geomechanical properties such as uniaxial compressive strength and shear strength requires considerable time in collecting appropriate samples, their preparation and laboratory testing. To minimize the time and cost, a number of empirical relations have been reported which are widely used for the estimation of complex rock properties from more easily acquired data. This paper reports the use of an artificial neural network to predict the deformation properties of Coal Measure rocks using dynamic wave velocity, point load index, slake durability index and density. The results confirm the applicability of this method.

Epoch determination for neural network by self-organized map (SOM)

Abstract: Artificial neural networks have a wide application in many areas of science and engineering and, particularly, in geotechnical problems with some degree of success due to the fact that the mechanical behavior of rocks are not salient. They are highly nonlinear, quite complex and complicated. While applying neural network in such complicated problems, epoch determination is based on hit-and-trail basis mainly. In this paper, the effect of different number of epochs is shown on the network and a method is proposed to determine the optimum number of epoch with the help of self-organized map (SOM) to avoid overtraining of the network. Data distribution is also done with the help of SOM and a statistical analysis is made to show consistency between training and testing dataset for ensuring the optimal model performance.

Assessment of tunnel instability—a numerical approach

Abstract: This paper outlines the application of numerical modeling to predict deformation and stability of tunnel to be excavated in Bansagar, M.P., India. To meet the ever-increasing demand of transportation, energy, and other infrastructure projects, a large volume of rock tunneling is being carried out throughout the world. The geotechnical properties along the route of the 1,800-m long tunnel in the Bansagar region of India have been studied. The rock mass rating and rock mass quality systems were employed for empirical rock mass quality determination. Numerical analysis for the stress–strain distribution of the tunnel excavation and support systems was also carried out. In order to simulate the excavation of tunnel (NATM) at a depth of 150 m below the ground , a series of finite element analyses using Mohr-coulomb elasto-plastic constitutive model has been carried out using PLAXIS 2D. The stability of tunnel has been analyzed, and stress pattern have been discussed.

Modeling of a jointed rock mass under triaxial conditions

Abstract: One of the most important aspects of designing a structure on or in a rock mass is based on the strength response of a jointed rock mass. Understanding this important aspect, the present study was undertaken to understand the strength response of a jointed rock mass with the help of a finite difference package FLAC3D. In the present work, an attempt has been made to understand the effect of discontinuity angle on the failure mode and strength of the rock mass. For this purpose, stress and displacement in the model were studied and various stress–strain histories were recorded at constant strain loading rate. Rock discontinuity plays a critical and vital role to understand physico-mechanical characteristics of a rock mass. It has wider application in the rock excavation engineering, e.g., caverns, tunnels, slope stability, dams, etc. Simulated rock results are compared with the analytically calculated results of the jointed rock mass and found in good agreement.

Static and dynamic analysis of a landslide

Abstract: Slope failure is a recurrent phenomenon in hilly regions. It is hazardous because of the accompanying rapid mass movement of soil and rock mass. To alleviate the damage caused by landslide, slope-stability analyses and stabilization techniques require in-depth understanding and appraisal of the process that govern the failure behaviour of slopes. Once the instability behaviour is understood, remedial measures such as retaining walls, rock bolts, anchoring, etc., can be recommended to stabilize the slope. This article deals with a comparative study used for the analysis of the stability of slopes of Amiyan area, near Kathgodam, Nainital, Uttarakhand assuming that the rock mass follow the Mohr–Coulomb failure criterion under static and dynamic loading conditions. The area constantly experiences local as well as regional slides along the river bank. A field study was carried out in the landslide area to collect the representative samples to determine the various physio-mechanical properties of rock as well ...

Sensitivity of total charge and maximum charge per delay on ground vibration

Abstract: Frequency is among the most used properties for determining the detrimental effect of ground blasting on the surrounding areas. In this paper, an attempt has been made to calculate the frequency of the blast by developing an adaptive neuro-fuzzy inference system (ANFIS) model. Blast design and explosive parameters are incorporated in the intelligent model. The sugeno type fuzzy inference system was generated using the subtractive clustering partition method. Two separate ANFIS networks have been generated, each with five inputs (maximum charge per delay and total charge, respectively in two different cases) and one output parameter (frequency) was trained using a hybrid parameter optimization method. A set of 160 data obtained from blast monitoring at a major surface coal mine in India was used for training the network. A different set of 27 data was used for validating and testing the designed network. It has been observed that for a given distance the frequency of the vibration is affected not only by t...

Quality assessment of potable water in the town of Kolasib, Mizoram (India)

Abstract: The potable water for the residents of the town of Kolasib in Mizoram state, India, is supplied by the Public Health Engineering Department (PHED) of the Government of Mizoram without any notable treatment. The source of water is the Tuichhuahen River, flowing from north to south in the area. Water samples were analyzed for their physicochemical and bacteriological characteristics in order to obtain the current quality level of the potable water in the twon. The samples were collected from two different sources, i.e., the supply from the government agency (PHED) and from the naturally occurring springs (tuikhurs). The results suggest that the water supplied by the PHED is better than that from the tuikhurs; however, the quality of water from both sources, which are used for drinking and domestic purposes, were found to be more or less within the tolerance limits.

Development of Emission Factors for Quantification of Blasting Dust at Surface Coal Mines

Abstract: Environmental impact assessment (EIA) and environmental management plan (EMP) is a statutory requirement for execution of new mining projects or for expansion of the operating projects. For this purpose, quantification of blasting dust emission is required. This can be done by developing emission factors for blasting. The concept is similar to that of specific charge in blasting. For mining operations other than blasting, quantification of dust can be done using emission factors. Emission estimation techniques are very limited for blasting. In this study, the emission factors were developed by carrying out a detailed field study at one of the largest opencast coal mines of India in all four seasons. Data on atmospheric and meteorological conditions were generated by installing sodar and automatic weather station at the mine site. Respirable dust samplers were installed for monitoring of the dust emitted during coal or overburden bench blasting. Emission factors for dust concentrations were developed in gram per cubic meter of rock excavated. The developed emission factors were used to estimate dust emissions for adjacent mines due to similarity in mining and meteorological conditions. Seasonal variations in moisture contents in benches, where dust was monitored, indicated the lowest emission factors in monsoon due to high moisture in the bench materials. Similar field studies were also conducted at another coalfield of India for two seasons. It was found that the emission factors are site-specific.

Development of Emission Factors for Quantification of Blasting Dust at Surface

Abstract: Environmental impact assessment (EIA) and environmental management plan (EMP) is a statutory requirement for execution of new mining projects or for expansion of the operating projects. For this purpose, quantification of blasting dust emission is required. This can be done by developing emission factors for blasting. The concept is similar to that of specific charge in blasting. For mining operations other than blasting, quantification of dust can be done using emis- sion factors. Emission estimation techniques are very limited for blasting. In this study, the emission factors were de- veloped by carrying out a detailed field study at one of the largest opencast coal mines of India in all four seasons. Da- ta on atmospheric and meteorological conditions were generated by installing sodar and automatic weather station at the mine site. Respirable dust samplers were installed for monitoring of the dust emitted during coal or overburden bench blasting. Emission factors for dust concentrations were developed in gram per cubic meter of rock excavated. The developed emission factors were used to estimate dust emissions for adjacent mines due to similarity in mining and meteorological conditions. Seasonal variations in moisture contents in benches, where dust was monitored, indicated the lowest emission factors in monsoon due to high moisture in the bench materials. Similar field studies were also conducted at another coalfield of India for two seasons. It was found that the emission factors are site-specific.

Artificial Neural Networks as a Valuable Tool for Well Log Interpretation

Abstract: Artificial neural networks (ANNs) are rapidly gaining popularity in the area of oil exploration. This article discusses the importance of ANNs to petroleum engineers and geoscientists and its advantages over other conventional methods of computing. ANNs can assist geoscientists in solving some fundamental problems such as formation, permeability prediction, and well data interpretation from geophysical well log responses with a greater degree of confidence comparable to actual well test interpretation. The main goal of the present article is to use the artificial neural network from a petroleum geoscientist's point of view and encourage geoscientists and researchers to consider it as a valuable alternative tool in the petroleum industry. A three-layer feed-forward back-propagation network has been used to predict neutron log (NPHI) and density log (RHOB) values using gamma ray (CGR), resistivity log (IDPH), and sonic log (DTCO) input parameters. The results are also compared by analysis performed...

Intelligent prediction of heating value of coal

Abstract: The gross calorific value (GCV) or heating value of a sample of fuel is one of the important properties which defines the energy of the fuel. Many researchers have proposed empirical formulas for estimating GCV value of coal. There are some known methods like Bomb Calorimeter for determining the GCV in the laboratory. But these methods are cumbersome, costly and time consuming. In this paper, multivariate regression analysis and Co-active neuro-fuzzy inference system (CANFIS) backed by genetic algorithm technique is used for the prediction of GCV, taking all the major constituents of the proximate and ultimate analyses properties as input parameters and the suitability of one technique over the other has been proposed based on the results. Correlations have been developed using multivariate regression analysis that are simple to use based on the proximate and ultimate analysis of data sets from 25 different states of USA because a very through study has been done and the data available is less variable. Also, CANFIS backed by genetic algorithm model is designed to predict the GCV of 4540 US coal samples from the abovementioned datasets. Optimization of the network architecture is done using a systematic approach (genetic algorithm). The network was trained with 4371, cross validation with 100, predicted with rest 69 datasets and the predicted results were compared with the observed values. The mean average percentage error in prediction is found to be negligible (0.2913%) and the generalization capability of the model was established to be excellent. A useful concept of sensitivity analysis is adopted to set the hierarchy of influence of input factors. The results of the present investigation provide functional and vital information for prediction of GCV of any type of coal in USA.

A comparative study of artificial neural network and multivariate regression analysis to analyze optimum renal stone fragmentation by extracorporeal shock wave lithotripsy.

Abstract: To compare the accuracy of artificial neural network (ANN) analysis and multi-variate regression analysis (MVRA) for renal stone fragmentation by extracorporeal shock wave lithotripsy (ESWL). A total of 276 patients with renal calculus were treated by ESWL during December 2001 to December 2006. Of them, the data of 196 patients were used for training the ANN. The predictability of trained ANN was tested on 80 subsequent patients. The input data include age of patient, stone size, stone burden, number of sittings and urinary pH. The output values (predicted values) were number of shocks and shock power. Of these 80 patients, the input was analyzed and output was also calculated by MVRA. The output values (predicted values) from both the methods were compared and the results were drawn. The predicted and observed values of shock power and number of shocks were compared using 1:1 slope line. The results were calculated as coefficient of correlation (COC) (r2 ). For prediction of power, the MVRA COC was 0.0195 and ANN COC was 0.8343. For prediction of number of shocks, the MVRA COC was 0.5726 and ANN COC was 0.9329. In conclusion, ANN gives better COC than MVRA, hence could be a better tool to analyze the optimum renal stone fragmentation by ESWL.

Surface exfoliation in ZnO by hydrogen implantation and its smoothening by high temperature annealing

Abstract: Zinc oxide (0001) bulk crystals were implanted by 100 keV H2+ ions with various fluences in the range of 5×1016 to 3×1017 cm–2. Surface layer exfoliation was observed in the case of samples that were implanted with fluence higher than 2.5×1017 cm–2, either in the as-implanted state or after annealing at higher temperatures up to 500 °C. The roughness of the exfoliated surfaces, as measured using atomic force microscopy, was found to be about 20 nm. These samples with exfoliated surfaces were further annealed at higher temperatures up to 1000 °C under air atmosphere. The surface morphology of the samples was studied using Nomarski optical microscopy, scanning electron microscopy (SEM) and atomic force microscopy (AFM). The defect characterization in the implanted ZnO samples was performed using transmission electron microscopy (TEM). Cross-sectional TEM measurements showed the formation of hydrogen filled nanovoids inside the damage band in ZnO. It was observed that after annealing the samples at 960 °C for 1 hr in air ambient, the roughness of the exfoliated surfaces reduced down to about 3 nm. (© 2010 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)