Journal Articles
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Item Explosive energy is the most commonly used form of energy to fragment rock mass/overburden and mineral deposits in the mines. Fragmentation obtained in the blasting process influences the downstream costs like loading cost, transportation cost, processing cost, etc. Among the various factors which influence the rock fragmentation, initiation system is one of the most important because presently much research is going on in this area of rock blasting. Some field studies were taken up with conventional detonating cord initiation and shock-tube-based NONEL initiation systems to study the influence of initiation systems on rock fragmentation. Fragmentation analysis was done using the boulder count method and image analysis. It was found that the shock-tube initiation gives 33% less boulders and 31% lesser K50 value compared to detonating cord initiation. © 2004 Taylor & Francis Ltd.(Taylor and Francis Ltd., Shock tube initiation for better fragmentation: A case study) Sastry, V.R.; Ram Chandar, K.2004Item Computer aided slope stability analysis(2006) Sastry, V.R.; Ram Chandar, K.; Santosh, M.Primary purpose of slope stability analysis in most of the engineering applications is to provide safe and economic design of slopes that prevent failure. The analysis technique chosen depends on both site conditions and potential mode of failure which depends upon the rock mass characteristics. Various slope stability analysis techniques include physical, numerical and analytical methods. Physical modeling is a time consuming process and a costly affair, while analytical method involves past experiences, it is site specific and depends upon various parameters which are difficult to quantify. Numerical analysis with sophisticated softwares provides an accurate solution within short duration. This paper presents an in-house developed software package called "V-slope" to analyze and interpret the slope with options for suggesting suitable safety measures based on the nature of slope. Slip circle and tension crack techniques were considered for analysis. The slope profile for different factor of safety (FOS) values will be displayed on the screen for easy understanding. In case the FOS is lower than the required, the V-slope gives suggestive measures. In case of temporary slopes the only way by which slope failure can be prevented is by decreasing the slope angle and in such cases the program gives additional volume of material to be excavated and the likely additional cost incurred for various slope angle options. For permanent slopes, option is provided for designing the soil nails, i.e. number of bolts required, length, diameter and spacing of the bolts etc. Finally the V-slope is compared with a comprehensive commercial software package Slide and the results were found very much satisfactory.Item Classification of Stability of Highwall During Highwall Mining: A Statistical Adaptive Learning Approach(Kluwer Academic Publishers, 2015) Ram Chandar, K.; Hegde, C.; Yellishetty, M.; Gowtham Kumar, B.The depleting coal deposits day by day required the introduction of novel methods of mining like highwall mining. Highwall mining is a method of extraction of coal blocked in the highwall. The method involves considerable challenges in the area of roof control and most importantly the stability of the highwall itself. Highwall mining has gained considerable importance all over the world, owing to the fact that the coal otherwise would not be extracted forever. This paper aims to assess the influence of varying conditions which can affect the stability of the highwall during highwall mining. The effect of gallery length, width of pillar and number of galleries are systematically studied through field investigations where a highwall mining was adopted first time in India. Initially, assessment was carried out using a numerical modelling approach and then the stability of the highwall is classified using multilinear regression, logistic regression and naive Bayes classifier. This will provide a mechanism to predict the stability of the highwall in future cases of similar conditions. The classification is done using statistical adaptive learning methods and a comparison of the methods is done. © 2014, Springer International Publishing Switzerland.Item Prediction of Bond's work index from field measurable rock properties(Elsevier B.V., 2016) Ram Chandar, K.; Deo, S.N.; Baliga, A.J.In mineral beneficiation, grinding is the final stage in the process of size reduction. The power consumed in this stage is higher when compared to other stages, owing to increased size reduction ratio. The primary purpose of grinding is to reduce the particle size to optimum so that mineral particles can be extracted more economically. Decision making plays an important role here, as it involves determining and comparing the energy that is required to perform the grinding process and also determining the amount of minerals lost as the coarser size particles are arrived at in mineral beneficiation. In general, Bond's work index is used to determine the grinding efficiency and also to calculate the power requirement. The process is very time consuming and it requires skilled labor and specialized mill. A systematic investigation was carried out to predict Bond's work index using simple field measurable properties of rocks. Tests were conducted on Basalt, Slate and Granite using a laboratory scale ball mill and rock properties namely density, Protodyakonov's strength index and rebound hardness number were determined. The results were analyzed using artificial neural networks and regression analysis. Mathematical equations were developed to predict Bond's work index based on rock properties using regression analysis, which resulted a very good correlation co-efficient values. © 2016 Elsevier B.V.Item A Critical Comparison of Regression Models and Artificial Neural Networks to Predict Ground Vibrations(Springer International Publishing, 2017) Ram Chandar, K.; Sastry, V.R.; Hegde, C.Blasting is important and an essential prerequisite in any opencast mine for fragmenting hard deposits. Blasting always produces unwanted effects like ground vibrations, noise and fly rock; among which ground vibrations effect is more on surrounding structures. Propagation of ground vibrations can lead to destruction of surrounding structures. Prediction of ground vibrations especially in terms of peak particle velocity is beneficial as opposed to conventional data monitoring techniques which can be expensive as well as time consuming. This paper uses predictors to estimate the intensity of ground vibrations and compares different methods of prediction methods like linear regression, multiple linear regression, non linear regression (NLR) and artificial neural networks. Intensity of ground vibrations generated from blasting operations was monitored in three different mines of limestone, dolomite and coal; obtaining about 168 ground vibration recordings in total. The statistical modelling or data-driven modeling has shown promise in the prediction of blast vibrations. Proposed a system of introducing site specific rock parameters like poison’s ratio, uniaxial compressive strength of rock and Young’s modulus to improve the correlation coefficient using statistical modelling (commonly called feature engineering in machine learning circles). © 2016, Springer International Publishing Switzerland.Item Prediction of peak particle velocity using multi regression analysis: case studies(Taylor and Francis Ltd. michael.wagreich@univie.ac.at, 2017) Ram Chandar, K.; Sastry, V.R.; Hegde, C.; Shreedharan, S.Ground vibrations produced from blasting operations cause structural vibrations, which may weaken structure if it occurs at the resonant frequency. Measurable parameters associated with ground vibrations are peak particle velocity (PPV), amplitude and dominant frequency (frequency of highest PPV amongst translational, vertical and horizontal vibrations). In this paper, an attempt is made to correlate measurable parameters associated with ground vibrations with scaled distance. Using the correlated data, it was found that a predictor equation can be determined for the amplitude and PPV, but not for dominant frequency as it is dynamic and depends upon infinitesimal changes that occur within a number of other parameters. Another analysis of the same is made using multiple linear regression analysis. This included predicting the PPV using scaled distance, maximum charge per delay, amplitude as predictors. A considerable improvement is seen in the prediction on adding the interaction of the predictors in multiple regressions. A comparison of different combination of predictors is made so as to assess the best combination giving the best R2 value for the given mine. Frequency is also plotted using the aforementioned method. However, it was found that the dominant frequency cannot be predicted with high accuracy even with this method. © 2016 Informa UK Limited, trading as Taylor & Francis Group.Item Use of iron ore mine tailings in infrastructure projects(Inderscience Enterprises Ltd., 2019) Shubhananda Rao, P.; Gayana, B.C.; Ram Chandar, K.Utilisation of iron ore tailings in bricks as a replacement for sand will help in sustainable and greener development. The literature shows the potential use of iron ore tailings as a replacement of natural fine aggregates. As natural sand reserves are depleting day by day, there is a need for substitution for sand in bricks. A comprehensive overview of the published literature on the use of iron ore tailings and other industrial waste is being presented. The effects of various properties such as compressive strength, thermal conductivity and durability of bricks have been presented in this paper. © 2019 Inderscience Enterprises Ltd.