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1.
This paper is an application of artificial neural networks (ANNs) in the prediction of the geometry of surface blast patterns in limestone quarries. The built model uses 11 input parameters which affect the design of the pattern. These parameters are: formation dip, blasthole diameter, blasthole inclination, bench height, initiation system, specific gravity of the rock, compressive and tensile strength, Young's modulus, specific energy of the explosive and the average resulting fragmentation size. Detailed data from a previous investigation were used to train and verify the network and predict burden and spacing of a blast. The built model was used to conduct parametric studies to show the effect of blasthole diameter and bench height on pattern geometry.  相似文献   

2.
Drilling and blasting is a major technology in mining since it is necessary for the initial breakage of rock masses in mining. Only a fraction of the explosive energy is efficiently consumed in the actual breakage and displacement of the rock mass, and the rest of the energy is spent in undesirable effects, such as ground vibrations. The prediction of induced ground vibrations across a fractured rock mass is of great concern to rock engineers in assessing the stability of rock slopes in open pit mines. The waveform superposition method was used in the Gol-E-Gohar iron mine to simulate the production blast seismograms based upon the single-hole shot vibration measurements carried out at a distance of 39 m from the blast. The simulated production blast seismograms were then used as input to predict particle velocity time histories of blast vibrations in the mine wall using the universal distinct element code (UDEC). Simulated time histories of particle velocity showed a good agreement with the measured production blast time histories. Displacements and peak particle velocities were determined at various points of the engineered slope. The maximum displacement at the crest of the nearest bench in the X and Y directions was 26 mm, which is acceptable in regard to open pit slope stability.  相似文献   

3.
Blasting is one of the primary mining operations for extracting minerals and ores however, if not designed properly, may have a varying degree of environmental and socio-economic impact in and around mining areas. In Indian mining industry, blast designs are fundamentally based on the experience and capability of the blasting crew and its assessment is more qualitative in nature, based on conventional trial and error basis. With the change in site geology and geotechnical parameters, the blast design parameters also require alterations, which can be standardized with the development of an intelligent system such as neural network. In this paper, the concept of artificial neural network and random forest algorithm has been used for better blast designs. Over 120 blast results from an opencast coal mine have been used for prediction of burden and energy factor with blast hole diameter, bench height to stemming ratio, nature of strata and average fragment size as input parameters. Out of 120 data sets 85 data sets recorded at a surface coal mine was used to train the model and 20 for the validation. Co-efficient of determination and root mean square error was chosen as the indicators to identify the optimum neural network and random forest model. The root mean square values obtained for energy factor is 0.153 while it is 0.1947 for burden. Similarly, the RMSE values obtained using random forest tree algorithm is 0.48 for burden while 50.76 for energy factor. The results revealed that random forest tree network system has potential to design better blast that is not generic and can be a potential tool for blasting engineers to design optimum blast for the mines.  相似文献   

4.
Most blast fragmentation models assume the rock mass properties. explosive properties and blast design variables to be constants and uniformly distributed within a blast. However, in reality all these input variables vary within a blast resulting in variation in the resulting fragmentation size distribution. A stochastic modelling approach is introduced in this paper to quantify this variation. This technique takes the input variables as statistical distributions rather than constants and through several thousand iterations, generates a statistical representation of the expected fragmentation resulting from a poduction blast. A case study of three production blasts from a large open pit mine are presented and the modelled fragmentation 'envelope' shows good agreement with the fragmentation 'envelope' estimated from Split image analysis. The various blast-related parameters influence different parts of the fragmentation distribution, e.g., rock strength and explosive velocity of detonation have most impact on the fines. The technique is used to identify the parameters that have the greatest influence on various size fractions. Such an analysis will be useful to direct resources to efficiently minimise the variation.  相似文献   

5.
In the blasting operation, risk of facing with undesirable environmental phenomena such as ground vibration, air blast, and flyrock is very high. Blasting pattern should properly be designed to achieve better fragmentation to guarantee the successfulness of the process. A good fragmentation means that the explosive energy has been applied in a right direction. However, many studies indicate that only 20–30 % of the available energy is actually utilized for rock fragmentation. Involvement of various effective parameters has made the problem complicated, advocating application of new approaches such as artificial intelligence-based techniques. In this paper, artificial neural network (ANN) method is used to predict rock fragmentation in the blasting operation of the Sungun copper mine, Iran. The predictive model is developed using eight and three input and output parameters, respectively. Trying various types of the networks, it was found that a trained model with back-propagation algorithm having architecture 8-15-8-3 is the optimum network. Also, performance comparison of the ANN modeling with that of the statistical method was confirmed robustness of the neural networks to predict rock fragmentation in the blasting operation. Finally, sensitivity analysis showed that the most influential parameters on fragmentation are powder factor, burden, and bench height.  相似文献   

6.
New Prediction Models for Mean Particle Size in Rock Blast Fragmentation   总被引：2，自引：1，他引：1  
The paper refers the reader to a blast data base developed in a previous study. The data base consists of blast design parameters, explosive parameters, modulus of elasticity and in situ block size. A hierarchical cluster analysis was used to separate the blast data into two different groups of similarity based on the intact rock stiffness. The group memberships were confirmed by the discriminant analysis. A part of this blast data was used to train a single-hidden layer back propagation neural network model to predict mean particle size resulting from blast fragmentation for each of the obtained similarity groups. The mean particle size was considered to be a function of seven independent parameters. An extensive analysis was performed to estimate the optimum value for the number of units for the hidden layer for each of the obtained similarity groups. The blast data that were not used for training were used to validate the trained neural network models. For the same two similarity groups, multivariate regression models were also developed to predict mean particle size. Capability of the developed neural network models as well as multivariate regression models was determined by comparing predictions with measured mean particle size values and predictions based on one of the most applied fragmentation prediction models appearing in the blasting literature. Prediction capability of the trained neural network models as well as multivariate regression models was found to be strong and better than the existing most applied fragmentation prediction model. Diversity of the blasts data used is one of the most important aspects of the developed models.  相似文献   

7.
Excavation of coal, overburden, and mineral deposits by blasting is dominant over the globe to date, although there are certain undesirable effects of blasting which need to be controlled. Blast-induced vibration is one of the major concerns for blast designers as it may lead to structural damage. The empirical method for prediction of blast-induced vibration has been adopted by many researchers in the form of predictor equations. Predictor equations are site specific and indirectly related to physicomechanical and geological properties of rock mass as blast-induced ground vibration is a function of various controllable and uncontrollable parameters. Rock parameters for blasting face and propagation media for blast vibration waves are uncontrollable parameters, whereas blast design parameters like hole diameter, hole depth, column length of explosive charge, total number of blast holes, burden, spacing, explosive charge per delay, total explosive charge in a blasting round, and initiation system are controllable parameters. Optimization of blast design parameters is based on site condition and availability of equipment. Most of the smaller mines have predesigned blasting parameters except explosive charge per delay, total explosive charge, and distance of blast face from surface structures. However, larger opencast mines have variations in blast design parameters for different benches based on strata condition: Multivariate predictor equation is necessary in such case. This paper deals with a case study to establish multivariate predictor equation for Moher and Moher Amlohri Extension opencast mine of India. The multivariate statistical regression approach to establish linear and logarithmic scale relation between variables to predict peak particle velocity (PPV) has been used for this purpose. Blast design has been proposed based on established multivariate regression equation to optimize blast design parameters keeping PPV within legislative limits.  相似文献   

8.
Explosion gas plays an important role in rock mass fragmentation and cast in rock blasting. In this technical note, the discontinuous deformation analysis method is extended for bench rock blasting by coupling the rock mass failure process and the penetration effect of the explosion gas based on a generalized artificial joint concept to model rock mass fracturing. By tracking the blast chamber evolution dynamically, instant explosion gas pressure is derived from the blast chamber volume using a simple polytropic gas pressure equation of state and loaded on the blast chamber wall. A bench blasting example is carried out. The blast chamber volume and pressure time histories are obtained. The rock failure and movement process in bench rock blasting is reproduced and analysed.  相似文献   

9.
In the last decade, fragmentation prediction has been attempted by many researchers in the field of blasting. Kuznetsov developed an equation for the estimation of average fragment size, x 50 , based on explosive energy and powder factors. Cunningham introduced a uniformity index n as a function of drilling accuracy, blast geometry and a rock factor A associated with a “blastability index”, which can be calculated from the jointing, density and hardness of the blasted rock mass. Knowing the mean size and the uniformity index, a Rosin-Rammler distribution equation can then be derived for calculating the fragment size distribution in a blasted muckpile. Analysis of existing data has revealed serious discrepancies between actual and calculated uniformity indices. The current integrated approach combines the Kuznetsov or similar equation and a comminution concept like the Bond Index equation to enable the estimation of both the 50% and 80% passing sizes ( k 50 and k 80 ). By substituting these two passing sizes into the Rosin-Rammler equation, the characteristic size x c and the uniformity index n can be obtained to allow the calculation of various fragment sizes in a given blast. The effectiveness of this new fragmentation prediction approach has been tested using sieved data from small-scale bench blasts, available in the literature. This paper will cover all tested results and a discussion on the discrepancy between measurement and prediction due to possible energy loss during blasting.  相似文献   

10.
Backbreak is an undesirable side effect of bench blasting operations in open pit mines. A large number of parameters affect backbreak, including controllable parameters (such as blast design parameters and explosive characteristics) and uncontrollable parameters (such as rock and discontinuities properties). The complexity of the backbreak phenomenon and the uncertainty in terms of the impact of various parameters makes its prediction very difficult. The aim of this paper is to determine the suitability of the stochastic modeling approach for the prediction of backbreak and to assess the influence of controllable parameters on the phenomenon. To achieve this, a database containing actual measured backbreak occurrences and the major effective controllable parameters on backbreak (i.e., burden, spacing, stemming length, powder factor, and geometric stiffness ratio) was created from 175 blasting events in the Sungun copper mine, Iran. From this database, first, a new site-specific empirical equation for predicting backbreak was developed using multiple regression analysis. Then, the backbreak phenomenon was simulated by the Monte Carlo (MC) method. The results reveal that stochastic modeling is a good means of modeling and evaluating the effects of the variability of blasting parameters on backbreak. Thus, the developed model is suitable for practical use in the Sungun copper mine. Finally, a sensitivity analysis showed that stemming length is the most important parameter in controlling backbreak.  相似文献   

11.
In blasting with air decks, repeated oscillation of shock waves within the air gap increases the time over which it acts on the surrounding rock mass by a factor at between 2 and 5. The ultimate effect lies in increasing the crack network in the surrounding rock and reducing the burden movement. Trials of air deck blasting in the structurally unfavourable footwall side of an open pit manganese mine has resulted in substantial improvements in fragmentation and blast economics. Better fragmentation resulted in improved shovel loading efficiency by 50–60%. Secondary blasting was almost eliminated. Use of ANFO explosive with this technique reduced explosive cost by 31.6%. Other benefits included reductions in overbreak, throw and ground vibration of the order of 60–70, 65–85 and 44% respectively. This paper reviews the theory of air deck blasting and describes in detail the air deck blast trials conducted in a manganese open pit mine in India. The blast performance data have been analysed to evaluate the benefits of air decking over conventional blasting.  相似文献   

12.
Tunnel blasting techniques in difficult ground conditions   总被引：1，自引：0，他引：1  
Summary The quality of tunnelling can be improved by proper blast design which takes into account the rock mass conditions. The effects of different rock mass properties on tunnel blast performance need to be assessed. The strength of the formation and joint orientation critically affected fragmentation and overbreak in a model study of blasting. Similar effects were noted in situ when the performance of a blast pattern in different rock mass conditions in the Tandsi inclines (Bihar, India) were analysed. Accordingly, the on-going blast pattern was modified for the poor ground conditions prevailing in the rest of the inclines. Improved fragmentation and smooth profile were obtained as a result; the rate of drivage improved considerably and the cost of excavation was reduced. Based on the observations in the model studies and the investigations at Tandsi, some guidelines for optimum blast design in difficult ground conditions are suggested.  相似文献   

13.
The influence of air deck blasting on blast performance and blast economics and its feasibility has been studied in the production blasting of soft and medium strength sandstone overburden rocks in an open pit coal mine in India. The air deck blasting technique was very effective in soft and medium strength rocks. Its main effects resulted in reducing fines, in producing more uniform fragmentation and in improving blast economics. The fines were reduced by 60–70% in homogeneous sandstones. Oversize boulders were reduced by 80% and shovel loading efficiency was improved by 20–40% in blocky sandstones. The explosive cost was reduced by 10–35% dependent on the type of rock mass. Throw, backbreak and ground vibration were reduced by 10–35%, 50–80% and 30–94% respectively. For a particular rock mass and blast design environment, air deck length (ADL) significantly influenced the fragmentation. ADL as represented by air deck factor (ADF) in the range of 0.10–0.35 times the original charge length (OCL) produced optimum results. ADF beyond 0.35 resulted in poor fragmentation and in inadequate burden movement.  相似文献   

14.
Blast Design Using Measurement While Drilling Parameters   总被引：1，自引：0，他引：1  
Measurement while drilling (MWD) techniques can provide a useful tool to aid drill and blast engineers in open cut mining. By avoiding time consuming tasks such as scan-lines and rock sample collection for laboratory tests, MWD techniques can not only save time but also improve the reliability of the blast design by providing the drill and blast engineer with the information specially tailored for use. While most mines use a standard blast pattern and charge per blasthole, based on a single rock factor for the entire bench or blast region, information derived from the MWD parameters can improve the blast design by providing more accurate rock properties for each individual blasthole. From this, decisions can be made on the most appropriate type and amount of explosive charge to place in a per blasthole or to optimise the inter-hole timing detonation time of different decks and blastholes. Where real-time calculations are feasible, the system could extend the present blast design even be used to determine the placement of subsequent holes towards a more appropriate blasthole pattern design like asymmetrical blasting.  相似文献   

15.
Summary The increasing range of explosive types and methods of initiation available to the blasting design engineer, and the possibilities of obtaining more detailed rock property data, require improvements in the precision of blasting design methods. Average design values, such as powder factor and specific charge, have little significance where rock properties vary in any lithological section of the blast. Application of the concept of incremental explosive energy distribution will increase the design sensitivity and control over blastability variations. In this paper the use of this concept is described for different levels of complexity. These range from the simple allocation of explosive energy for large rock sections, to the use of more complex energy attentuation functions to allocate incremental specific energy levels. Procedures to develop rock fragmentation predictions from such data are also outlined.  相似文献   

16.
One of the fundamental requirements for being able to optimise blasting is the ability to predict fragmentation. An accurate blast fragmentation model allows a mine to adjust the fragmentation size for different downstream processes (mill processing versus leach, for instance), and to make real time adjustments in blasting parameters to account for changes in rock mass characteristics (hardness, fracture density, fracture orientation, etc). A number of blast fragmentation models have been developed in the past 40 years such as the Kuz-Ram model [1]. Fragmentation models have a limited usefulness at the present time because: 1. The input parameters are not the most useful for the engineer to determine and data for these parameters are not available throughout the rock mass. 2. Even if the input parameters are known, the models still do not consistently predict the correct fragmentation. This is because the models capture some but not all of the important rock and blast phenomena. 3. The models do not allow for 'tuning' at a specific mine site. This paper describes studies that are being conducted to improve blast fragmentation models. The Split image processing software is used for these studies [2, 3].  相似文献   

17.
Summary The purpose of this study is to statistically correlate the fragmentation gradient () and average fragment size () with the blasting test parameters for rock masses having different characteristics. Blasting tests were conducted in limestone exposed during the highway construction between Tarsus and Pozanti (Turkey). Three test sites were classified as poor rock, good rock, and very good rock according to their RMR ratings. The selected blasting test parameters that affect the degree of fragmentation were burden, bench height and ANFO charge. After each blast, the muckpiles were screened and fragment size distribution graphs were plotted. Yates' method was applied for experimental design and analysis of variance. The single and combined effects of blasting test parameters were analyzed through the Yates' tables and significant and non-significant treatment combinations were determined for different rock masses. Some conclusions drawn from this research are: 1. The increase of RMR ratings promotes fragmentation, hence, increases blasting efficiency. 2. In rock masses of low RMR ratings, the volume of broken material is large, but fragmentation into small sizes is low. The opposite is true for rock masses of high RMR ratings. 3. The length of charge column is the significant factor affecting the average fragment size regardless the type of rock mass and is more significant in very good quality rock mass.  相似文献   

18.
Blasting is well-known as an effective method for fragmenting or moving rock in open-pit mines. To evaluate the quality of blasting, the size of rock distribution is used as a critical criterion in blasting operations. A high percentage of oversized rocks generated by blasting operations can lead to economic and environmental damage. Therefore, this study proposed four novel intelligent models to predict the size of rock distribution in mine blasting in order to optimize blasting parameters, as well as the efficiency of blasting operation in open mines. Accordingly, a nature-inspired algorithm (i.e., firefly algorithm – FFA) and different machine learning algorithms (i.e., gradient boosting machine (GBM), support vector machine (SVM), Gaussian process (GP), and artificial neural network (ANN)) were combined for this aim, abbreviated as FFA-GBM, FFA-SVM, FFA-GP, and FFA-ANN, respectively. Subsequently, predicted results from the abovementioned models were compared with each other using three statistical indicators (e.g., mean absolute error, root-mean-squared error, and correlation coefficient) and color intensity method. For developing and simulating the size of rock in blasting operations, 136 blasting events with their images were collected and analyzed by the Split-Desktop software. In which, 111 events were randomly selected for the development and optimization of the models. Subsequently, the remaining 25 blasting events were applied to confirm the accuracy of the proposed models. Herein, blast design parameters were regarded as input variables to predict the size of rock in blasting operations. Finally, the obtained results revealed that the FFA is a robust optimization algorithm for estimating rock fragmentation in bench blasting. Among the models developed in this study, FFA-GBM provided the highest accuracy in predicting the size of fragmented rocks. The other techniques (i.e., FFA-SVM, FFA-GP, and FFA-ANN) yielded lower computational stability and efficiency. Hence, the FFA-GBM model can be used as a powerful and precise soft computing tool that can be applied to practical engineering cases aiming to improve the quality of blasting and rock fragmentation.  相似文献   

19.
选择三塘湖矿区某露天采区进行研究,通过分析影响岩石物理力学强度指标的因素及岩体强度指标,选择适合本矿边坡稳定计算方法、普氏系数计算公式,得出边坡角值和剥离强度指标,最终评价其工程地质类型,该矿区属层状岩中等型。此外,根据该露天矿实际开采条件,确定开采工艺和剥离方式,建议应做好位移监测工作。  相似文献   

20.
The paper proposes a standardized image-processing procedure with the use of sieve analysis results for calibration which is utilized to measure the size distribution of fragmentation at Sungun mine. Through this procedure, a number of 19 bench blasting in various levels have been initially selected as the target of the study for each, multiple photos were taken immediately after blast from suitable perspectives and locations of the muckpiles surfaces. The number of image sampling was chosen adequately high to achieve further reliability of the whole photography procedure. Then fragments of each muckpile were separately mixed by a loader, where another image sampling from these new muckpiles, bucket of loaders, and haulage trucks was performed. For the purpose of sieve analysis, seven sieves with the mesh sizes between 1.27 cm (0.5 in) and 25.4 cm (10 in) were designed, manufactured, and then installed at Sungun semi-industrial laboratory. Additionally, three mass samples of the mixed fragments were randomly chosen among the 19 muckpiles for sieving. During image analysis stage, “sieve shift” and “mass power” factors, required to obtain standardized size distribution, were precisely assigned when the results obtained by the image analysis software was in accordance with the sieving results. In order to validate the reliability of the image processing, a comparative analysis of the achieved results was made with the results of the original Kuz–Ram model [Cunningham (1983) The Kuz–Ram model for prediction of fragmentation from blasting. In: Proceedings of the first international symposium on rock fragmentation by blasting, Lulea, Sweden, pp 439–454]. Finally, the image-processing procedure was found to be more efficient, with results close-matched to the real results of the sieve analysis.  相似文献   

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