A Blind Comparison Between Results of Four Image Analysis Systems Using a Photo-Library of Piles of Sieved Fragments

Four image analysis systems for measuring rock fragmentation: FragScan, PowerSieve®, Split and WipFrag, have been compared under conditions necessary to provide an objective though limited assessment of their capabilities. The analysis of results is based on a sample of ten photographs taken from a series of photographs of controlled artificial muckpiles. These were created from dumping a blended mixture of sieved samples of limestone aggregate, in order to create a range of near perfect Rosin-Rammler sieve size distributions. Results from the various systems are compared with sieved results using both histogram and cumulative forms, with and without fines corrections in the case of Split and Wipfrag. Statistical indicators are evaluated to examine the match between system prediction values and sieving values. Commentaries on the results by the inventors of each system have been incorporated. All four systems were found to perform both well in some cases and poorly in others. From a detailed examination of the results, some insight into the strengths and weaknesses of the various systems is presented.     

A Practical Procedure for the Measurement of Fragmentation by Blasting by Image Analysis

Summary. The operation of a digital image analysis system in a limestone quarry is described. The calibration of the system, required in order to obtain moderately reliable fragmentation values, is done from muckpile sieving data by tuning the image analysis software settings so that the fragmentation curve obtained matches as close as possible the sieving. The sieving data have also been used to extend the fragment size distribution curves measured to sizes below the system’s optical resolution and to process the results in terms of fragmented rock, discounting the material coming from a loose overburden (natural fines) that is cast together with the fragmented rock. Automatic and manual operation modes of the image analysis are compared. The total fragmentation of a blast is obtained from the analysis of twenty photographs; a criterion for the elimination of outlier photographs has been adopted using a robust statistic. The limitations of the measurement system due to sampling, image processing and fines corrections are discussed and the errors estimated whenever possible. An analysis of consistency of the results based on the known amount of natural fines is made. Blasts with large differences in the amount of fines require a differentiated treatment, as the fine sizes tend to be the more underestimated in the image analysis as they become more abundant; this has been accomplished by means of a variable fines adjustment factor. Despite of the unavoidable errors and the large dispersion always associated with large-scale rock blasting data, the system is sensitive to relative changes in fragmentation.     

Application of soft computing in predicting rock fragmentation to reduce environmental blasting side effects

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.     

Prediction of blast fragmentation using multivariate analysis procedures

An extensive multivariate analysis procedure for prediction of blast fragmentation distribution is presented. Several blasts performed in various mines and rock formations in the world are brought together and evaluated. Blast design parameters, the modulus of elasticity, in situ block size are considered to perform multivariate analysis. The hierarchical cluster analysis is used to separate the blasts data into different groups of similarity. Group memberships were checked by the discriminant analysis. The multivariate regression analysis was applied to develop prediction equations for the estimation of the mean particle size of muckpiles. Two different prediction equations were developed based on the rock stiffness. Validation of the proposed equations on various mines is presented and the capability of the prediction equations was compared with one of the most applied fragmentation distribution models appearing in the blasting literature. Prediction capability of the proposed models was found to be strong. Diversity of the blasts data used is one of the most important aspects of the developed models. The models are not complex and suitable for practical use at mines. Copyright © 2010 John Wiley & Sons, Ltd.     

A Statistical Analysis of Fragmentation After Single Hole Bench Blasting

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.     

Development of Rock Engineering Systems-Based Models for Flyrock Risk Analysis and Prediction of Flyrock Distance in Surface Blasting

The environmental effects of blasting must be controlled in order to comply with regulatory limits. Because of safety concerns and risk of damage to infrastructures, equipment, and property, and also having a good fragmentation, flyrock control is crucial in blasting operations. If measures to decrease flyrock are taken, then the flyrock distance would be limited, and, in return, the risk of damage can be reduced or eliminated. This paper deals with modeling the level of risk associated with flyrock and, also, flyrock distance prediction based on the rock engineering systems (RES) methodology. In the proposed models, 13 effective parameters on flyrock due to blasting are considered as inputs, and the flyrock distance and associated level of risks as outputs. In selecting input data, the simplicity of measuring input data was taken into account as well. The data for 47 blasts, carried out at the Sungun copper mine, western Iran, were used to predict the level of risk and flyrock distance corresponding to each blast. The obtained results showed that, for the 47 blasts carried out at the Sungun copper mine, the level of estimated risks are mostly in accordance with the measured flyrock distances. Furthermore, a comparison was made between the results of the flyrock distance predictive RES-based model, the multivariate regression analysis model (MVRM), and, also, the dimensional analysis model. For the RES-based model, R ² and root mean square error (RMSE) are equal to 0.86 and 10.01, respectively, whereas for the MVRM and dimensional analysis, R ² and RMSE are equal to (0.84 and 12.20) and (0.76 and 13.75), respectively. These achievements confirm the better performance of the RES-based model over the other proposed models.     

Characterization of underground rock fragmentation

Summary This paper focuses on the methodology and techniques developed to characterize the rock fragments produced by blasting in an underground environment. This work formed part of an integrated approach to the optimization of blasting design at a Canadian mine. Details are given of the photographic and image analysis techniques adopted, together with data from a program of full scale, study blasts in the mine. Features of the observed fragmentation are reviewed which related to controlled variation in the blast designs, together with other factors which were observed both to influence fragmentation characteristics and to interact with loading equipment productivity.     

Stochastic Modeling Approach for the Evaluation of Backbreak due to Blasting Operations in Open Pit Mines

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.     

Fragmentation Assessment using the FragScan System: Quality of a Blast

Checking the quality of a blast may be considered as subjective. Checking this quality will require measuring objective parameters. One of them is the resulting fragmentation of the blasted product. Numerous fragmentation 'measuring' systems have been developed and marketed, based on image-processing. This presentation of FragScan will illustrate advantages and difficulties when using such a system. FragScan is essentially defending a policy of large and representative sampling. The purpose is to show how fragmentation is discriminating both productivity and profit of quarry operations on a blast-by blast basis. The next step will then be to 'drive' the blasting process to reach a 'better' fragmentation. Other applications require the fragmentation as an essential result: this is the case for large boulders used in structure-reinforcement. Several case-studies have shown that FragScan can be used for quality-control, checking size-distribution of the product according to the requirements of the end-user. Only clear thinking about the precise use of such a 'measurement' will further the success of this task.     

An RES-Based Model for Risk Assessment and Prediction of Backbreak in Bench Blasting

Most blasting operations are associated with various forms of energy loss, emerging as environmental side effects of rock blasting, such as flyrock, vibration, airblast, and backbreak. Backbreak is an adverse phenomenon in rock blasting operations, which imposes risk and increases operation expenses because of safety reduction due to the instability of walls, poor fragmentation, and uneven burden in subsequent blasts. In this paper, based on the basic concepts of a rock engineering systems (RES) approach, a new model for the prediction of backbreak and the risk associated with a blast is presented. The newly suggested model involves 16 effective parameters on backbreak due to blasting, while retaining simplicity as well. The data for 30 blasts, carried out at Sungun copper mine, western Iran, were used to predict backbreak and the level of risk corresponding to each blast by the RES-based model. The results obtained were compared with the backbreak measured for each blast, which showed that the level of risk achieved is in consistence with the backbreak measured. The maximum level of risk [vulnerability index (VI) = 60] was associated with blast No. 2, for which the corresponding average backbreak was the highest achieved (9.25 m). Also, for blasts with levels of risk under 40, the minimum average backbreaks (<4 m) were observed. Furthermore, to evaluate the model performance for backbreak prediction, the coefficient of correlation (R ²) and root mean square error (RMSE) of the model were calculated (R ² = 0.8; RMSE = 1.07), indicating the good performance of the model.     

Improving Collar Zone Fragmentation by Top Air-Deck Blasting Technique

Air gap in an explosive column has long been applied in open-pit blasting as a way of reducing explosive charge, vibration, fly rock and improve fragment size. In conventional blasting a greater amount of explosive energy is lost in the generation of oversize fragments. Oversize fragments reduces loading and hauling efficiencies of equipment which requires secondary blasting. Recurring oscillation of shock waves in the air gap increases the time over which it acts on the adjacent rock mass by factor of 2–5. Top air deck blasting technique trial conducted with an application of gas bags at Chimiwungo pit resulted in an improved fragmentation of about 94 % less than 950 mm. Results obtained from the analysis of muckpile images using split-desktop exhibited that the mean fragment size was 264.81 mm and F20, F80 and top-size were 41.99, 683.18 and 1454.69 mm respectively. Optimum crusher feed size was as large as 1200 mm and crushed down to the 40 mm and only a small percent of the material was above 1200 mm. Gas bag application resulted in a significant reduction in explosives load in production holes without loss in fragmentation or movement of the collar zone. This reduced total cost of charging as compared to conventional blasts with a variance of $20, powder factor was dropped to an average of 0.86 kg/bcm. The technique reduced the cost of bulk blend explosive by 15 %, reduced overall cost of charging per hole by 12 %, enhanced premature ejections. The overall blast results were satisfactory, 443,624 tonnes of blasted material from the block which represented 90 % of the total muckpile material was within 900 mm size. The overall muckpile blasted was well fragmented.     

Application of artificial intelligence techniques for predicting the flyrock distance caused by blasting operation

Flyrock arising from blasting operations is one of the crucial and complex problems in mining industry and its prediction plays an important role in the minimization of related hazards. In past years, various empirical methods were developed for the prediction of flyrock distance using statistical analysis techniques, which have very low predictive capacity. Artificial intelligence (AI) techniques are now being used as alternate statistical techniques. In this paper, two predictive models were developed by using AI techniques to predict flyrock distance in Sungun copper mine of Iran. One of the models employed artificial neural network (ANN), and another, fuzzy logic. The results showed that both models were useful and efficient whereas the fuzzy model exhibited high performance than ANN model for predicting flyrock distance. The performance of the models showed that the AI is a good tool for minimizing the uncertainties in the blasting operations.     

Class I and Class II Rocks: Implication of Self-sustaining Fracturing in Brittle Compression

Tests to determine the complete stress–strain curve of rocks indicate whether the rocks can be classified a Class I or Class II. Class II rocks exhibits the potential for self-sustained failure in the post-peak region. The purpose of the research described in this paper was to investigate whether or not this self-sustained failure characteristic is related to the fragmentation of the rock. The aim of the research was, therefore, to determine possible relationships between fragmentation and various properties of several rocks types, including the influence of the Class II characteristic. Fragmentation of rock depends on its self-sustaining failure behaviour and the energy available in the post-peak region to shatter the rock. The correlation of static and dynamic rock properties with size of fragments resulting from compression tests demonstrate clear relationships of Class II rocks, but the same cannot be said for Class I rocks. Analyses of test results show that fragmentation increases with an increase in rock strength, and is explosive for Class II rocks. Probability density distributions were constructed to show the overall comparison of fragment sizes produced during failure of Class II and Class rocks. The calculated probability of passing at X₅₀ and X₁₀ sieve sizes show that Class II rocks as a group are more finely fragmented. It can therefore be concluded that, when breaking rocks under the same steady loading conditions, Class II rocks will show greater fragmentation than Class I rocks.     

Strength,fragmentation and fractal properties of mixed flaws

Experiments on Portland cement samples containing mixed flaws are conducted to investigate the strength, fragmentation and fractal properties. Flaw geometry is a new combination of two edge-notched flaws and an imbedded flaw, which is different from those in the previous studies, where parallel or coplanar flaws are used. The physical implications of the shear-box test applied to result to rock slopes are studied. The physical and analytical fragmentation characteristics of preflawed samples are analyzed through the sieve test and fractal theory, respectively. Three different patterns of tensile cracks and shear cracks are observed. A sliding crack model is presented to elucidate the brittle failure flaws. In all of the cases of the shear-box tests, the coalescence is produced by the linkage of shear cracks, and two types of coalescence (Type C1 and Type C2) have been classified, which tend to confirm the observations from the numerical model and field of jointed rock slopes. The shear strength is a function of the flaw geometry and the shear–normal stress ratio. The result of sieve tests indicates that the fragment size distribution of fragments has the fractal property, providing a physical understanding of the fragmentation mechanism. The fragments under the shear-box test have fractal dimensions between 2.2 and 2.6, which are larger than those under the compression test but similar to those in the fault cores. The fragmentation in the case of Type C2 has a smaller fractal dimension, corresponding to a larger shear strength.     

Timing effects on the fragmentation of small scale blocks of granodiorite

A series of small scale tests, simulating multi-hole blasts have been performed to establish the effect of delays on blast fragmentation. The blasts were performed in high quality granodiorite blocks, which were cut from stone prepared by dimensional stone quarry operations. The pattern used was equilateral triangular, with a distance of 10.2 cm between boreholes, which had a diameter of 11 mm, were loaded with detonating cord and the coupling medium was water. The delays used were achieved using different lengths of detonating cord for the cases of delays between 0 and 100 μs between holes and a sequential blasting machine firing seismic detonators for larger delays up to 4 ms. All fragments were collected and screened. The experiments showed that the worst fragmentation was achieved with simultaneous initiation of all charges. Fragmentation improved with the delay time between holes up to 1 ms between holes. If the experiments are scaled up, the results show that in granodiorite, fragmentation optimization requires delays of few milliseconds per metre of burden. The findings, agree with previously published work, involving larger scale experiments and other rock types.     

Prediction of flyrock and backbreak in open pit blasting operation: a neuro-genetic approach

An ideally performed blasting operation enormously influences the mining overall cost. This aim can be achieved by proper prediction and attenuation of flyrock and backbreak. Poor performance of the empirical models has urged the application of new approaches. In this paper, an attempt has been made to develop a new neuro-genetic model for predicting flyrock and backbreak in Sungun copper mine, Iran. Recognition of the optimum model with this method as compared with the classic neural networks is faster and convenient. Genetic algorithm was utilized to optimize neural network parameters. Parameters such as number of neurons in hidden layer, learning rate, and momentum were considered in the model construction. The performance of the model was examined by statistical method in which absolutely higher efficiency of neuro-genetic modeling was proved. Sensitivity analysis showed that the most influential parameters on flyrock are stemming and powder factor, whereas for backbreak, stemming and charge per delay are the most effective parameters.     

Simulation of mineral grades and classification of mineral resources by using hard and soft conditioning data: application to Sungun porphyry copper deposit

The presence of geological units with different grade characteristics mostly leads to problems during the grade modeling process. In special cases, if the area under study has units with small thickness and low grade with respect to the dominant unit of the area, it is difficult to reproduce different grade contents in these units in the simulated grade models because of the low thickness and lack of data in these units. In this study, the local moment constraints method, based on the definition of soft conditioning data reflecting geological knowledge, is investigated for improving simulated grade models under the mentioned conditions. This method is applied for grade simulation at the 1,750 m level of Sungun porphyry copper mine. The studied area is divided into two rock type domains: Sungun porphyry and Dyke. The Sungun porphyry unit is the dominant rock type in the considered area and has, on average, a higher copper grade, while dykes discontinue Sungun porphyry rock units sporadically and most of them are barren of mineralization. It is demonstrated that the use of soft conditioning data makes the simulated grade model closer to reality and improves the reproduction of grade contents considering the rock type units in the area. In the next step, the results obtained from conditional simulation are used for mineral resources classification. To this end, the conditional coefficient of variation is chosen as a criterion for measuring uncertainty and for defining the resources classes. Then, it is shown that uncertainty can be considerably reduced in the prepared models if soft data are considered; as a result, an increase in measured resource classification is observed.     

硬岩隧道高压气体膨胀破岩开挖试验

为了解决传统钻爆法在隧道工程中振动大的问题,引入一种新型破岩技术--高压气体膨胀破岩技术。通过在某隧道掌子面采用该技术进行现场试验,获得该技术试验时的振动速度值和试验后的破岩效果,将获得的结果与传统钻爆法得到的相应结果进行对比分析,结果表明,高压气体膨胀破岩技术在施工时产生的振动比钻爆法小,证明了将该技术应用在隧道工程中是可行的,解决了该隧道采用钻爆法施工振动风险大的问题,为类似工程破岩提供了一种新途径。     

Artificial neural network or empirical criteria? A comparative approach in evaluating maximum charge per delay in surface mining — Sungun copper mine

Ground vibration due to blasting causes damages in the existence of the surface structures nearby the mine. The study of vibration control plays an important role in minimizing environmental effects of blasting in mines. Ground vibration regulations primarily rely on the peak particle velocity (PPV, mm/s). Prediction of maximum charge weight per delay (Q, kg) by distance from blasting face up to vibration monitoring point as well as allowable PPV was proposed in order to perform under control blasting and therefore avoiding damages on structures nearby the mine. Various empirical predictor equations have proposed to determine the PPV and maximum charge per delay. Maximum charge per delay is calculated by using PPV predictors indirectly or Q predictor directly. This paper presents the results of ground vibration measurement induced by bench blasting in Sungun copper mine in Iran. The scope of this study is to evaluate the capability of two different methods in order to predict maximum charge per delay. A comparison between two ways of investigations including empirical equations and artificial neural network (ANN) are presented. It has been shown that the applicability of ANN method is more promising than any under study empirical equations.