Blast Design Parameters Research Articles

Establishment of blasting design parameters influencing mean fragment size using state-of-art statistical tools and techniques

Abstract In the present study, principal component analysis (PCA) and stepwise selection and elimination (SSE) techniques were used to establish significant parameters (blasting design, rock and explosive) in surface coal mines by reducing dimensionality and variables from a host of blasting parameters. Mean fragment size (MFS) prediction models were subsequently developed using multiple linear regression (MLR) analysis technique. The two constructed and proposed models adequately selected relevant blast design, explosive and rock mass parameters. The performances of these models were assessed through the determination coefficient (R 2 ), F-ratio, standard error of estimate and root mean square error (RMSE). The PCA technique has shown good promise in eliminating the redundant parameters and in selecting relevant blast design parameters. Hierarchical cluster analysis technique was used for confirming the similarity of blasting design parameters in two trial blasting data set. The results were tested and validated with the 19 actual blast data set at acceptable correlation levels and have been illustrated in the form of figures, tables and graphs. MFS prediction equations based on PCA and SSE techniques were simple and suitable for practical use in overburden bench blasting of Indian coal mines.

The minimum response time in rock blasting: a dimensional analysis of full-scale experimental data

When designing large-scale blasting operations, great consideration should be given to tailor the fragment size distribution in order to fulfil production requirements, while ensuring at the same time efficient and economical downstream operations such as loading, hauling, crushing and grinding. The selection of an optimum inter-hole delay time has been found to have beneficial effects in controlling fragmentation and face movement. The minimum response time Tmin is reported in the literature as a key parameter for the selection of the appropriate inter-hole delay time. In this paper, data collected from 10 monitored full-scale blasts are used to investigate bivariate correlations between various design parameters. Further processing of the data using dimensional analysis resulted in the derivation of a mathematical relation that can estimate Tmin as a function of blast design parameters and the desired size reduction ratio of blasting.

Safety Concerns of Ancient Temple due to Blasting in Adjacent Mines

Directives from the Hon’ble Supreme Court of India led to the banning of mining activities within a radius of 2 km of the Sri Jambunatheswara ancient temple in Hospet taluk of Karnataka State of India. On recommendation of the Department of Archaeology & Museums, Government of Karnataka, CSIR-CIMFR undertook extensive investigations wherein the ground vibration and air overpressure due to blasting in nearby iron ore mines were monitored to assess their damage and annoyance potentials. The magnitudes of blast-induced ground vibration and air-overpressure recorded in the temple were found to be within the standard safe limits stipulated by the Directorate General of Mines Safety, India when trial blasts were carried out at a distance greater than 290 m from the temple. When blasts were conducted at a distance of beyond 845 m from the temple, neither vibration nor sound of blasting could be recorded or heard at the temple premises, indicating it a safe zone for blasting. After thorough analyses of the recorded data, precise blast design parameters were recommended for blasting at distances beyond 200 m from the temple and allowing this distance to be demarcated as the safe zone where controlled blasting could ensure safety of the ancient temple.

Assessment of backbreak due to blasting operation in open pit mines: a case study

One of the major problems related to blasting operations in open pit mines is the formation of cracks on the benches behind the last row of blast holes (backbreak). Prediction of backbreak plays an important role in the safety of open pit mining and the reduction of the detrimental effects resulting from blasting operations. In this study, we have tried to develop predictive models for anticipating backbreak. For this purpose, two of the most popular techniques, regression tree (RT) analysis and adaptive neuro-fuzzy inference system (ANFIS), were taken into account and a predictive model was constructed based on each. For training and testing of these models, a database including 175 blasting events in the Sungun Copper Mine (SCM), Iran, was used. These models predict backbreak based on the major blast design parameters (i.e., burden, spacing, stemming length, powder factor, and geometric stiffness ratio). It was found that both models can be used to predict the backbreak, but the comparison of two models, in terms of statistical performance indices, shows that the ANFIS model provides better results than the RT.

Rock fragmentation control in opencast blasting

The blasting operation plays a pivotal role in the overall economics of opencast mines. The blasting sub-system affects all the other associated sub-systems, i.e. loading, transport, crushing and milling operations. Fragmentation control through effective blast design and its effect on productivity are the challenging tasks for practicing blasting engineer due to inadequate knowledge of actual explosive energy released in the borehole, varying initiation practice in blast design and its effect on explosive energy release characteristic. This paper describes the result of a systematic study on the impact of blast design parameters on rock fragmentation at three mines in India. The mines use draglines and shovel–dumper combination for removal of overburden. Despite its pivotal role in controlling the overall economics of a mining operation, the expected blasting performance is often judged almost exclusively on the basis of poorly defined parameters such as powder factor and is often qualitative which results in very subjective assessment of blasting performance. Such an approach is very poor substitutes for accurate assessment of explosive and blasting performance. Ninety one blasts were conducted with varying blast designs and charging patterns, and their impacts on the rock fragmentation were documented. A high-speed camera was deployed to record the detonation sequences of the blasts. The efficiency of the loading machines was also correlated with the mean fragment size obtained from the fragmentation analyses.

Blastability evaluation for rock mass fragmentation in Iran central iron ore mines

In this research, we investigated the influence of rock mass properties, blast design parameters and explosive properties on blast fragmentation. Rock mass properties were evaluated in 51 blasting blocks using engineering geological mapping of 1961meters of the scanline, experiments on intact rock samples and measuring P-wave velocity (Vp) for 1771meters of seismic profiles. The results indicate that increasing spacing, persistence, opening, roughness, waviness of discontinuities, and Vp and uniaxial compressive strength (UCS) of intact rock as well as the increase of discontinuities angle with the bench face of blasting block will increase the size distribution of blasted rocks. In addition, evaluation of the influence of connector type, specific drilling and specific charge has shown that using the Nonel system will decrease the mean size of fragmentation. It is also demonstrated that increasing specific drilling and specific charge quantities will result in the increase of mean size of fragmentation.

Rock Mass Blastability Classification Using Fuzzy Pattern Recognition and the Combination Weight Method

Rock mass blastability classification provides a theoretical basis for rock mass blasting design, which is used to select blasting explosives, to estimate the unit explosive consumption, and to determine blasting design parameters. The primary factors that affect rock mass blastability were analyzed by selecting five indexes for rock mass blastability classification, that is, the rock Protodyakonov coefficient, rock tensile strength, rock density, rock wave impedance, and integrity coefficient of rock mass, and by identifying standards for the rock mass blastability classification and a method for testing the blasting classification indexes. The index weights were calculated using the combination weight method, which is based on game theory. A model for rock mass blastability classification was developed in combination with a fuzzy pattern recognition method. This classification method was applied to a Heidaigou open-pit coal mine, where mudstone, fine sandstone, medium sandstone, and coarse sandstone were determined to have a blastability degree of II, which corresponds to a blastability characterization of “easy,” and the unit explosive consumption of mudstone, fine sandstone, medium sandstone, and coarse sandstone was determined to be 0.44, 0.42, 0.40, and 0.36 kg/m3, respectively. These results were used to develop a loose blasting design that was effective for loose blasting.

Prediction of blast-induced flyrock in Indian limestone mines using neural networks

Frequency and scale of the blasting events are increasing to boost limestone production. Mines are approaching close to inhabited areas due to growing population and limited availability of land resources which has challenged the management to go for safe blasts with special reference to opencast mining. The study aims to predict the distance covered by the flyrock induced by blasting using artificial neural network (ANN) and multi-variate regression analysis (MVRA) for better assessment. Blast design and geotechnical parameters, such as linear charge concentration, burden, stemming length, specific charge, unconfined compressive strength (UCS), and rock quality designation (RQD), have been selected as input parameters and flyrock distance used as output parameter. ANN has been trained using 95 datasets of experimental blasts conducted in 4 opencast limestone mines in India. Thirty datasets have been used for testing and validation of trained neural network. Flyrock distances have been predicted by ANN, MVRA, as well as further calculated using motion analysis of flyrock projectiles and compared with the observed data. Back propagation neural network (BPNN) has been proven to be a superior predictive tool when compared with MVRA.

A Dimensional Analysis Approach to Study Blast-Induced Ground Vibration

The prediction of ground vibration is of great importance in the alleviation of the detrimental effects of blasting. Therefore, a vibration control study to minimize the harm of ground vibration and its influence on nearby structures can play an important role in the mining industry. In this paper, a dimensional analysis (DA) technique has been performed on various blast design parameters to propose a new formula for the prediction of the peak particle velocity (PPV). After obtaining the DA formula, 105 data sets were used to determine the unknown coefficients of the DA equation, as well as site constants of different conventional predictor equations. Then, 12 new blast data sets were used to compare the capability of the DA formula with conventional predictor equations. The results were compared based on the coefficient of determination and mean absolute error between measured and predicted values of the PPV.

Investigation of the effects of blasting design parameters and rock properties on blast-induced ground vibrations

This study addresses the effects of rock characteristics and blasting design parameters on blast-induced vibrations in the Kangal open-pit coal mine, the Tulu open-pit boron mine, and the Kirka open-pit boron mine. In this study, multiple vibration measurements have been conducted, and the related data have been analyzed and evaluated. Several artificial neural network (ANN) and regression models based on the same blasting design parameters, resistivity, and P-wave and S wave velocities of the surrounding rocks have been constructed to estimate the peak particle velocities and the frequencies of related blast-induced vibrations. The data derived from these models and the classical evaluations indicate that ANNs provide more reliable results than the other methods.

APPLICATION OF ARTIFICIAL NEURAL NETWORK FOR BLAST PERFORMANCE EVALUATION

Despite of technological advancement in the field of rock breakage, blasting is still an economical means of rock excavation for mining or civil engineering projects. Blasting has some environmental as well as societal side effects such as ground vibrations, air blasts, noises, back breaks, fly rocks, dusts and, of course, annoyance of inhabitants living surrounding the mining areas. Recent developments in the field of blasting techniques can minimize such ill effects by optimizing rock friendly explosive and blast design parameters. The purpose of this paper is to present the techniques, advances, problems and likely direction of future developments in exploring the applications of Artificial Neural Networks (ANN) in rock fragmentation by blasting and its significance in minimizing side effects to environment in particular and society at large. Many researchers have found the back-propagation algorithm in ANN is especially capable of solving predictive problems in rock blasting. ANN has been successfully applied in predicting, controlling, assessing impact of blast design parameters, ground vibrations, air blasts, back breaks etc. in mines. ANN needs to be applied in certain grey areas like predicting and controlling fly rock hazards in opencast mines, blast induced dust, blasting in jointed rockmass etc.

Apply Roof-Broken the Blasting Technique to Control Rock Burst

In working mining process of No.108 coal seam in Shengli Mine, the lower triangular goaf area hanging arch caused shock pressure. For this, The theoretical analysis do this weaken the roof through the roof-broken blasting technology, to optimize blasting design parameters, combined with the actual situation of the working surface to improve the blasting process, improve the top relief solve risk technology and solve the problem of rock burst. The application shows: successful implementation of advanced deep hole pre-splitting blasting to address a key challenge for charge and blasting technology, effective control of the roof power mutation occurs, improve the safety factor of the mining area, and provides an important mining work under similar conditions.

Magnitude of vibration triggering component determines safety of structures

Transmission of blast waves is a complex phenomenon and the characteristics vary with blast design parameters and geo-technical properties of medium. Frequency of vibration and triggering component for structural excitation generally quantifies safe vibration magnitude. At closer distance or higher elevations than the blast locations, vertical or transverse component will be the first arrival to trigger the sensor for monitoring and at far off distances longitudinal component triggers the sensor to monitor. Similarly, for shorter depth of blastholes and wider blast geometries, vertical or transverse component triggers the sensor to monitor even for longer distances of measurement. Analyzing the cause of such occurrence, the paper firstly puts forward a mathematical model to illustrate the same. Thereafter, considering single-degree of freedom for dynamic analysis of structures, the paper communicates that incident particle velocity exiting a structure to vibrate should be considered to limit vibration magnitude for safety of structures.

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.

Prediction of flyrock in open pit blasting operation using machine learning method

Flyrock is one of the most hazardous events in blasting operation of surface mines. There are several empirical methods to predict flyrock. Low performance of such models is due to the complexity of flyrock analysis. Existence of various effective parameters and their unknown relationships are the main reasons for inaccuracy of the empirical models. Presently, the application of new approaches such as artificial intelligence is highly recommended. In this paper, an attempt has been made to predict flyrock in blasting operations of Soungun Copper Mine, Iran incorporating rock properties and blast design parameters using support vector machine (SVM) method. To investigate the suitability of this approach, the predictions by SVM have been compared with multivariate regression analysis (MVRA), too. Coefficient of determination (CoD) and mean absolute error (MAE) were taken as performance measures. It was found that CoD between measured and predicted flyrock was 0.948 and 0.440 by SVM and MVRA, respectively, whereas MAE between measured and predicted flyrock was 3.11 and 7.74 by SVM and MVRA, respectively.

Comparison Between Neural Networks and Multiple Regression Analysis to Predict Rock Fragmentation in Open-Pit Mines

Keywords Rock fragmentation Neural networks Multiple regression Gol-e-Gohar iron mine Open-pit mines1 IntroductionBlasting is the most frequently used means of quarryingand mining rock excavation, and the quality of the frag-mentation of rock mass is a major concern of any blastingoperation (Latham and Lu 1999).The prediction and assessment of the rock size distri-bution produced by blasting are important concerns inunderstanding the blasting process. The rock fragmentationdistribution influences downstream processes such as loadand haul rates, crushing and grinding performance, andalso ore recovery in beneficiation processes (Michaud et al.1997). In the studies at Que´bec Cartier Mines, MacKenzie(1966) found that the efficiency of all the subsystems ofmining is dependent on the fragmentation. Additionally,uniform particle size distribution also eliminates the needfor secondary blasting of large boulders.It should be noted that many controllable and uncon-trollable factors influence rock fragmentation. Effectivefactors influencing fragmentation are classified into threecategories: blast design parameters, explosive properties,and rock mass properties.Burden, spacing between boreholes, bench height, drill-hole diameter, hole length, charge depth, stem height,subdrilling, drilling pattern (square or staggered), holeinclination (vertical or inclined), blasting direction, andblasting sequence (instantaneous or delayed) are blastdesign parameters, which are controllable. The secondgroup consists of explosive properties. Explosive type(ANFO, water gel, emulsion, or dynamite), its density(varies between 0.80 and 1.60 g/cm

Predicting blast-induced ground vibration using general regression neural network

Blasting is still an economical and viable method for rock excavation in mining and civil works projects. Ground vibration generated due to blasting is an undesirable phenomenon which is harmful for the nearby inhabitants and dwellings and should be prevented. In this study, an attempt has been made to predict the blast-induced ground vibration and frequency by incorporating rock properties, blast design and explosive parameters using the general regression neural network (GRNN) technique. To validate this methodology, the predictions obtained were compared with those obtained using the artificial neural network (ANN) model as well as by multivariate regression analysis (MVRA). Among all the methods, GRNN provides excellent predictions with a high degree of correlation.

Understanding main causes of nitrogen oxide fumes in surface blasting

Post-blast nitrogen oxide fumes (NOx) from surface blasting activities have become an important operational issue in Australian Coal mines. Post-blast fumes are a direct product of the detonation process which can be easily identified as the resultant yellow to orange post-blast clouds. There is general agreement that the conditions leading to fumes are associated with fuel deficiencies or incomplete detonation of the explosive product. From a practical perspective this can be due to one or a combination of factors such as explosive product characteristics, confinement effects, ground conditions, inappropriate blast design parameters, explosive product selection, on-bench practices and potential contamination of explosive product in the blasthole. This paper presents the preliminary results of a project funded by the Australian Coal Association Research Program (ACARP). The main objective of this project is to gain a better understanding of the principal causes of post-blast fumes at the operational level so that incidences can be minimised. Results from a comprehensive literature review, an industry survey and analysis of newly established blast registers have indicated that an appropriate matching of product to ground is essential to minimise fume incidences. For this to occur there must be a clear understanding of the potential impact of the characteristics of key components in any given product formulation and ANFO/Emulsion blends. Analysis has confirmed the impact of confinement and ground conditions as well as the potential contamination of product with fine drill cuttings near the stemming region. Trends associated with increases in the likelihood of fume incidences from long sleep times were identified but could not be entirely confirmed with the available data. The analysis on overburden blasts showed that it is fair to assume that the likelihood of high level fume incidences may increase when a product is slept for >10 days and that decisions to have product sleeping for longer periods should be supported by a good understanding of the product characteristics and ground conditions (geology and hydrogeology). Further work is required to build capabilities to verify on-bench product specifications and characteristics including the ability to measure in hole density changes, product moisture, verify AN/Emulsion blending ratios and undertake a more through characterisation of ground conditions. It is also important to extend the range of measurement systems to evaluate the performance of the detonation process in situ. To that end the authors have embarked in the development of instrumentation to better understand the impact of changing characteristics of explosive products in situ; a brief description and preliminary results from this research effort are also discussed in this paper.

Application of multivariate analysis for prediction of blast-induced ground vibrations

Prediction of ground vibration is of great importance in mitigation of blast-induced adverse effects. In this research, an innovative multivariate analysis procedure for prediction of blast-induced ground vibration is presented. Several blasts performed in a sandstone quarry were brought together and an extensive blast database is created. Blast design parameters, and the parameters related to ground vibration were considered to perform multivariate analysis. The hierarchical cluster analysis was used to separate the blasts data into different groups of similarity. Group memberships were analyzed and confirmed by the discriminant analysis. A linear discriminant equation was created to classify prospective blasts. Regression analysis was applied to develop prediction equations for the estimation of blast induced ground vibration. Validation analysis of the proposed equations was performed using test blasts. Prediction capability of the proposed models was found to be strong.

Investigation of the blast fragmentation using the mean fragment size and fragmentation index

An extensive multivariate analysis procedure for prediction of the blast fragmentation is presented. Several blasts performed in various mines and rock formations in the world are compiled and evaluated. Blast design parameters, modulus of elasticity, in situ block size are considered in the multivariate analysis. Two main approaches for prediction of blast fragmentation given in the literature are used for comparison. First, a mean fragment size prediction model is developed. Then fragmentation indices are determined for each blast and the fragmentation index prediction model is created using discriminant analysis method. Discriminant analysis played a key role during design stage of both models. The structure and prediction ability of the two models are compared and discussed. Models can be used separately or to complement each other. The models are applied to a wide range of test blasts. The models are not complex and suitable for practical use in mines.