Blast Design Parameters Research Articles

A Dynamic Image Analysis Method for Fragmentation Measurement in Blasting

Fragment size optimization with selection of best values of blast design variables is an important process in mine-mill fragmentation system to maximize the system performance. This calls for measurement and analysis of mean fragment size with respect to blast design parameters. Digital image analysis technique is the most accepted method for measurement of blasted fragment sizes and their distribution. For quick assessment of the fragment sizes, a new novel method based on the digital images extracted from a blast video is reported in this paper. Correction factor for the size of fragments, considering the face movement is also proposed. The method has been tested with the help of seven blast data sets. The proposed dynamic image analysis technique can not only be used in fragment size estimation but also to assess the time-progressive size reduction in a blast, which can help designing the delay timing. Further, a possibility to estimate the in-situ block size is also explored with this method. The images of blast fragmentation were extracted from their videos at an interval of 0.08 s. These images were analyzed later for measurement of mean fragment size at respective times. The fragment size of the complete muck generated by the blast was also measured and correlated well with the sizes achieved from video analysis. The analysis revealed that from 0.08 s to 0.56 s from the initiation of the blasts, the fragment size reduction progressed from 58% to 80% of the estimated in-situ rock block sizes. Significant effect of blast design variables and two firing patterns on the mean fragment size was also observed. The analysis suggested that V-type firing pattern provides finer fragment size in comparison to the diagonal firing pattern. The suggested method provides an easy yet fast way for the assessment of blast fragment size.

An Overview of Flyrock and its Prediction in Surface Mine Blasting using Soft Computing Techniques

The occurrence of flyrocks due to blasting has certainly gained tremendous attention by recent researchers as well as mine operators since time immemorial. With the increase in demand for the mineral and subsequent thrust on the surface mining operations, the mining projects are expanding their scale of operations. As such, not only the mines, but also the nearby inhabitants are endangered. So flyrock is one of the most hazardous side effects of blasting operation in surface mining. There are several empirical methods for predicting flying rocks. The poor performance of these different methods is due to the complexity and difficulty of rock analysis. The existence of various influential parameters and their unknown relationships are the main reasons for the inaccuracy of empirical models. In this light, the present paper gives an overview of state-of-art researches and their outcome in the area of control and prediction of flyrocks. The paper discusses the significant contribution of soft computing techniques in controlling and minimizing the flyrocks. Furthermore, it lays emphasis on scientific and categorical identification of most significant rock explosive and blasting design parameters in the prediction models to enhance their precision and universalization.

Prediction of blast-induced ground vibration using GPR and blast-design parameters optimization based on novel grey-wolf optimization algorithm

Prediction of blast-induced ground vibration using GPR and blast-design parameters optimization based on novel grey-wolf optimization algorithm

Blastability and Ore Grade Assessment from Drill Monitoring for Open Pit Applications

Blasting performance is influenced by mechanical and structural properties of the rock, on one side, and blast design parameters on the other. This paper describes a new methodology to assess rock mass quality from drill-monitoring data to guide blasting in open pit operations. Principal component analysis has been used to combine measurement while drilling (MWD) information from two drill rigs; corrections of the MWD parameters to minimize external influences other than the rock mass have been applied. First, a Structural factor has been developed to classify the rock condition in three classes (massive, fractured and heavily fractured). From it, a structural block model has been developed to simplify the recognition of rock classes. Video recording of the inner wall of 256 blastholes has been used to calibrate the results obtained. Secondly, a combined strength-grade factor has been obtained based on the analysis of the rock type description and strength properties from geology reports, assaying of drilling chips (ore/waste identification) and 3D unmanned aerial vehicle reconstructions of the post-blast bench face. Data from 302 blastholes, comprised of 26 blasts, have been used for this analysis. From the results, four categories have been identified: soft-waste, hard-waste, transition zone and hard-ore. The model determines zones of soft and hard waste rock (schisted sandstone and limestone, respectively), and hard ore zones (siderite rock type). Finally, the structural block model has been combined with the strength-grade factor in an overall rock factor. This factor, exclusively obtained from drill monitoring data, can provide an automatic assessment of rock structure, strength, and waste/ore identification.

Dust reduction method based on water infusion blasting in open-pit mines: a step toward green mining

ABSTRACT This research proposes a new dust reduction method based on water infusion blasting in order to effectively minimize the dust production during blasting in open-pit mines. The proposed method was applied at the Haerwusu open-pit coal mine, China. A high-speed camera was used to record the dynamic process of dust generation during blasting. The images acquired from the main locations of the dust were analyzed using the gray level recognition method in MATLAB. The proposed method was then numerically simulated using ANSYS/Fluent software. Finally, the field experiment was conducted to verify the method by analyzing its dust reduction efficiency. The numerical results show that the dust reduction rate of about 78% can be achieved when the water bag is filled at the height of 1.8 m according to the blast design parameters of the mine (burden = 11 m, spacing = 7 m, hole diameter = 0.25 m, hole depth = 20 m). The experimental results achieved a 75% dust reduction rate, which meets the requirements of reducing blasting dust pollution in the studied mine. These investigations and findings conclude that the proposed method is effective, technically feasible, and economical. It can be widely applied to other open-pit mines as a green mining technology.

Blasting pattern optimization using gene expression programming and grasshopper optimization algorithm to minimise blast-induced ground vibrations

Blast-induced ground vibration is considered as one of the most hazardous phenomena of mine blasting, which can even cause casualties and severe damages to the adjacent properties. Measuring peak particle velocity (PPV) is helpful to know the actual vibration level but prediction of blast vibration prior to the blast is a tedious job due to involvement of blast design, explosive and rock parameters. Nowadays, efficient application of intelligent systems has been approved in different branches of science and technology. In this paper, a gene expression programming (GEP) model was developed to predict PPV using various blasting patterns as model inputs, which showed a high level of accuracy for the implemented model. Also, to optimize blast pattern attaining minimum ground vibration during blasting operation, the developed functional GEP model was taken as objective function for grasshopper optimization algorithm (GOA). Construction of GOA model was performed using a trial and error mechanism to find out the best possible pertinent GOA parameters. Finally, it was observed that utilizing GOA technique, PPV can be reduced by 67% with optimized blast parameters including burden of 3.21 m, spacing of 3.75 m, and charge per delay of 225 kg. A sensitivity analysis was also performed to understand the influence of each input parameters on the blast vibrations.

PRELIMINARY ASSESSMENT OF THE EFFECTS OF BLAST DESIGN FACTORS ON FRAGMENTATION AT LAFARGE KANTHAN LIMESTONE QUARRY, CHEMOR, PERAK

The results of blasting affect every other downstream operation in quarrying and mining process. Factors influencing blast results can be classified as either controllable or non-controllable. If desired fragmentation is to be obtained, the controllable factors (blast geometry and explosive properties) must be sufficiently designed to match the non-controllable ones (geological factors and legislative constraints). This study investigates the influence of blast design parameters on rock fragmentation. Six different blast designs were studied and analyzed. Rock samples were obtained from each face to evaluate the uniaxial compressive strength (UCS). Images of muck pile were captured using suitable digital camera. The images were uploaded into the WipFrag software to analyze the fragmentation resulting from the blasting. The particle size distribution of each blast was obtained, and the mean fragment size correlated with the blast design parameters. The percentage cumulative passing for gyratory crusher with the feed size of 1500 mm ranges between 92.8 to 100%. The stiffness ratio, powder factor and uniaxial compressive strength have high correlation with mean fragment size. The stiffness ratio increases with mean fragment size with a correlation coefficient of 0.89. The mean fragment size becomes finer with increase in powder factor with a correlation coefficient of 0.76. Powder factor also has a high correlation with the uniaxial compressive strength of the rock. The higher the uniaxial compressive strength of rock, the higher the powder factor needed for a specified fragment size. In this study, spacing to burden ratio has a very weak correlation with the fragment size. All the studied blast events produced good fragmentation with a uniformity index varying from 2.097 to 2.525.

Prediction and minimization of blast-induced flyrock using gene expression programming and cuckoo optimization algorithm

Flyrock is one of the most dangerous adverse environmental effects of the blast, which can cause serious damage to the nearby buildings and people and even lead to the death in some cases. Therefore, it is very essential to correctly predict the flyrock distance to control and reduce the flyrock and also to determine the safety area. Due to the multiplicity of the parameters influencing the flyrock and the complexity of interacting parameters, the performance of empirical equations for predicting the flyrock distance is not satisfied and they cannot guaranty safety for the surrounding environment. In this research, in the first step, the predictor equation for the blast-induced flyrock was obtained using the gene expression programming. To achieve this goal, the data from 318 blast blocks in the Sungun copper mine was recorded and the amount of flyrock resulting from each blast was accurately measured. Burden, spacing, stemming, charge length and powder factor were used as the input parameters in gene expression programming (GEP) model. Among the gathered datasets, 268 datasets were intended for the model training and 50 datasets for the model testing. Then, the obtained equation from GEP was used as cost function for minimizing flyrock by cuckoo optimization algorithm. For this purpose, five strategies were defined and some optimal blast designs were provided for each strategy. The results showed that the gene expression programming model with the correlation coefficient of 0.91 and the root mean square error of 5.85 m has a good performance in predicting the blast-induced flyrock using the blast design parameters. Also, the maximum flyrock value of blast designs provided by the cuckoo algorithm was reduced by 43.6% relative to the maximum flyrock value of the initial blast designs. Input parameters sensitivity analysis showed that the powder factor is the most effective parameter on the flyrock phenomenon.

Predicting rock size distribution in mine blasting using various novel soft computing models based on meta-heuristics and machine learning algorithms

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.

Influence of void ratio on “Blast Pull” for different confinement factors of development headings in underground metalliferous mines

Development headings such as adits, ramps, haulages, cross-cuts, ore drives and exploratory drives are the important structures of any underground metalliferous mines. Drilling and blasting is the most preferred excavation method used to create such drivages for extracting mineral deposits, since the progress rate of drivages impacts the overall productivity of mines. There are many controllable blast design parameters and uncontrollable geological-geotechnical parameters influencing the blast pull (i.e. advancement factor). This paper investigates the influence of void ratio (i.e. the ratio between total area of empty holes and cut area of a first shot hole) on blast pull for development faces having different confinement factors. The confinement factor is defined as the ratio of drill hole length and cross-sectional area of development headings. For this study, field investigations were performed in four lead–zinc mines. Thereafter, experimental blasts were conducted in haulage drives on the footwall side, ore drives and exploratory drives on the hanging wall side, for different confinement factors ranging from 0.10 to 0.37. Several parallel hole cut designs were experimented upon, with the void ratios varying from 19.80% to 75%. Results revealed that the void ratio plays an important role in the advancement factor of development headings. Furthermore, with an increasing confinement factor, the void ratio should be kept high for achieving maximum blast pull. Statistical analysis was carried out to establish trends between the void ratio and the confinement factor of development faces using accuracy indicators of MAPE, MAD and MSD. Eventually, a relationship was established for the effective parallel hole cut designs to achieve more than 90% advancement factor.

Safety Analysis of Lining Structure Influenced by Blasting of Tunnel with Extralarge Section and Small Space

In order to ensure the safety of the Daling tunnel with extralarge section and small space, the three-dimensional model of blasting dynamic simulation had been established and verified. Then, the model was used to analyze the influence of the surrounding rock characteristics and blasting design parameters on the blasting vibration of the first hole, and the analysis of the sensitivity of each factor was carried out. The results showed that blasting of the second hole had a serious impact on the safety of the first hole lining structure. Based on the safety threshold and analysis of sensitivity, the explosive velocity, charge density, and digging length were selected as the key parameters affecting the safety of the tunnel structure. Meanwhile, the corresponding engineering measure was taken based on the results of sensitivity and the actual situation on site, and, after that, the maximum PPV of the right wall of the first hole had been reduced to 9.3 cm/s, which effectively guaranteed the safe construction of the tunnel.

Assessment of the effect of blasthole design parameters on total cost in quarries

Crushed stone is the most important raw material for concrete and asphalt production. As a high-volume low-unit-value industry, the crushed stone production sector is highly competitive which implies the necessity to optimise the costs of all of its production operations, i.e. drilling and blasting, secondary breaking, loading, hauling and crushing. In this context, this paper investigates the effect of bench blasting design parameters on the unit-costs and total production cost of aggregate production. For this purpose, 40 blasts were conducted and investigated. The studied parameters included different blasthole length (12 m, 15 m, 18 m, 21.5 m and 25 m) and diameter (89 mm and 102 mm), in addition to different spacing, burden and stemming length. As a result, it was found that an increase in blasthole length led to an increase in the unit cost of drilling operation. In addition, it was found that a blasthole length of 15 m ensured optimum fragmentation, diminishing the costs of subsequent operations. Incrementing the blasthole diameter from 89 to 102 mm reduced the total unit cost by 0.091 $/m3. Incrementing the burden and spacing by 0.25 m also decreased the total unit cost by 0.097 $/m3. However, accreting the stemming length by 0.5 m induced a slight decrease in the total unit cost by 0.026 $/m3.

Design and Realization of the Intelligent Design System for Tunnel Blasting in Mine Based on Database

With the improvement of informatization and standardization of mine development, it is an inevitable trend to apply computer technology to the intelligent design of tunnel blasting excavation. Seeking to the problems existing in the intelligent design system of blasting excavation, a new intelligent design system of blasting excavation is constructed from the perspective of database technology. The new intelligent design system adopts the design of central database system and subuser rights to meet the data synchronization and sharing among different users. At the same time, the new system also extracts blasting design parameters and actual engineering data and designs a more reasonable and comprehensive database structure, database logic, and data word table. Based on the T-S fuzzy neural network model, the intelligent search rules of excavation blasting data are also constructed in the new system. Finally, based on Oracle and VB.NET as the new system development platform, MFC, and ADO as the new system development technology, the new intelligent design system for tunnel blasting and excavation was completed. The implementation of the new system addresses the needs of blasting data information storage and search and lays a foundation for the informatization and standardization of tunnel excavation blasting.

Modelisation of fractured rock mass for open pit mining in Vietnam

In open pit mining, besides the value of the minerals obtained, the cost of waste rock removal determines the efficiency of the mining operation. The paper introduces the rock mass data processing method and modelisation of fractured rock mass to serve the optimal calculation of technological stages in mining. This code is a discrete fracture network (DFN) code that couple geometrical block system construction based on modelisation stochastic with RESOBLOK simulations. The method of three-dimensional modeling (3D) of the fractured rock mass in the quarry is based on the rock mass data, the geometrical parameters of the open pit mine. From there, the rock mass simulation models were used in the analysis of the stability analysis of open pit benches, the optimization of the blast design parameters at overburden benches, project construction materials, and technology projects for block stone extraction. An application in some open pit mines such as quarries of Vietnam is presented.

Comparison of the existing and calculated blast design parameters for the rock mass conditions at Bamesso-Latet rock quarry

Blasting is the most cost effective methodology to break rock for mining engineering applications. A good production blast will break only the rock that is needed to be removed, leaving the host rock with minimal damage. Accurate measurement of blast, fragmentation is important in mining and quarrying operations, in monitoring and optimizing their design. Currently, there are several methods available to predict damage due to blasting. The accuracy of many of these methods is questionable, and in most cases, the methodologies over predict the results. This paper presents a practical method (i.e. Langefors’s method) that we shall use in Bamesso-Latet rock quarry to do a comparison between the existing and calculated blast design parameters. The proposed method allows to assess the rock damage from blasting. It shows great potential as a practical aid to control and get a good quality of the fragmented material in Bamesso-Latet rock quarry.

Coupled geomechanical classification and multivariate statistical analysis approach for the optimization of blasting rock boulders

The prediction of blasting rock boulder in discontinuous rock is crucial for the optimization of blasting operations. Therefore, it is necessary to understand the role played by the geological features of a rock mass in controlling oversize fragments. This research is the result of coupled use of multivariate analysis methods and geomechanical indexes in order to identify the main rock mass and blast parameters that affect directly the oversize boulder production during blasting operations. The aggregate quarries selected for this study belong to the Eocene and the Jurassic rock in Tunisia. Diverse techniques were used as atomic absorption, XR diffraction, microscopic study, mechanical test, and scanning electron microscopy image to identify rock matrix. The methodology established by cluster analysis generated from mechanical classification as rock quality designation, rock mass rating, Q-Barton index, and strength index makes possible to classify the studied rock into three classes. A principal component analysis method developed in XLSAT 2018 has been performed on various blast design parameters to illustrate the relation between blasting and rock parameter. For the prediction of oversize fragment resulting from the blasts, a specific formula for every quarry was generated by statistical method. The proposed formulas can be considered as sufficient with an accuracy of more than 80% of the blasted rock after model testing compared with precise boulder percent in twenty blasts.

A hybrid paretosearch algorithm and goal attainment method for maximizing production and reducing blast-induced ground vibration: a blast design parameter selection approach

ABSTRACT This study uses a hybrid paretosearch algorithm and goal attainment method to determine blast design parameters that will maximize production (total blasted volume) and minimize blast-induced ground vibration. Fifty-five (51) blast data collected at Quarry X in the Western US are used. A nonlinear and linear regression models are developed to serve as the objective function for the minimization of blast-induced ground vibration and maximization of production respectively. The blast design parameters are then determined using the hybrid optimization technique. The results obtained show that hole depths of 11.58 m, burden of 4.24 m, spacing of 4.88 m and stemming length of 3.61 m are the blast design parameter values that produce a minimum PPV within the interval of 0.51 and 38.1 mm/sec as stated in the Explosive regulation by the Colorado State Division of Oil and Public Safety for a total blast volume greater than 16,622 m3.

Blast vibration dependence on total explosives weight in open-pit blasting

It is well established that the blast design parameters, namely explosives weight per delay, distance between the blast site and monitoring location, delay interval, total explosives detonated in a blasting round, velocity of detonation (VOD) of explosives, burden, spacing, explosive column length, top stemming length, number of decks and their length, transmitting media and its geology, and scattering in the delay time of detonators, influence blast-induced vibrations. A study was conducted to assess the effect of total weight of explosive detonated in the blasting round on the magnitude of blast vibrations at four big coal open-cast mines in India keeping all the parameters constant as stated above. Accordingly, experimental as well as production blasts were conducted at dragline and shovel benches. The results revealed that the magnitude of blast vibrations was influenced by the total amount of explosive detonated in a blasting round at shorter distances regardless of maximum explosives weight per delay. This paper describes the result of a study carried out to investigate these effects at open-cast projects in India. The study involved 60 blasts with varying blast designs and 498 vibration data were recorded.

Estimation of Fines Generation in Blasting Using Dynamic Rock Properties and a Near-Field PPV Damage Model

In the hard-rock mining industry, the generation of fines during blasting is an important issue because it has a significant effect on production processes. Although various approaches have been used to estimate fines generation in recent decades, as a practical matter, measuring the amount of fines is nearly impossible given the technical limitations and the scale of mine blasts. In this study, a dynamic PPV damage model was developed and applied to predict fines generation as a result of blasting. This modified version of the crushed zone model (CZM) considers dynamic rock properties and peak particle velocity (PPV), which represents the induced stress in blasting. Dynamic rock-fracturing tests were conducted using a split Hopkinson pressure bar (SHPB) to determine the parameters that represent the relationships between rock strength and strain rate as well as the relationships between fines amount and strain rate in the model. A case study was conducted that entailed determining the radius of the crushed zone for given blast designs and rock properties and then measuring the fines amount within this crushed zone. This case study indicated that the dynamic PPV model can provide a rough estimate of fines generation for given dynamic rock properties and blast design parameters and that this method has the potential to be a useful indicator, or index, of fines generation.

Predicting Blast-Induced Ground Vibrations in Some Indian Tunnels: a Comparison of Decision Tree, Artificial Neural Network and Multivariate Regression Methods

The present study compares three different techniques (decision tree, artificial neural network and multivariate regression analysis) for predicting blast-induced ground vibrations in some Indian tunnelling projects. The performance of these models was also compared to site-specific conventional predictor equations. A database consisting of 137 vibration records was randomly divided into training and testing sets for model generation. Eight input parameters (total charge, tunnel cross-section, maximum charge per delay, number of holes, hole diameter, distance from blasting face, hole depth and charge per hole) were selected for model development using bivariate correlation analysis. Results indicated that the decision tree is best suited for predicting vibrations. The decision tree further suggested that the intensity of near-field ground vibrations is mainly affected by total charge fired in a round, whereas the intensity of far-field vibrations is governed by maximum charge per delay and charge per hole. Conventional ground vibration predictors and machine learning techniques such as neural networks do not depict the relationship between input and output parameters. However, the present study substantiates that the decision tree can be a good tool for precise prediction of ground vibrations. Further, the decision tree can classify and relate different blast design parameters for refining blast designs to control ground vibrations on sites.