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1.
The present paper mainly deals with the prediction of maximum explosive charge used per delay (Q MAX) using an artificial neural network (ANN) incorporating peak particle velocity (PPV) and distance between blast face to monitoring point (D). One hundred and fifty blast vibration data sets were monitored at different vulnerable and strategic locations in and around major coal producing opencast coal mines in India. One hundred and twenty-four blast vibrations records were used for the training of the ANN model vis-à-vis to determine site constants of various conventional vibration predictors. The other 26 new randomly selected data sets were used to test, evaluate and compare the ANN prediction results with widely used conventional predictors. Results were compared based on coefficient of correlation (R), mean absolute error and mean squared between measured and predicted values of Q MAX. It was found that coefficient of correlation between measured and predicted Q MAX by ANN was 0.985, whereas it ranged from 0.316 to 0.762 by different conventional predictor equations. Mean absolute error and mean squared error was also very small by ANN, whereas it was very high for different conventional predictor equations.  相似文献   

2.
The present paper mainly with deals the prediction of safe explosive charge used per delay (QMAX) using support vector machine (SVM) incorporating peak particle velocity (PPV) and distance between blast face to monitoring point (D). 150 blast vibration data sets were monitored at different vulnerable and strategic locations in and around a major coal producing opencast coal mines in India. 120 blast vibrations records were used for the training of the SVM model vis-à-vis to determine site constants of various conventional vibration predictors. Rest 30 new randomly selected data sets were used to compare the SVM prediction results with widely used conventional predictors. Results were compared based on coefficient of correlation (R) between measured and predicted values of safe charge of explosive used per delay (QMAX). It was found that coefficient of correlation between measured and predicted QMAX by SVM was 0.997, whereas it was ranging from 0.063 to 0.872 by different conventional predictor equations.  相似文献   

3.
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.  相似文献   

4.
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   

5.
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 complexity of flyrock analysis. Existence of various effective parameters and their unknown relationships are the main reasons for inaccuracy of the empirical models. Presently, application of new approaches such as artificial intelligence is highly recommended. In this paper, an attempt has been made to predict and control flyrock in blasting operation of Sangan iron mine, Iran incorporating rock properties and blast design parameters using artificial neural network (ANN) method. A three-layer feedforward back-propagation neural network having 13 hidden neurons with nine input parameters and one output parameter were trained using 192 experimental blast datasets. It was also observed that in ascending order, blastability index, charge per delay, hole diameter, stemming length, powder factor are the most effective parameters on the flyrock. Reducing charge per delay caused significant reduction in the flyrock from 165 to 25 m in the Sangan iron mine.  相似文献   

6.
This study addresses the effects of rock characteristics and blasting design parameters on blast-induced vibrations in the Kangal open-pit coal mine, the Tülü open-pit boron mine, the K?rka open-pit boron mine, and the TKI Çan coal mine fields. Distance (m, R) and maximum charge per delay (kg, W), stemming (m, SB), burden (m, B), and S-wave velocities (m/s, Vs) obtained from in situ field measurements have been chosen as input parameters for the adaptive neuro-fuzzy inference system (ANFIS)-based model in order to predict the peak particle velocity values. In the ANFIS model, 521 blasting data sets obtained from four fields have been used (r 2 = 0.57–0.81). The coefficient of ANFIS model is higher than those of the empirical equation (r 2 = 1). These results show that the ANFIS model to predict PPV values has a considerable advantage when compared with the other prediction models.  相似文献   

7.
Blasting is a widely used technique for rock fragmentation in opencast mines and tunneling projects. Ground vibration is one of the most environmental effects produced by blasting operation. Therefore, the proper prediction of blast-induced ground vibrations is essential to identify safety area of blasting. This paper presents a predictive model based on gene expression programming (GEP) for estimating ground vibration produced by blasting operations conducted in a granite quarry, Malaysia. To achieve this aim, a total number of 102 blasting operations were investigated and relevant blasting parameters were measured. Furthermore, the most influential parameters on ground vibration, i.e., burden-to-spacing ratio, hole depth, stemming, powder factor, maximum charge per delay, and the distance from the blast face were considered and utilized to construct the GEP model. In order to show the capability of GEP model in estimating ground vibration, nonlinear multiple regression (NLMR) technique was also performed using the same datasets. The results demonstrated that the proposed model is able to predict blast-induced ground vibration more accurately than other developed technique. Coefficient of determination values of 0.914 and 0.874 for training and testing datasets of GEP model, respectively show superiority of this model in predicting ground vibration, while these values were obtained as 0.829 and 0.790 for NLMR model.  相似文献   

8.
Environmental impact of blasting at Drenovac limestone quarry (Serbia)   总被引：1，自引：1，他引：0  
In present paper, the blast-induced ground motion and its effect on the neighboring structures are analyzed at the limestone quarry "Drenovac" in central part of Serbia. Ground motion is examined by means of existing conventional predictors, with scaled distance as a main influential parameter, which gave satisfying prediction accuracy (R > 0.8), except in the case of Ambraseys–Hendron predictor. In the next step of the analysis, a feed-forward three-layer back-propagation neural network is developed, with three input units (total charge, maximum charge per delay and distance from explosive charge to monitoring point) and only one output unit (peak particle velocity). The network is tested for the cases with different number of hidden nodes. The obtained results indicate that the model with six hidden nodes gives reasonable predictive precision (R ≈ 0.9), but with much lower values of mean-squared error in comparison to conventional predictors. In order to predict the influence level to the neighboring buildings, recorded peak particle velocities and frequency values were evaluated according to United States Bureau of Mines, USSR standard, German DIN4150, Australian standard, Indian DMGS circular 7 and Chinese safety regulations for blasting. Using the best conventional predictor, the relationship between the allowable amount of explosive and distance from explosive charge is determined for every vibration standard. Furthermore, the effect of air-blast overpressure is analyzed according to domestic regulations, with construction of a blasting chart for the permissible amount of explosive as a function of distance, for the allowable value of air-blast overpressure (200 Pa). The performed analysis indicates only small number of recordings above the upper allowable limit according to DIN4150 and DMGS standard, while, for all other vibration codes the registered values of ground velocity are within the permissible limits. As for the air-blast overpressure, no damage is expected to occur.  相似文献   

9.
Prediction of blast-induced air overpressure using support vector machine   总被引：2，自引：1，他引：1  
Prediction of blast-induced air overpressure (AOP) is very complicated and intricate due to the number of influencing parameters affecting air wave propagation. In this paper, an attempt has been made to predict the blast-induced AOP by support vector machine (SVM) using maximum charge per delay and distance from blast-face to monitoring station of AOP. To investigate the suitability of this approach, SVM predictions are compared with a generalized predictor equation. Seventy-five air blasts were monitored at different locations around three mines. AOP data sets of two limestone mines are taken for the training and testing of the SVM network as well as to determine site constants for generalized equation. The remaining mine data sets are used for the validation and comparison of AOP.  相似文献   

10.
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   

11.
Ground vibrations produced from blasting operations cause structural vibrations, which may weaken structure if it occurs at the resonant frequency. Measurable parameters associated with ground vibrations are peak particle velocity (PPV), amplitude and dominant frequency (frequency of highest PPV amongst translational, vertical and horizontal vibrations). In this paper, an attempt is made to correlate measurable parameters associated with ground vibrations with scaled distance. Using the correlated data, it was found that a predictor equation can be determined for the amplitude and PPV, but not for dominant frequency as it is dynamic and depends upon infinitesimal changes that occur within a number of other parameters. Another analysis of the same is made using multiple linear regression analysis. This included predicting the PPV using scaled distance, maximum charge per delay, amplitude as predictors. A considerable improvement is seen in the prediction on adding the interaction of the predictors in multiple regressions. A comparison of different combination of predictors is made so as to assess the best combination giving the best R2 value for the given mine. Frequency is also plotted using the aforementioned method. However, it was found that the dominant frequency cannot be predicted with high accuracy even with this method.  相似文献   

12.
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   

13.
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   

14.
This study evaluates the impacts resulting from quarry-blasting operation on nearby buildings and structures as it generates ground vibration, air blast, and fly rocks. In this paper, first blasting operation and its possible environmental effects are defined. Then the methods of blast-vibration prediction and commonly accepted criteria to prevent damage were introduced. A field experimental work was conducted to minimize the vibration effects at Saribayir quarry as it is an identical case for the many quarries situated in and around Istanbul, Turkey. Although the local surrounding geology and rock mechanics have great influence on vibrations as uncontrollable parameter, the charge weight per delay, delay period, geometric parameters of the blasts were changed to solve the existing vibration problem in the studied quarry. To obtain a realistic result, 10 blasts were carried out and 30 seismic records were made in different places mainly very close the buildings and the other vulnerable structures around the quarry. The evaluation is performed whether the vibration level are within safe limits or not. The prediction equation based on scaled distance concept is also determined, however, it is a site-specific model and need to be updated when the quarry advances. The safe blast parameters which minimize the environmental effect were determined for the Saribayir quarry.  相似文献   

15.
In order to control or reduce the ground vibrations caused by underground blasts in Malmberget mine, a number of blast tests were carried out during production blasts and a series of single shot waveforms were obtained. Then the single shot waveforms from the same ring or different rings were analysed and compared with each other. The results showed that the single shots are reproducible, meaning that the ground vibrations caused by underground blasts can be controlled by means of the interference of the vibration waveforms measured. Finally, a formal test using electronic detonators and employing an optimum delay time of 8 ms was done in production. The test for an 11-borehole ring shows that the maximum vertical ground vibrations are reduced to the maximum vertical vibrations of a single shot. Particularly, the total vibration history for the 11-borehole-ring blast is shortened to about 200 ms over a velocity of 2 mm/s. However, the total vibration history of a normal production blast of 11-borehole ring is always 1400 ms over a velocity of 2 mm/s, namely the total vibration time of a production blast can be reduced to one seventh of that of the common production blasts by using the vibration control method. This indicates that the vibration control method introduced in the paper is feasible for underground mining blasts.  相似文献   

16.
Investigating the propagation and attenuation of blast vibration in rock slopes is the key point to assess the influence of underground mine blasting on overlaying open pit slopes stability and determining the potential risk. In this paper, Daye Iron Mine in China has been chosen as the case to study the effect of blast vibrations on overlaying open pit slopes due to underground mine blast. Firstly, the characteristics of blast loadings are analyzed by the dynamic finite element method. Then, a three dimensional (3D) numerical model of the open pit and the underground mine is made, which is verified by the field monitoring data to prove its reliability. The effect of blast vibration on overlaying open pit slope due to underground mine blasting are discussed based on the peak particle velocity (PPV) and the peak effective tensile stress (PETS) distribution characteristics which are calculated and analyzed by inputting the obtained blast vibration data into the numerical model. The results show that the effect of present mining blasting on the stability of pit slopes are limited because the simulated maximum PPV and PETS of monitoring point on slopes are all < 0.747 cm/s and 0.738 MPa. At last, according to numerical simulations of the underground mine blasting, the PPV predicting formulas for the slopes in Daye Open Pit Iron Mine is proposed based on the classic Sadaovsk formula.  相似文献   

17.
The aim of this study is to predict the peak particle velocity (PPV) values from both presently constructed simple regression model and fuzzy-based model. For this purpose, vibrations induced by bench blasting operations were measured in an open-pit mine operated by the most important magnesite producing company (MAS) in Turkey. After gathering the ordered pairs of distance and PPV values, the site-specific parameters were determined using traditional regression method. Also, an attempt has been made to investigate the applicability of a relatively new soft computing method called as the adaptive neuro-fuzzy inference system (ANFIS) to predict PPV. To achieve this objective, data obtained from the blasting measurements were evaluated by constructing an ANFIS-based prediction model. The distance from the blasting site to the monitoring stations and the charge weight per delay were selected as the input parameters of the constructed model, the output parameter being the PPV. Valid for the site, the PPV prediction capability of the constructed ANFIS-based model has proved to be successful in terms of statistical performance indices such as variance account for (VAF), root mean square error (RMSE), standard error of estimation, and correlation between predicted and measured PPV values. Also, using these statistical performance indices, a prediction performance comparison has been made between the presently constructed ANFIS-based model and the classical regression-based prediction method, which has been widely used in the literature. Although the prediction performance of the regression-based model was high, the comparison has indicated that the proposed ANFIS-based model exhibited better prediction performance than the classical regression-based model.  相似文献   

18.
Blasting operations usually produce significant environmental problems which may cause severe damage to the nearby areas. Air-overpressure (AOp) is one of the most important environmental impacts of blasting operations which needs to be predicted and subsequently controlled to minimize the potential risk of damage. In order to solve AOp problem in Hulu Langat granite quarry site, Malaysia, three non-linear methods namely empirical, artificial neural network (ANN) and a hybrid model of genetic algorithm (GA)–ANN were developed in this study. To do this, 76 blasting operations were investigated and relevant blasting parameters were measured in the site. The most influential parameters on AOp namely maximum charge per delay and the distance from the blast-face were considered as model inputs or predictors. Using the five randomly selected datasets and considering the modeling procedure of each method, 15 models were constructed for all predictive techniques. Several performance indices including coefficient of determination (R 2), root mean square error and variance account for were utilized to check the performance capacity of the predictive methods. Considering these performance indices and using simple ranking method, the best models for AOp prediction were selected. It was found that the GA–ANN technique can provide higher performance capacity in predicting AOp compared to other predictive methods. This is due to the fact that the GA–ANN model can optimize the weights and biases of the network connection for training by ANN. In this study, GA–ANN is introduced as superior model for solving AOp problem in Hulu Langat site.  相似文献   

19.
A number of factors influence the generation, propagation and intensity of ground vibrations. However, there are conflicting opinions with regard to the influence of the blast size on the intensity of ground vibrations. This paper discusses the experiments conducted in an opencast coal mine in India and a simulation study carried out to establish the influence of total charge in a blast on the intensity of ground vibrations. Studies clearly indicate that the total explosive charge in a blast has insignificant influence on the intensity of ground vibrations for distances between 100 m and 3000 m.  相似文献   

20.
准确圈定煤矿工作面底板突水预警重点监测区域,实现监测位置和潜在突水点位置在空间上的匹配,是突水灾害预警急需解决的问题之一。为研究煤矿工作面底板突水灾害预警重点监测区域评价技术,采用水文地质分析、GIS空间分析及ANN预测等技术手段,建立了底板突水灾害预警重点监测区域评价指标体系,提出了将不连续指标转化为连续指标的方法,建立了评价模型,研发了重点监测区域评价GIS系统,实现了煤矿底板突水灾害预警重点监测区域GIS与ANN耦合评价技术,最后以赵庄煤矿5303回采工作面底板突水监测预警为例,利用研发的系统圈定了该工作面重点监测区域。研究表明,确定预警重点监测区域的影响因素主要有含水层水压、含水层富水性、含水层防（隔）水煤岩柱厚度、老空区危险性指数、断层危险性指数、陷落柱危险性指数和封闭不良钻孔危险性指数,利用分段函数可以有效将不连续指标转化为连续指标,研发的评价系统可以实现煤矿突水灾害预警监测位置自动评价,评价结果与现场揭露及水害预警系统监测结果一致。   相似文献   

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