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 thermal conductivity of rocks by soft computing

The transfer of energy between two adjacent parts of rock mainly depends on its thermal conductivity. Knowledge of the thermal conductivity of rocks is necessary for the calculation of heat flow or for the longtime modeling of geothermal resources. In recent years, considerable effort has been made to develop artificial intelligence techniques to determine these properties. Present study supports the application of artificial neural network (ANN) in the study of thermal conductivity along with other intrinsic properties of rock due to its increasing importance in many areas of rock engineering, agronomy, and geoenvironmental engineering field. In this paper, an attempt has been made to predict the thermal conductivity (TC) of rocks by incorporating uniaxial compressive strength, density, porosity, and P-wave velocity using artificial neural network (ANN) technique. A three-layer feed forward back propagation neural network with 4-7-1 architecture was trained and tested using 107 experimental data sets of various rocks. Twenty new data sets were used for the validation and comparison of the TC by ANN. Multivariate regression analysis (MVRA) has also been done with same data sets of ANN. ANN and MVRA results were compared based on coefficient of determination (CoD) and mean absolute error (MAE) between experimental and predicted values of TC. It was found that CoD between measured and predicted values of TC by ANN and MVRA were 0.984 and 0.914, respectively, whereas MAE was 0.0894 and 0.2085 for ANN and MVRA, respectively.     

Artificial Neural Network as a Tool for Backbreak Prediction

Backbreak is one of the destructive side effects of the blasting operation. Reducing of this event is very important for economic of a mining project. Involvement of various parameters has made the backbreak analyzing difficult. Currently there is no any specific method to predict or control the phenomenon considering all the effective parameters. In this paper, artificial neural network (ANN) as a powerful tool for solving such complicated problems is used to predict backbreak in blasting operation of the Sangan iron mine, Iran. Network training was fulfilled using a collected database of the practiced operation including blast design details and rock condition. Trying various types of the networks, a network with two hidden layers was found to be optimum. Performance of the ANN model was compared with statistical analysis using datasets which were kept apart from the original database. According to the obtained results, for the ANN model there existed a higher correlation (R² = 0.868) and lesser error (RMSE = 0.495) between the predicted and measured backbreak as compared to the regression model. Also, sensitivity analysis revealed that the inputs rock factor and number of rows are the most and the least sensitive parameters on the output backbreak, respectively.     

Burden prediction in blasting operation using rock geomechanical properties

Burden prediction is a vital task in the production blasting. Both the excessive and insufficient burden can significantly affect the result of blasting operation. The burden which is determined by empirical models is often inaccurate and needs to be adjusted experimentally. In this paper, an attempt was made to develop an artificial neural network (ANN) in order to predict burden in the blasting operation of the Mouteh gold mine, using considering geomechanical properties of rocks as input parameters. As such here, network inputs consist of blastability index (BI), rock quality designation (RQD), unconfined compressive strength (UCS), density, and cohesive strength. To make a database (including 95 datasets), rock samples are used from Iran’s Mouteh goldmine. Trying various types of the networks, a neural network, with architecture 5-15-10-1, was found to be optimum. Superiority of ANN over regression model is proved by calculating. To compare the performance of the ANN modeling with that of multivariable regression analysis (MVRA), mean absolute error (E _a), mean relative error (E _r), and determination coefficient (R ²) between predicted and real values were calculated for both the models. It was observed that the ANN prediction capability is better than that of MVRA. The absolute and relative errors for the ANN model were calculated 0.05 m and 3.85%, respectively, whereas for the regression analysis, these errors were computed 0.11 m and 5.63%, respectively. Moreover, determination coefficient of the ANN model and MVRA were determined 0.987 and 0.924, respectively. Further, a sensitivity analysis shows that while BI and RQD were recognized as the most sensitive and effective parameters, cohesive strength is considered as the least sensitive input parameters on the ANN model output effective on the proposed (burden).     

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.     

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.     

A study of soft computing models for prediction of longitudinal wave velocity

Genetic algorithm (GA) and support vector machine (SVM) optimization techniques are applied widely in the area of geophysics, civil, biology, mining, and geo-mechanics. Due to its versatility, it is being applied widely in almost every field of engineering. In this paper, the important features of GA and SVM are discussed as well as prediction of longitudinal wave velocity and its advantages over other conventional prediction methods. Longitudinal wave measurement is an indicator of peak particle velocity (PPV) during blasting and is an important parameter to be determined to minimize the damage caused by ground vibrations. The dynamic wave velocity and physico-mechanical properties of rock significantly affect the fracture propagation in rock. GA and SVM models are designed to predict the longitudinal wave velocity induced by ground vibrations. Chaos optimization algorithm has been used in SVM to find the optimal parameters of the model to increase the learning and prediction efficiency. GA model also has been developed and has used an objective function to be minimized. A parametric study for selecting the optimized parameters of GA model was done to select the best value. The mean absolute percentage error for the predicted wave velocity (V) value has been found to be the least (0.258 %) for GA as compared to values obtained by multivariate regression analysis (MVRA), artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), and SVM.     

The use of air decks in production blasting in an open pit coal mine

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

Predicting elastic properties of schistose rocks from unconfined strength using intelligent approach

Elastic properties of rocks play a major and crucial role for the design of any engineering structure. Determination of elastic properties in laboratory is tedious, laborious, very time consuming, as well as expertise is required, whereas determination of uniaxial compressive strength (UCS) and tensile strength in laboratory is simple, easy, and less expertise is required. Here, an attempt has been made to predict the elastic properties (Poisson’s ratio and Young’s modulus) of the schistose rocks from unconfined strength (UCS and tensile strength) using artificial neural network (ANN). A three-layer feed-forward back propagation neural network with 2-5-2 architecture was trained up to 855 epochs to predict the elastic properties of rock mass. The network was trained and tested by 120 data sets, and validation of the network was done by 20 new randomly selected data sets of UCS and tensile strength. The samples were collected from the schistose rocks of Nathpa-Jhakri hydropower project site, SJVNL, Himachal Pradesh, India. To check the validity and suitability of the artificial neural network technique, multivariate regression analysis (MVRA) is also performed, and comparison has been made. It was found that ANN gives closer values of predicted Poisson’s ratio and Young’s modulus as compared to MVRA. The coefficient of determination for Poisson’s ratio was 0.9809 and 0.843 by ANN and MVRA, respectively, whereas 0.9922 and 0.9362 for Young’s modulus by ANN and MVRA, respectively. The mean absolute percentage error (MAPE) for Young’s modulus is 11.13 and 28.21 by ANN and MVRA, respectively; whereas MAPE for Poisson’s ratio is 3.64 and 9.23 by ANN and MVRA, respectively.     

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.     

河北某铁矿掘进爆破参数的优化与实践

针对河北某铁矿投产初期巷道掘进爆破效果差、效率低的问题,重点从炮孔间距和布置上对爆破参数进行了优化。生产实践证明,优化后的掘进爆破参数达到了提高巷道掘进效率、降低爆破成本的目的。优化后的爆破参数已在该矿实际生产中推广应用,为今后掘进爆破参数进一步优化积累了经验。     

Prediction and controlling of flyrock in blasting operation using artificial neural network

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.     

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.     

改进的支持向量机方法在矿山地质环境质量评价中的应用

传统支持向量机（SVM）评价模型中网格搜索法对参数的选择受到主观因素的影响,选用粒子群算法对SVM模型进行优化,并应用改进的SVM模型（PSO-SVM）对长吉图经济区135个矿山进行地质环境质量评价。PSO-SVM模型的评价结果与综合评价结果的相同率（注：该相同率是指两个评价结果相同的个数占所有评价样本数的百分比）达到95.56%,与SVM模型评价结果的相同率达到91.11%。结合研究区实际情况并分析三种评价结果得出,PSO-SVM模型的评价结果更符合实际情况。改进的支持向量机方法能够避免人为因素影响,提高矿山地质环境评价水平,在评价中具有可行性和有效性。基于改进的支持向量机方法评价结果表明,研究区矿山地质环境受矿山开采等人为活动的影响,54.1%的矿山遭受严重破坏（III级）,25.9%为中度破坏（II级）。评价结果可为研究区矿山环境恢复治理提供决策支持。     

Prediction of blast-induced air overpressure using support vector machine

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.     

深孔爆破装药结构优选数值分析方法及其应用

针对地下深孔爆破采矿过程中常出现的爆破后冲作用严重以及爆破块度不均匀等若干问题,以南方某铅锌矿实际采用的爆破、炸药和岩石参数为基础,采用ANSYS/LS-DYNA对矿山拟采用的不同装药量、不同耦合系数的6种装药结构建立了数值计算模型。通过分析爆炸仿真过程中的Von Mises有效应力信息,结合爆破破岩机制及Mises屈服理论,确定了深孔爆破的最佳炮孔装药结构。现场试验表明,优化的装药结构爆破块度均匀且爆破后冲作用得到有效控制,基本上解决了矿山深孔侧向爆破存在的问题,研究结果为深孔爆破的优化设计提供理论依据和技术支持。     

Prediction of ground vibrations resulting from the blasting operations in an open-pit mine by adaptive neuro-fuzzy inference system

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.     

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.     

Investigation of Instability and Blasting Caving Technology of Composite Support Structure in the Goaf of Gypsum Mine

According to the structure form of room and pillar goaf in gypsum mine, the mechanical model of pillar roof composite supporting structure was established in this research. Based on the cusp catastrophe theory and energy dissipation theory, the energy dissipation relationship of the support structure was analyzed, and a new instability criterion of the support system was derived by introducing the control parameters α and β. On this basis, the study of blasting caving was carried out. The influence of row spacing and hole depth on blasting caving was analyzed using ANSYS/LS DYNA software. The blasting influence range, stress wave attenuation and vibration velocity attenuation indices are obtained, and the blasting parameters such as the optimal distance and depth of blast holes between pillars and roof were optimized. Based on the results of theoretical analysis and numerical, combined with the engineering geological conditions of Dahan gypsum mine, the practical study of blasting caving was carried out. The caving scheme and caving sequence was determined, then the blasting caving effect was analyzed. The caving effect was found to be good, and the applicability of the theoretical model is verified.

     

基于GA-SVM的矿井涌水量预测

矿井涌水量的准确预测对预防矿山透水事故的发生至关重要,提出利用GA优化的SVM模型（GA-SVM）来实现矿井涌水量的短期准确预测。该方法利用GA的自动寻优功能寻找SVM的最佳参数,提高了预测的准确率。首先,利用微熵率法求矿井涌水量时间序列的最佳嵌入维数和延迟时间,进行相空间重构。其次,采集义煤集团千秋煤矿2011—2015年实际涌水量的时间序列,利用GA-SVM模型对最后12组数据进行预测,其预测平均绝对百分比误差仅为0.92%,最大相对误差为2.62%。最后,与PSO-SVM和BP神经网络预测进行对比,结果表明GA-SVM优化模型适用于矿井涌水量的预测并且预测精度较高。