Flyrock Distance Research Articles

Development of a precise model for prediction of blast-induced flyrock using regression tree technique

Drilling and blasting is the predominant rock fragmentation method in open-cast mines and civil construction works. Flyrock is one of the most hazardous effects caused by blasting operation. Therefore, the ability to make accurate predictions of the blast-induced flyrock is essential to reduce the environmental problems. This paper aimed to develop a precise and applicable model based on regression tree (RT) to predict blast-produced flyrock distance in Ulu Tiram quarry, Malaysia. In this regard, 65 blasting operations were investigated and the most influential factors on the flyrock, i.e. blast-hole length, spacing, burden, stemming, maximum charge used per delay and powder factor, were measured. Also, the flyrock distance values for the considered blasting events were carefully measured. In order to check the precision of the proposed RT model, multiple linear regression (MLR) model was also developed and both of the predictive models were compared. For this work, some statistical functions, i.e. median absolute error, coefficient of determination (R 2) and root mean squared error, were used and computed. The results revealed that the RT can be introduced as a powerful technique to predict flyrock distance and the proposed RT model can estimate flyrock distance better than MLR model. Also, sensitivity analysis was performed and it was found that the powder factor is the most influential parameter on the flyrock in the studied case.

Toward predicting blast-induced flyrock: a hybrid dimensional analysis fuzzy inference system

A hybrid dimensional analysis fuzzy inference system approach was introduced to predict blast-induced flyrock in surface mining, by integrating a dimensional analysis procedure and Mamdani’s fuzzy inference system. In the dimensional analysis, the blast-induced flyrock was considered as a function of the most effective parameters. Hence, a number of dimensionless products resulted and were used as input and output parameters of Mamdani’s fuzzy inference system. The capability of the hybrid approach was determined by comparing its results with the real measurement of flyrock in the case of a copper mine, based on a number of 320 in situ blasting datasets. Predictions by the system were close to the real measurements. Sensitivity analysis of the hybrid dimensional analysis fuzzy inference system showed that the most effective dimensionless products on flyrock distance were spacing, the multiplication of rock mass rating and hole length, and the subtraction of burden and hole length multiplication and stemming length.

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

The main objective of blasting operations is to provide proper rock fragmentation and to avoid undesirable environmental impacts such as flyrock. Flyrock is the source of most of the injuries and property damage in a majority of blasting accidents in surface mines. Therefore, proper prediction and subsequently optimization of flyrock distance may reduce the possible damages. The first objective of this study is to develop a new predictive model based on gene expression programming (GEP) for predicting flyrock distance. To achieve this aim, three granite quarry sites in Malaysia were investigated and a database composed of blasting data of 76 operations was prepared for modelling. Considering changeable GEP parameters, several GEP models were constructed and the best one among them was selected. Coefficient of determination values of 0.920 and 0.924 for training and testing datasets, respectively, demonstrate that GEP predictive equation is capable enough of predicting flyrock. The second objective of this study is to optimize blasting data for minimization purpose of flyrock. To do this, a new non-traditional optimization algorithm namely firefly algorithm (FA) was selected and used. For optimization purposes, a series of analyses were performed on the FA parameters. As a result, implementing FA algorithm, a reduction of about 34 % in results of flyrock distance (from 60 to 39.793 m) was observed. The obtained results of this study are useful to minimize possible damages caused by flyrock.

Genetic programming and gene expression programming for flyrock assessment due to mine blasting

This research is aimed to develop new practical equations to predict flyrock distance based on genetic programming (GP) and genetic expression programming (GEP) techniques. For this purpose, 97 blasting operations in Delkan iron mine, Iran were investigated and the most effective parameters on flyrock were recorded. A database comprising of five inputs (i.e. burden, spacing, stemming length, hole depth, and powder factor) and one output (flyrock) was prepared to develop flyrock distance. Several GP and GEP models were proposed to predict flyrock considering the modeling procedures of them. To compare the performance prediction of the developed models, coefficient of determination (R2), mean absolute error (MAE), root mean squared error (RMSE) and variance account for (VAF) were computed and then, the best GP and GEP models were selected. According to the obtained results, it was found that the best flyrock predictive model is the GEP based-model. As an example, considering results of RMSE, values of 2.119 and 2.511 for training and testing datasets of GEP model, respectively show higher accuracy of this model in predicting flyrock, while, these values were obtained as 5.788 and 10.062 for GP model.

Prediction of blast-induced flyrock using differential evolution algorithm

Flyrock is an undesirable phenomenon in blasting operations. Due to high potential to cause damage to machinery and nearby structures and to cause injuries, even fatal, to personnel, flyrock is the most dangerous adverse effect of blasting. For controlling and decreasing the effect of this phenomenon, it is necessary to predict it. Because of multiplicity of effective parameters and complexity of interactions among these parameters, empirical methods may not be fully appropriate for flyrock estimation. The scope of this study is to predict flyrock induced by blasting through a novel approach based on the combination of differential evaluation algorithm (DE) and dimensional analysis algorithm (DA). For this purpose, the parameters of 300 blasting operations were accurately recorded and flyrock distances were measured for each operation. In the next stage, two new empirical predictors were developed to predict flyrock distance. The results clearly showed the superiority of the proposed DE---DA model in comparison with the empirical approaches.

Application of PSO to develop a powerful equation for prediction of flyrock due to blasting

Drilling and blasting is a widely-used method for rock fragmentation in open-pit mines, tunneling and civil projects. Flyrock, as one of the most dangerous effects induced by blasting, can cause substantial damage to structures and injury to human. Therefore, the ability to make proper predictions of flyrock distance is important to reduce and minimize the environmental side effects caused by blasting operation. The main goal of the present research is to develop a precise equation for predicting flyrock through particle swarm optimization (PSO) approach. For comparison purpose, multiple linear regression (MLR) was also used. In this regard, a database including several controllable blasting parameters was collected from 76 blasting events in three quarry sites, Malaysia. In modeling procedures, five effective parameters on the flyrock including burden, spacing, stemming, powder factor and rock density were used as input parameters, while flyrock was considered as output parameter. In order to check the performance of the developed models, several statistical functions, i.e., root-mean-square error, Nash and Sutcliffe and coefficient of multiple determination (R2), were computed. The results revealed that the proposed PSO equation is more reliable than MLR in predicting the flyrock. Based on sensitivity analysis results, it was also found that the RD was the most effective parameter on the flyrock in the studied cases.

Risk Assessment and Prediction of Flyrock Distance by Combined Multiple Regression Analysis and Monte Carlo Simulation of Quarry Blasting

Flyrock is considered as one of the main causes of human injury, fatalities, and structural damage among all undesirable environmental impacts of blasting. Therefore, it seems that the proper prediction/simulation of flyrock is essential, especially in order to determine blast safety area. If proper control measures are taken, then the flyrock distance can be controlled, and, in return, the risk of damage can be reduced or eliminated. The first objective of this study was to develop a predictive model for flyrock estimation based on multiple regression (MR) analyses, and after that, using the developed MR model, flyrock phenomenon was simulated by the Monte Carlo (MC) approach. In order to achieve objectives of this study, 62 blasting operations were investigated in Ulu Tiram quarry, Malaysia, and some controllable and uncontrollable factors were carefully recorded/calculated. The obtained results of MC modeling indicated that this approach is capable of simulating flyrock ranges with a good level of accuracy. The mean of simulated flyrock by MC was obtained as 236.3 m, while this value was achieved as 238.6 m for the measured one. Furthermore, a sensitivity analysis was also conducted to investigate the effects of model inputs on the output of the system. The analysis demonstrated that powder factor is the most influential parameter on fly rock among all model inputs. It is noticeable that the proposed MR and MC models should be utilized only in the studied area and the direct use of them in the other conditions is not recommended.

Development of a new model for predicting flyrock distance in quarry blasting: a genetic programming technique

This research was aimed at developing a new model to predict flyrock distance based on a genetic programming (GP) technique. For this purpose, six granite quarry mines in the Johor area of Malaysia were investigated, for which various controllable blasting parameters were recorded. A total of 262 datasets consisting of six variables (i.e., powder factor, stemming length, burden-to-spacing ratio, blast-hole diameter, maximum charge per delay, and blast-hole depth) were collected applied to developing the flyrock predictive model. To identify the optimum model, several GP models were developed to predict flyrock. In the same way, using non-linear multiple regression (NLMR) analysis, various models were established to predict flyrock. Finally, to compare the performance of the developed models, regression coefficient (R2), root mean square error (RMSE), variance account for (VAF), and simple ranking methods were computed. According to the results obtained from the test dataset, the best flyrock predictive model was found to be the GP based model, with R2 = 0.908, RMSE = 17.638 and VAF = 89.917, while the corresponding values for R2, RMSE and VAF for the NLMR model were 0.816, 26.194, and 81.041, respectively.

Simultaneous prediction of blast-induced flyrock and fragmentation in opencast limestone mines using back propagation neural network

Goal of blasting operations is to achieve desired fragment size to operate the mine and plant economically while maintaining safety that includes prevention of flyrock accidents. This paper focuses on the simultaneous prediction of flyrock distance and fragmentation using back propagation neural network techniques. Thus, linear charge concentration, burden, spacing, stemming length, specific charge, unconfined compressive strength and rock quality designation are taken as input. Flyrock distance and fragment size are chosen as output. The predicted outputs by back propagation neural network (BPNN), multi variate regression analysis (MVRA) have been compared. The quite lower root mean square error (RMSE) and mean absolute error (MAE) in BPNN than MVRA prove that BPNN is a better prediction method. Also, the predicted output in BPNN correlates better with the observed output than MVRA. Sensitivity analysis for both independent variables for BPNN and MVRA is also included in this paper.

Prediction of Flyrock Distance in Open Pit Blasting Using Surface Response Analysis

Flyrock is a rock thrown to greater distance than desired and is a dangerous and unwanted phenomenon in surface mines, particularly, when blasting is proceeding close to human occupation and dwellings. The prediction of flyrock distance is critical in defining the statutory danger zone of blasting and has evaded blasters for quite some time. Control of flyrock with its distance prediction involves identification of key variables and understanding their influence. Theoretical models though provide a good understanding of the phenomenon, the confidence that can be assigned to such models is still very less. This study presents novel method to identify, merge and consolidate independent variables into a simplified equation for flyrock distance prediction without compromising on the actual field applications. Field investigations were carried out in several mines and relevant data were generated relating to flyrock. The key parameters, namely, explosive, blast design and rock mass nature were characterized and analysed. An empirical model involving the key contributors for flyrock generation and distance prediction were assimilated and a new equation was developed based on actual data collected by employing surface response analysis. The developed model was found to be statistically significant and validated. Sensitivity analysis was conducted to ascertain the role of independent factors on flyrock distance.

Estimating flyrock distance in bench blasting through blast induced pressure measurements in rock

Estimating flyrock distance in bench blasting through blast induced pressure measurements in rock

Development of a novel flyrock distance prediction model using BPNN for providing blasting operation safety

One of the threatening safety problems in mines is flyrock distance range through blasting operation. Inaccurate evaluation of flyrock can cause fatal and nonfatal accidents. The presented results in this paper verify efficiency of artificial neural network in prediction of flyrock considering all influencing parameters such as: hole diameter, height, subdrilling, number of holes, spacing, burden, ANFO amount, dynamite weight, stemming, powder factor, specific drilling, and delay time. In this research, optimum structure of network was determined by studying different transfer functions and number of the neurons using a programming code. In this case, optimum structure configuration is logsig transfer functions for the two hidden layers and tansig or logsig one for output, and there are eight neurons in each hidden layers. By calculating strength of relationship between flyrock and all influencing parameters using cosine amplitude method (CAM), the powder factor is defined as most effective parameter on the flyrock.

Prediction of Blast-Induced Flyrock in Opencast Mines Using ANN and ANFIS

The aim of present study is prediction of blast-induced flyrock distance in opencast limestone mines using artificial intelligence techniques such as artificial neural network (ANN) and adaptive neuro fuzzy inference system (ANFIS). Blast design and geotechnical variables such as linear charge concentration, burden, stemming length, specific charge, unconfined compressive strength, and rock quality designation have been selected as independent variables and flyrock distance has been used as dependent variable. Blasts required for the study purpose have been conducted in four limestone mines in India. Out of one hundred and twenty-five (125) blasts, dataset of one hundred blasts have been used for training, testing and validation of the ANN and ANFIS based prediction model. Twenty-five (25) data have been used for evaluation of the trained ANN and ANFIS models. In order to know the relationship among the independent and dependent variables, multi-variable regression analysis (MVRA) has also been performed. The performance indices such as root mean square error (RMSE), mean absolute error (MAE) and coefficient of determination (R2) have been evaluated for ANN, ANFIS and MVRA. RMSE as well as MAE have been found lower and R2 has been found higher in case of ANFIS in comparison of ANN and MVRA. ANFIS has been found a superior predictive technique in comparison to ANN and MVRA. Sensitivity analysis has also been performed using ANFIS to assess the impact of independent variables on flyrock distance.

Evaluation and prediction of flyrock resulting from blasting operations using empirical and computational methods

Mines, quarries and construction sites face environmental impacts, such as flyrock, due to blasting operations. Flyrock may cause damage to structures and injury to human. Therefore, flyrock prediction is required to determine safe blasting zone. In this regard, 232 blasting operations were investigated in five granite quarries, Malaysia. Blasting parameters comprising maximum charge per delay and powder factor were prepared to predict flyrock using empirical and intelligent methods. An empirical graph was proposed to predict flyrock distance for different powder factor values. In addition, using the same datasets, two intelligent systems, namely artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) were used to predict flyrock. Considering some model performance indices including coefficient of determination (R2), value account for and root mean squared error and also using simple ranking procedure, the best flyrock prediction models were selected. It was found that the ANFIS model can predict flyrock with higher performance capacity compared to ANN predictive model. R2 values of testing datasets are 0.925 and 0.964 for ANN and ANFIS techniques, respectively, suggesting the superiority of the ANFIS technique in predicting flyrock.

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 novel approach for blast-induced flyrock prediction based on imperialist competitive algorithm and artificial neural network.

Flyrock is one of the major disturbances induced by blasting which may cause severe damage to nearby structures. This phenomenon has to be precisely predicted and subsequently controlled through the changing in the blast design to minimize potential risk of blasting. The scope of this study is to predict flyrock induced by blasting through a novel approach based on the combination of imperialist competitive algorithm (ICA) and artificial neural network (ANN). For this purpose, the parameters of 113 blasting operations were accurately recorded and flyrock distances were measured for each operation. By applying the sensitivity analysis, maximum charge per delay and powder factor were determined as the most influential parameters on flyrock. In the light of this analysis, two new empirical predictors were developed to predict flyrock distance. For a comparison purpose, a predeveloped backpropagation (BP) ANN was developed and the results were compared with those of the proposed ICA-ANN model and empirical predictors. The results clearly showed the superiority of the proposed ICA-ANN model in comparison with the proposed BP-ANN model and empirical approaches.

Blasting-induced flyrock and ground vibration prediction through an expert artificial neural network based on particle swarm optimization

Blasting is a major component of the construction and mining industries in terms of rock fragmentation and concrete demolition. Blast designers are constantly concerned about flyrock and ground vibration induced by blasting as adverse and unintended effects of explosive usage on the surrounding areas. In recent years, several researches have been done to predict flyrock and ground vibration by means of conventional backpropagation (BP) artificial neural network (ANN). However, the convergence rate of the BP-ANN is relatively slow and solutions can be trapped at local minima. Since particle swarm optimization (PSO) is a robust global search algorithm, it can be used to improve ANNs' performance. In this study, a novel approach of incorporating PSO algorithm with ANN has been proposed to eliminate the limitation of the BP-ANN. This approach was applied to simulate the flyrock distance and peak particle velocity (PPV) induced by blasting. PSO parameters and optimal network architecture were determined using sensitivity analysis and trial and error method, respectively. Finally, a model was selected, and the proposed model was trained and tested using 44 datasets obtained from three granite quarry sites in Malaysia. Each dataset involved ten inputs, including the most influential parameters on flyrock distance and PPV, and two outputs. The results indicate that the proposed method is able to predict flyrock distance and PPV induced by blasting with a high degree of accuracy. Sensitivity analysis was also conducted to determine the influence of each parameter on flyrock distance and PPV. The results show that the powder factor and charge per delay are the most effective parameters on flyrock distance, whereas sub-drilling and charge per delay are the most effective parameters on PPV.

Regression and Fitting Analysis of Flyrock Prediction Formulae for Loosening Blasting

With so many complex influence factors of blasting flyrock, there is no critical formula for prediction the flying distance of blasting flyrock which was adapted by most of the scholars. Widely existing influential prediction formulas are mainly aimed at ordinary blasting technology, using the statistical law or mechanics analysis to get the prediction. The calculated data for flyrock distance are different distinctly. Predictive values are different largely between various formulas. Moreover, when loose blasting, the values predicted by those formulas will be larger than the actual data. This situation wastes lots of human and financial resources for blasting alert. In view of the present situation, the field experiment of loosening blasting was carried and the impacts of blasting parameters to the flyrock distance in loosening blasting were considered respectively. Some blasting parameters were regressed to get a prediction formula. The correlation analysis indicates that the formula for loosening blasting is good for flying distance.

Analysis of the Movement Trajectory of Blasting Flyrock in the Bottom of Open Pits

The main hazards caused by blasting include vibration, flyrock, air shock wave, noise and dust. And the blasting flyrock is one of the dangerous factors to surrounding environment. Small and medium open pits are usually surrounded with residential districts, so controlling the casting distance of flyrock is very important. It is difficult to predict the casting direction and flying distance of blasting flyrock because of its more influence factors. Based on Sihe stope of Fangmayu Iron Mine, the paper analyses the causes, movement rule and casting direction of the bottom flyrock of open pit caused by bench blasting, calculates the casting distance of bottom flyrock, obtains the actual casting initial velocity and the biggest safe distance of blasting flyrock in Sihe stope. The study results can provide references for reducing the flyrock harzards of open pit caused by bench blasting.

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

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