Prediction Of Air Overpressure Research Articles

Prediction of blast-induced air overpressure using a hybrid machine learning model and gene expression programming (GEP): A case study from an iron ore mine

<abstract> <p>Mine blasting can have a destructive effect on the environment. Among these effects, air overpressure (AOp) is a major concern. Therefore, a careful assessment of the AOp intensity should be conducted before any blasting operation in order to minimize the associated environmental detriment. Several empirical models have been established to predict and control AOp. However, the current empirical methods have many limitations, including low accuracy, poor generalizability, consideration only of linear relationships among influencing parameters, and investigation of only a few influencing parameters. Thus, the current research presents a hybrid model which combines an extreme gradient boosting algorithm (XGB) with grey wolf optimization (GWO) for accurately predicting AOp. Furthermore, an empirical model and gene expression programming (GEP) were used to assess the validity of the hybrid model (XGB-GWO). An analysis of 66 blastings with their corresponding AOp values and influential parameters was conducted to achieve the goals of this research. The efficiency of AOp prediction methods was evaluated in terms of mean absolute error (MAE), coefficient of determination (R<sup>2</sup>), and root mean square error (RMSE). Based on the calculations, the XGB-GWO model has performed as well as the empirical and GEP models. Next, the most significant parameters for predicting AOp were determined using a sensitivity analysis. Based on the analysis results, stemming length and rock quality designation (RQD) were identified as two variables with the greatest influence. This study showed that the proposed XGB-GWO method was robust and applicable for predicting AOp driven by blasting operations.</p> </abstract>

Forecast of Airblast Vibrations Induced by Blasting Using Support Vector Regression Optimized by the Grasshopper Optimization (SVR-GO) Technique

Air overpressure (AOp) is an undesirable environmental effect of blasting. To date, a variety of empirical equations have been developed to forecast this phenomenon and prevent its negative impacts with accuracy. However, the accuracy of these methods is not sufficient. In addition, they are resource-consuming. This study employed support vector regression (SVR) optimized with the grasshopper optimizer (GO) algorithm to forecast AOp resulting from blasting. Additionally, a novel input selection technique, the Boruta algorithm (BFS), was applied. A new algorithm, the SVR-GA-BFS7, was developed by combining the models mentioned above. The findings showed that the SVR-GO-BFS7 model was the best technique (R2 = 0.983, RMSE = 1.332). The superiority of this model means that using the seven most important inputs was enough to forecast the AOp in the present investigation. Furthermore, the performance of SVR-GO-BFS7 was compared with various machine learning techniques, and the model outperformed the base models. The GO was compared with some other optimization techniques, and the superiority of this algorithm over the others was confirmed. Therefore, the suggested method presents a framework for accurate AOp prediction that supports the resource-saving forecasting methods.

Intelligence Prediction of Some Selected Environmental Issues of Blasting: A Review

Background: Blasting is commonly used for loosening hard rock during excavation for generating the desired rock fragmentation required for optimizing the productivity of downstream operations. The environmental impacts resulting from such blasting operations include the generation of flyrock, ground vibrations, air over pressure (AOp) and rock fragmentation. Objective: The purpose of this research is to evaluate the suitability of different computational techniques for the prediction of these environmental effects and to determine the key factors which contribute to each of these effects. This paper also identifies future research needs for the prediction of the environmental effects of blasting operations in hard rock. Methods: The various computational techniques utilized by the researchers in predicting blasting environmental issues such as artificial neural network (ANN), fuzzy interface system (FIS), imperialist competitive algorithm (ICA), and particle swarm optimization (PSO), were reviewed. Results: The results indicated that ANN, FIS and ANN-ICA were the best models for prediction of flyrock distance. FIS model was the best technique for the prediction of AOp and ground vibration. On the other hand, ANN was found to be the best for the assessment of fragmentation. Conclusion and Recommendation: It can be concluded that FIS, ANN-PSO, ANN-ICA models perform better than ANN models for the prediction of environmental issues of blasting using the same database. This paper further discusses how some of these techniques can be implemented by mining engineers and blasting team members at operating mines for predicting blast performance.

Stochastic fractal search-tuned ANFIS model to predict blast-induced air overpressure

Air overpressure (AOp) induced by rock blasting is an undesirable phenomenon in open-pit mines and civil construction works. The prediction of AOp has been always a complicated task since many parameters have potential to affect the propagation of air waves. This study aims to assess the capability of a new hybrid evolutionary model based on an integrated adaptive neuro-fuzzy inference system (ANFIS) with a stochastic fractal search (SFS) algorithm. To assess the reliability and acceptability of ANFIS-SFS model, the particle swarm optimization (PSO) and genetic algorithm (GA) were also combined with ANFIS. The proposed models were developed using a comprehensive database including 62 sets of data collected from four granite quarry sites in Malaysia. Performances of the ANFIS-SFS, ANFIS-GA, and ANFIS-PSO models were checked using statistical functions as the performance criteria. The obtained results showed that the proposed ANFIS-SFS model, with root mean square error of 1.223 dB, provided much higher generalization capacity than the ANFIS-PSO (RMSE of 1.939 dB), ANFIS-GA (RMSE of 2.418 dB), and ANFIS (RMSE of 3.403 dB) models in terms of predicting AOp. This clearly demonstrates the effectiveness of SFS to provide a more accurate model in the AOp prediction field.

Soft computing models for predicting blast-induced air over-pressure: A novel artificial intelligence approach

Applying soft computing models for solving real-life problems has yielded many significant benefits, especially in the mining industry. This study proposed a novel soft computing model for estimating blast-induced air over-pressure (AOp) with high accuracy. Accordingly, the boosted smoothing spline (BSTSM) and genetic algorithm (GA) were considered and combined, namely GA-BSTSM model. One hundred twenty-one blasts were collected at the Coc Sau open-pit coal mine (Vietnam) for this aim. The explosive capacity used (W) and distance (R) were considered as the primary input variables for the aiming of AOp prediction. Also, the meteorological conditions such as temperature (T), relative humidity (RH), atmospheric pressure (AP), wind speed (WS), and wind direction (WD) were taken into account to predict AOp in this study. To confirm the performance of the proposed GA-BSTSM model, an empirical model and six other artificial intelligence models were also developed to predict AOp based on the same dataset, including CART (classification and regression tree), KNN (k-nearest neighbors), ANN (artificial neural network), BRR (Bayesian ridge regression), SVR (support vector regression), and Gaussian process (GP). The developed models were then evaluated through three performance indices (i.e., RMSE, R2, and VAF) using the testing dataset. In addition, a Taylor diagram was also developed to evaluate the quality of the models. The evaluation results showed that the proposed GA-BSTSM model yielded a robust performance with high accuracy for AOp prediction herein. The findings also disclosed that meteorological factors have a strong influence on the accuracy of AOp predictive models, especially RH and WS.

Novel approach for forecasting the blast-induced AOp using a hybrid fuzzy system and firefly algorithm

Air overpressure (AOp) produced by blasting is one of the environmental hazards of mining operations. Accordingly, the accurate prediction of AOp is very important, and this issue requires the application of appropriate prediction models. With this in view, this paper aims to propose a new data-driven model in the prediction of AOp using a hybrid model of fuzzy system (FS) and firefly algorithm (FA). This combination is abbreviated as FS-FA model. The used data-sets in the proposed FS-FA model were arranged in a format of three input parameters. In total, 86 sets of the mentioned parameters were prepared. To avoid over-fitting, the data-sets were divided into two parts of training (80%) and test sets (20%). Three quantitative standard statistical performance evaluation measures, variance account for (VAF), coefficient correlation (R2) and root mean squared error (RMSE), were used to check the accuracy of the FS-FA model. According to the results, the R2 and RMSE values obtained from the proposed FS-FA model were equal to 0.977 and 1.241 (for testing phase), respectively, which clearly demonstrate the merits of the proposed FS-FA model. In other words, the obtained R2 and RMSE show that FS-FA model has high prediction level in the modeling of blast-induced AOp.

Developing an innovative soft computing scheme for prediction of air overpressure resulting from mine blasting using GMDH optimized by GA

Air overpressure (AOp) is one of the most important undesirable effects induced by blasting operations in the mining or tunneling projects. Hence, the present precise model for the prediction of AOp would be much beneficial to control the AOp. To this end, the present study proposes a new hybrid of group method of data handling (GMDH) and genetic algorithm (GA). In the other words, the GA is used to optimize the GMDH. The proposed GMDH–GA model was constructed, trained, and tested based on a collection of 84 actual datasets collected from the Shur river dam region. In the modeling, four input parameters were considered: maximum charge per delay, distance between the blasting point and monitoring station, powder factor and rock mass rating. The coefficient of determination (R2), root mean square error (RMSE) and variance account for (VAF), as the statistical performance indices, were used to evaluate the accuracy of the proposed GMDH–GA model. Consequently, the results indicate that the predicted values using the GMDH–GA model are in excellent agreement with the actual data (with the R2 of 0.988), which demonstrate the reliability of the GMDH–GA model.

Controlling of the Environmental Effects of Air Shock Wave Due to Blasting In Quarry Mines

This paper presents controlling the environmental effects of air shock waves due to blasting in quarry mines. The aim of this study is to predict the air overpressure due to blasting, in order to protect the dwelling area adjacent to the quarry. Prediction of air overpressure is important for mine operators who utilize blasting to ensure their blasts will not cause damage to nearby structures and stay within regulatory limits. Airblast is dependent on the size of the blast and air shock wave magnitude is dependent on how far away from the blast a recording device is located. Air shock waves are an integral part of the process of rock blasting and consequently, they are unavoidable. In this paper, important and widely used empirical formulae have been used to predict the air overpressure for quarry mines. Moreover, a comparative analysis between the results obtained for a constant charge per delay of 100 kg, and monitoring distance of 20 m, 30 m, and 40 m were carried out to select the suitable empirical formula. The paper concludes with the guidelines for applying the air overpressure wave based on the various distances and amount of charges per delay.

Development of GP and GEP models to estimate an environmental issue induced by blasting operation.

Air overpressure (AOp) is one of the most adverse effects induced by blasting in the surface mines and civil projects. So, proper evaluation and estimation of the AOp is important for minimizing the environmental problems resulting from blasting. The main aim of this study is to estimate AOp produced by blasting operation in Miduk copper mine, Iran, developing two artificial intelligence models, i.e., genetic programming (GP) and gene expression programming (GEP). Then, the accuracy of the GP and GEP models has been compared to multiple linear regression (MLR) and three empirical models. For this purpose, 92 blasting events were investigated, and subsequently, the AOp values were carefully measured. Moreover, in each operation, the values of maximum charge per delay and distance from blast points, as two effective parameters on the AOp, were measured. After predicting by the predictive models, their performance prediction was checked in terms of variance account for (VAF), coefficient of determination (CoD), and root mean square error (RMSE). Finally, it was found that the GEP with VAF of 94.12%, CoD of 0.941, and RMSE of 0.06 is a more precise model than other predictive models for the AOp prediction in the Miduk copper mine, and it can be introduced as a new powerful tool for estimating the AOp resulting from blasting.

Optimizing Blasting’s Air Overpressure Prediction Model using Swarm Intelligence

Air overpressure (AOp) resulting from blasting can cause damage and nuisance to nearby civilians. Thus, it is important to be able to predict AOp accurately. In this study, 8 different Artificial Neural Network (ANN) were developed for the purpose of prediction of AOp. The ANN models were trained using different variants of Particle Swarm Optimization (PSO) algorithm. AOp predictions were also made using an empirical equation, as suggested by United States Bureau of Mines (USBM), to serve as a benchmark. In order to develop the models, 76 blasting operations in Hulu Langat were investigated. All the ANN models were found to outperform the USBM equation in three performance metrics; root mean square error (RMSE), mean absolute percentage error (MAPE) and coefficient of determination (R2). Using a performance ranking method, MSO-Rand-Mut was determined to be the best prediction model for AOp with a performance metric of RMSE=2.18, MAPE=1.73% and R2=0.97. The result shows that ANN models trained using PSO are capable of predicting AOp with great accuracy.

Development of a novel soft-computing framework for the simulation aims: a case study

The simulation of blast-induced air-overpressure (AOp) has been a major area of interest in the recent years, and many models have been employed in this field. The scope of this paper is to propose a novel soft-computing framework for predicting the AOp through the implementation of hybrid evolutionary model based on artificial neural network (ANN) with teaching–learning-based optimization (TLBO). The parameters considered during the formulation of the prediction model were maximum charge per delay, rock mass rating, and distance from the blasting face as the inputs and AOp as the output. Totally, 85 blasting events in Shur river dam region have been monitored and the mentioned parameters have been measured. Then, the performances and prediction efficiency of the models have been compared on the basis of performance indices, namely the R square (R2), root-mean-square error (RMSE). The obtained results show that the ANN–TLBO with R2 of 0.932 and RMSE of 2.56 yields the better performance for the prediction of AOp as compared to ANN. As a conclusion, it can be found that the proposed ANN–TLBO model has an excellent potential for the prediction aims.

Prediction of air-overpressure caused by mine blasting using a new hybrid PSO–SVR model

The aim of the present study is to predict air-overpressure (AOp) resulting from blasting operations in the Shur river dam, Iran. AOp is considered as one of the most detrimental side effects induced by blasting. Therefore, accurate prediction of AOp is essential in order to minimize/reduce the environmental effects of blasting. This paper proposes a new hybrid model of particle swarm optimization (PSO) and support vector regression (SVR) for AOp prediction. To construct the PSO---SVR model, the linear (L), quadratic (Q) and radial basis (RBF) kernel functions were applied. Here, these combinations are abbreviated using PSO---SVR-L, PSO---SVR-Q and PSO---SVR-RBF. In order to check the accuracy of the proposed PSO---SVR models, multiple linear regression (MLR) was also utilized and developed. A database consisting of 83 datasets was applied to develop the predictive models. The performance of the all predictive models were evaluated by comparing performance indices, i.e. coefficient correlation (CC) and root mean square error (RMSE). As a result, PSO can be used as a reliable algorithm to train the SVR model. Moreover, it was found that the PSO---SVR---RBF model receives better results in comparison with other developed hybrid models in the field of AOp prediction.

Estimation of air-overpressure produced by blasting operation through a neuro-genetic technique

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.

Several non-linear models in estimating air-overpressure resulting from mine blasting

This research presents several non-linear models including empirical, artificial neural network (ANN), fuzzy system and adaptive neuro-fuzzy inference system (ANFIS) to estimate air-overpressure (AOp) resulting from mine blasting. For this purpose, Miduk copper mine, Iran was investigated and results of 77 blasting works were recorded to be utilized for AOp prediction. In the modeling procedure of this study, results of distance from the blast-face and maximum charge per delay were considered as predictors. After constructing the non-linear models, several performance prediction indices, i.e. root mean squared error (RMSE), variance account for (VAF), and coefficient of determination (R2) and total ranking method are examined to choose the best predictive models and evaluation of the obtained results. It is obtained that the ANFIS model is superior to other utilized techniques in terms of R2, RMSE, VAF and ranking herein. As an example, RMSE values of 5.628, 3.937, 3.619 and 2.329 were obtained for testing datasets of empirical, ANN, fuzzy and ANFIS models, respectively, which indicate higher performance capacity of the ANFIS technique to estimate AOp compared to other implemented methods.

Prediction of blast-induced air overpressure: a hybrid AI-based predictive model.

Blast operations in the vicinity of residential areas usually produce significant environmental problems which may cause severe damage to the nearby areas. Blast-induced air overpressure (AOp) is one of the most important environmental impacts of blast operations which needs to be predicted to minimize the potential risk of damage. This paper presents an artificial neural network (ANN) optimized by the imperialist competitive algorithm (ICA) for the prediction of AOp induced by quarry blasting. For this purpose, 95 blasting operations were precisely monitored in a granite quarry site in Malaysia and AOp values were recorded in each operation. Furthermore, the most influential parameters on AOp, including the maximum charge per delay and the distance between the blast-face and monitoring point, were measured and used to train the ICA-ANN model. Based on the generalized predictor equation and considering the measured data from the granite quarry site, a new empirical equation was developed to predict AOp. For comparison purposes, conventional ANN models were developed and compared with the ICA-ANN results. The results demonstrated that the proposed ICA-ANN model is able to predict blast-induced AOp more accurately than other presented techniques.

Blast induced air overpressure and its prediction using artificial neural network

Air blast is considered to be one of the most hazardous environmental disturbances created by blasting operation. Prediction of air overpressure (AOP) generated owing to blasting is difficult due to the influence of several factors in the air wave transmission. Blast design parameters, wind direction and speed, atmospheric temperature, humidity and topography, etc. are all affecting AOP. In this paper, an attempt has been made to predict AOP using artificial neural network (ANN) by incorporating the most influential parameters like maximum charge weight per delay, depth of burial of charge, total charge fired in a round and distance of measurement. To investigate the effectiveness of this approach, the predicted values of AOP by ANN were compared with those predicted by generalised equation incorporating maximum charge weight per delay and distance of measurement. Air overpressure data sets obtained from four different mines in India were used for the neural network as well as to form generalised equation. The network was trained by 70 data sets and validated with 25 data sets. The network and generalised predictor equations were tested with 15 AOP data sets obtained from another two mines. The results obtained from neural network analysis showed that the depth of burial of the charges and maximum charge weight per delay were among the blast designed parameters that have most influence on AOP. Based on the ANN result, depth of burial of charge has more relative sensitivity and weight than the maximum charge weight per delay. The average percentage of prediction error for ANN was 2·05, whereas for generalised equation, it was 5·97. The relationship between measured and the predicted values of AOP was found to be more logical in the case of ANN (correlation coefficient: 0·931) than that of generalised equation (correlation coefficient: 0·867).