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![Fig. 2 Back-propagation neural network [19]](profile/Mohan-Yellishetty/publication/220677847/figure/fig2/AS:668963482640405@1536504731880/Back-propagation-neural-network-19_Q320.jpg)
![Fig. 3 ANN process flowchart [19]](profile/Mohan-Yellishetty/publication/220677847/figure/fig3/AS:668963482660885@1536504731903/ANN-process-flowchart-19_Q320.jpg)
In this study, an attempt has been made to evaluate and predict the blast-induced ground vibration by incorporating explosive
charge per delay and distance from the blast face to the monitoring point using artificial neural network (ANN) technique.
A three-layer feed-forward back-propagation neural network with 2-5-1 architecture was trained and te...
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... zone and radial fracture zone encom- pass a volume of permanently deformed rock. When the stress wave intensity diminishes to the level, where no permanent deformation occurs within the rock mass (i.e., beyond the fragmentation zone), strain waves propagate through the medium as elastic waves, oscillating the particles through which they travel (Fig. 1). These waves in the elastic zone are known as ground vibration, which closely conform to the visco-elastic behavior. The wave motion spreads concentrically from the blast site in all the directions and gets attenuated due to spreading of fixed energy over a greater mass of material and away from its origin [4]. Though the ground ...
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... between the hidden and output layers [12]. This procedure is repeated with each pair of training case. Each pass through all the training patterns is called a cycle or epoch. The process is then repeated with as many epochs as needed until the error is within the user-specified goal. A schematic representation of the whole process is shown in Fig. 3 ...
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... site constants were determined from the multiple regression analysis of the previously mentioned 130 cases. The calculated values of site constants for the various predictor equations are given in Table 4. Figure 11 shows a comparison between predicted PPV by ANN and conventional predictor equations. Here, predic- tion by ANN is closer to the measured PPV, whereas prediction by conventional predictors has a wide variation. ...
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Citations
... Public and regulatory bodies demand greater responsibility and accountability from industries, especially those with significant environmental impacts such as blasting. Khandelwal et al. [12] described ground vibration because of an explosion in a rock mass during the blasting process. The blast hole explodes with a detonating charge that fractures the rock. ...
Ground vibration is one of the most hazardous outcomes of blasting. It has a negative impact both on the environment and the human population near to the blasting area. To evaluate the magnitude of blasting vibrations, it is important to consider PPV as a fundamental critical base parameter practice in terms of vibration velocity. This study aims to explore the application of different soft computing techniques, including a Gaussian process regression (GPR), decision tree (DT), and support vector regression (SVR), for the prediction of blast-induced ground vibration (PPV) in quarry mining. The three models were evaluated using classical mathematical evaluation metrics (R2, RMSE, MSE, MAE). The result shows that the GPR model achieves an excellent prediction result; with R2 = 0.94, RMSE = 0.0384, MSE = 0.0014, and MAE = 0.0265, it shows high accuracy in predicting PPV. The Shapley additive explanation (SHAP) results emphasize the importance of understanding the interactions between the various factors and their effects on the vibration assessment. The findings can inform the development of more sustainable and environmentally friendly models for predicting blasting vibrations. Using a GPR to simulate and predict blasting-induced ground vibrations is the study’s main contribution. The GPR can capture complicated, non-linear correlations in data, making it ideal for blast-induced ground vibrations, which are dynamic and nonlinear. By using a Gaussian process regression, we can help companies and researchers improve the safety and efficiency in blast-induced ground vibration environments.
... The network was trained using Levenberg-Marquardt backpropagation algorithm, which ultimately produced appropriate training method results. This benefit is substantial when working with large networks that contain many neurons and can significantly shorten training and evaluation periods [44][45][46][47][48]. In addition, among all available training algorithms in the prediction of mechanical properties, crack width, and propagation of waste ceramic concrete subjected to elevated temperatures, the Levenberg-Marquardt algorithm produced the best ANN prediction [29]. ...
Mortar is subjected to high temperatures during fire attacks or when it is near heat-radiating equipment like furnaces and reactors. The physical and microstructure of mortar were considerably altered by high temperatures. In this investigation, the effects of elevated temperatures on the flexural and compressive strengths of wood ash (WA) cement mortar modified with green-synthesised Nano titanium oxide (NT) were examined. In order to produce mortar samples, the cement was replaced with 10% WA, and 1-3% NT by weight of binder were added at constant water-binder ratio. The specimens were heated to 105, 200, 400, 600, and 800 • C with an incremental rate of 10 • C per min in the electric furnace for a sustained period of 2 h to measure their strengths. The machine learning algorithm of artificial neural networks with Levenberg-Marquardt backpropagation training techniques of different network architectures was engaged to predict the compressive strength of WA-cement-NT-based mortar produced. The findings showed that higher temperatures reduced compressive strength after 400 • C and flexural strength after 200 • C. The mortar specimen with a 3% NT addition showed the highest residual compressive strength increase, ranging from 18.75 to 27.38%. Compared to compressive strength, flexural strength is more severely affected by high temperatures. The backpropagation training algorithm revealed that each hidden layer displayed its unique strong prediction. However, Levenberg-Marquardt backpropagation training technique of 7-10-10-1 network structures yielded the best performance metrics for training, validation, and testing compared to 7-10-10-10 and 7-10-1 network architectures.
... SVM are well-known for their capacity to deal with high-dimensional data as well as their excellent performance in classification problems. Khandelwal and Kankar used SVM to make predictions about air bursts, and then they compared those values to the outcomes of the generalised predictor equation [23]. They demonstrated that the predicted values of air blasts using SVM were much more in line with the actual values when compared to the anticipated values using the predictor equation. ...
Air overpressure, often known as AOp, is one of the unfavourable effects of galena blasting. It has high damage potential to structural buildings, ecosystem, and occasionally endanger mine workers due to the ore characteristics. The efficiency of most available techniques to manage this mine challenge is site specific and sometimes limited by poor prediction accuracy. In the current study, several deep and machine learning approaches have used to develop blast-induced AOP prediction models as a way forward to the recent gap. These techniques include, Long Short-Term Memory (LSTM), Multi-Layer Perceptron (MLP), Support Vector Regression (SVR), Random Forest (RF), and Multivariate Adaptive Regression Splines (MARS). To achieve the research aim, 90 measured field data sets were monitored and collected from the Anguran open-pit lead-and-zinc mine (Iran). To improve the proposed model performance, chaos game optimisation (CGO) as a nature-inspired algorithm was adopted. The optimized models were compared with each other and as well with the ordinary models to determine the model with the best prediction accuracy. The results show that, in terms of accuracy levels, the performance of hybrid algorithm approach is superior to that of single based models. The LSTM-CGO model, out of the 10 proposed models, has the highest prediction accuracy statistically. This study demonstrated how well deep learning techniques provide solution to safe and environmental friendly mining operation. The proposed soft computing models are applicable as a tool to forecast AOP in surface mine blasting operation as a pre-blast design decision making reference.
... The maintenance strategy and the appropriate statistical distribution should be based on the occurrence rate of system failures and breakdowns. Much work has been carried out by researchers in the field of creating such tools: transport optimization methods have been proposed [16,17]; the advantages and uses of ANNs for forecasting in various operations have been reviewed [18][19][20][21][22][23][24][25]; and studies are also underway to develop an intelligent optimal energy management strategy [26][27][28][29][30][31][32][33][34]. The subject of improving the reliability and availability of equipment through the use of various intelligent computer systems is highly relevant [35][36][37]; the chosen approach will directly determine the planning and maintenance strategy, the length of the equipment lifecycle and, subsequently, the entire plant lifecycle. ...
Among the most widespread systems in industrial plants are automated drive systems, the key and most common element of which is the induction motor. In view of challenging operating conditions of equipment, the task of fault detection based on the analysis of electrical parameters is relevant. The authors propose the identification of patterns characterizing the occurrence and development of the bearing defect by the singular analysis method as applied to the stator current signature. As a result of the decomposition, the time series of the three-phase current are represented by singular triples ordered by decreasing contribution, which are reconstructed into the form of time series for subsequent analysis using a Hankelization of matrices. Experimental studies with bearing damage imitation made it possible to establish the relationship between the changes in the contribution of the reconstructed time series and the presence of different levels of bearing defects. By using the contribution level and tracking the movement of the specific time series, it became possible to observe both the appearance of new components in the current signal and the changes in the contribution of the components corresponding to the defect to the overall structure. The authors verified the clustering results based on a visual assessment of the component matrices' structure similarity using scattergrams and hierarchical clustering. The reconstruction of the time series from the results of the component grouping allows the use of these components for the subsequent prediction of faults development in electric motors.
... Although there are several parameters measured in the tunnel site, many researchers in their empirical and computational PPV models suggested using only two predictors: DI and C [66,67]. Therefore, these two variables were used in this study, as well, to forecast PPV results produced by blasting. ...
Peak particle velocity (PPV) caused by blasting is an unfavorable environmental issue that can damage neighboring structures or equipment. Hence, a reliable prediction and minimization of PPV are essential for a blasting site. To estimate PPV caused by tunnel blasting, this paper proposes two neuro-based metaheuristic models: neuro-imperialism and neuro-swarm. The prediction was made based on extensive observation and data collecting from a tunnelling project that was concerned about the presence of a temple near the blasting operations and tunnel site. A detailed modeling procedure was conducted to estimate PPV values using both empirical methods and intelligence techniques. As a fair comparison, a base model considered a benchmark in intelligent modeling, artificial neural network (ANN), was also built to predict the same output. The developed models were evaluated using several calculated statistical indices, such as variance account for (VAF) and a-20 index. The empirical equation findings revealed that there is still room for improvement by implementing other techniques. This paper demonstrated this improvement by proposing the neuro-swarm, neuro-imperialism, and ANN models. The neuro-swarm model outperforms the others in terms of accuracy. VAF values of 90.318% and 90.606% and a-20 index values of 0.374 and 0.355 for training and testing sets, respectively, were obtained for the neuro-swarm model to predict PPV induced by blasting. The proposed neuro-based metaheuristic models in this investigation can be utilized to predict PPV values with an acceptable level of accuracy within the site conditions and input ranges used in this study.
... So, recognising right blast pattern is an important job for a mining engineer. ANN for forecasting the peak-particle velocity for the blast-induced ground vibrations is studied by [42][43][44]. At Sungun Copper Mines Multi Attribute Decision Making (MADM)-TOPSIS model was used to determine the best suitable blasting pattern [45]. ...
p>To keep up with the new technology modernization and the profit in shake of investors and stakeholders and importantly for the nation, and to ensure health and safety mining industry needs to approve new-age autonomous technologies and intelligent system in their field. Integration of Artificial Intelligence, Machine Learning, Internet of Things (IoT) and Automation are the keys to the 4th revolution in mining industry. This paper presents the overview of recent research upon artificial intelligence enhanced techniques and possibilities in mining operations and mining related domains. There is also a brief about the recent autonomous techniques and equipment in mining industry. Implementations and possibilities of artificial intelligence in safety and accident analysis of mining operations are sincerely detailed. Computer vision and spatial image analysis is also discussed as the recent advancement of deep learning and pattern recognition. Other mining related implementations of intelligent systems includes fragment analysis of ores, intelligent ventilation, on-site mineral processing simplification, digital twinning, mineral exploration, mineral price forecasting, mining equipment selection, post-mining land reclamation and scheduling. This paper also notes the detailed obstacles for implementing intelligent systems in mining industry.</p
... So, recognising right blast pattern is an important job for a mining engineer. ANN for forecasting the peak-particle velocity for the blast-induced ground vibrations is studied by [42][43][44]. At Sungun Copper Mines Multi Attribute Decision Making (MADM)-TOPSIS model was used to determine the best suitable blasting pattern [45]. ...
p>To keep up with the new technology modernization and the profit in shake of investors and stakeholders and importantly for the nation, and to ensure health and safety mining industry needs to approve new-age autonomous technologies and intelligent system in their field. Integration of Artificial Intelligence, Machine Learning, Internet of Things (IoT) and Automation are the keys to the 4th revolution in mining industry. This paper presents the overview of recent research upon artificial intelligence enhanced techniques and possibilities in mining operations and mining related domains. There is also a brief about the recent autonomous techniques and equipment in mining industry. Implementations and possibilities of artificial intelligence in safety and accident analysis of mining operations are sincerely detailed. Computer vision and spatial image analysis is also discussed as the recent advancement of deep learning and pattern recognition. Other mining related implementations of intelligent systems includes fragment analysis of ores, intelligent ventilation, on-site mineral processing simplification, digital twinning, mineral exploration, mineral price forecasting, mining equipment selection, post-mining land reclamation and scheduling. This paper also notes the detailed obstacles for implementing intelligent systems in mining industry.</p
... Hence, it needs to be predicted with a high degree of accuracy to reduce the potential risk of damage. One of the most important descriptors to determine the ground vibration is the peak particle velocity ( ) and has been used in many studies (Khandelwal et al. 2011, Nguyen et al. 2020. By reviewing the previous studies in the field of blasting-vibration, it can be found that the is related to some controllable and uncontrollable parameters (Hasanipanah et al. 2017. ...
Fragmenting the rock mass is considered as the most important work in open-pit mines. Ground vibration is the most hazardous issue of blasting which can cause critical damage to the surrounding structures. This paper focuses on developing an explicit model to predict the ground vibration through an multi objective evolutionary polynomial regression (MOGA-EPR). To this end, a database including 79 sets of data related to a quarry site in Malaysia were used. In addition, a gene expression programming (GEP) model and several empirical equations were employed to predict ground vibration, and their performances were then compared with the MOGA-EPR model using the mean absolute error (MAE), root mean square error (RMSE), mean (u), standard deviation of the mean (r), coefficient of determination (R2) and a20-index. Comparing the results, it was found that the MOGA-EPR model predicted the ground vibration more precisely than the GEP model and the empirical equations, where the MOGA-EPR scored lower MAE and RMSE, u and r closer to the optimum value, and higher R2 and a20-index. Accordingly, the proposed MOGA-EPR model can be introduced as a useful method to predict ground vibration and has the capacity to be generalized to predict other blasting effects.
... The magnitude of ground vibrations is mainly affected by blasting parameters, geometric and geomechanical parameters, and the distance between the blasting location and the monitoring point. Blasting parameters include the amount of explosive per delay, the number of blast events, the power factor of the explosive, the diameter and depth of the hole, the burden, and the spacing of charges [6][7][8] . In addition, if the frequency of BIGV is close to or equal to the natural frequency of the structure, resonance can occur, which strengthens the vibrations of the structure, so that their amplitude will reach a maximum and cause more significant damage or even destruction [9 , 10] . ...
The damage caused to nearby structures by blast-induced ground vibrations (BIGV) in open-pit mines is essentially dynamic. Predicting this damage can be used to set the allowable range for BIGV. To improve the accuracy of the predictions of structural damage due to BIGV in open-pit mines, an optimized Bayes discriminant analysis (OBDA) model is proposed. In this paper, a stepwise discriminant analysis (SDA) was used to screen the variables, and the Bayes discriminant analysis was optimized with the jackknife method. The influence of different sample covariance matrices and prior probabilities on the OBDA model was considered based on good engineering practice. Moreover, nine comparative models were established for a comparative analysis. The OBDA model is discussed in depth. The rationality of the degree of damage of a structure was demonstrated statistically. The coefficient of variation method and improved CRITIC method were used to calculate the weights of the primary variables. The rationality of SDA was verified. The hypothesis that the covariance matrices were not equal was verified statistically, and the influence of the significance level on the SDA was also discussed. The OBDA model was applied to evaluate the damage to structures caused by BIGV in Tonglushan open-pit mine. The results show that: (1) The model has an excellent discriminatory effect, and its correct-judgment rate was 89.167%. (2) The OBDA model has better prediction performance compared with nine comparative models and with the previously used random forest and gradient-boosted machine models. The OBDA model may be a new option for predicting the damage caused to structures by BIGV in open-pit mining.
... During the blasting process, part of the explosive energy can cause some negative effects of blasting [40][41][42]. Blast-induced ground vibration, an important and common negative effect and poses a threat to the safety of the surrounding buildings and the residents' lives [43][44][45][46]. Therefore, it is of great significance to control, weaken, predict, and contain the impacts of blast-induced ground vibration on buildings, slopes, groundwater, etc. in nearby areas. ...
The relevance vector machine (RVM) is considered a robust machine learning method and its superior performance has been confirmed through many successful engineering applications. To improve the performance of the RVM model, three single kernel functions, and three multikernel functions, including two newly proposed multikernel functions, tenfold cross-validation, and the hybrid particle swarm optimization with grey wolf optimizer (HPSOGWO) algorithm were combined to develop an artificial intelligence (AI) model framework. Afterwards, a new application of the RVM method was used and introduced for two different datasets of the blast-induced ground vibration. In addition, an artificial neural network (ANN) model and seven empirical equations were also developed for comparison purposes, and their prediction performances were evaluated considering three performance metrics, i.e., root mean square error (RMSE), correlation coefficient (R2), and mean absolute error (MAE). The obtained results showed that the multikernel RVM model can provide better performance capacity than the single-kernel RVM model. As a result, the AI models were found to be more applicable than the empirical equations in estimating blast-induced ground vibration. The prediction performance results of these models confirmed that the selected database has a great impact on the prediction capacity. Therefore, it is a common act to compare the performance of various models based on the selected database before selecting an optimal predictive model. The proposed model in this study provides new theoretical and practical support for the prediction of blast-induced ground vibration and can be utilized by other researchers in similar fields.
