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Over the past few decades, inducing of ground vibrations from blasting may cause severe damage to surrounding structures, plants, and human beings in the mining industry. Therefore, it is essential to monitor and predict the ambiguous vibration levels and take measures to reduce their hazardous effect. In this study, to evaluate and predict the amb...
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... researchers proposed several criteria indicators for evaluation of blast- induced ground vibration damage. 5,[8][9][10][11][12][13] Multiple researchers investigated and proposed a num- ber of conventional vibration predictors for the prediction of PPV, which are summarized in Table 1. Suggested con- ventional predictor equations can predict the PPV depend- ing on two parameters such as distance from the blasting face to monitoring point and maximum charge per delay. ...
Citations
... Over the years, researchers have sought to overcome the limitations of conventional empirical equations by employing advanced soft computing analytics to describe complex real-world phenomena. Techniques such as artificial neural networks (ANNs), fuzzy models, linear regression, decision trees, random forests, and deep learning have been utilized to predict and estimate blast-induced vibrations [18][19][20][21][22][23][24][25]. Notable studies by Refs. ...
In this study, we introduced the chaos game optimization-artificial neural network (CGO-ANN) model as a novel approach for predicting peak particle velocity (PPV) induced by mine blasting. The CGO-ANN model is compared with other established methods, including the particle swarm optimization-artificial neural network (PSO-ANN), the genetic algorithm-artificial neural network (GA-ANN), single ANN, and the USBM empirical model. The aim is to demonstrate the superiority of the CGO-ANN model for PPV prediction. Utilizing a dataset comprising 180 blasting events from the Tonglushan Copper Mine in China, we investigated the performance of each model. The results showed that the CGO-ANN model outperforms other models in terms of prediction accuracy and robustness. This study highlights the effectiveness of the CGO-ANN model as a promising tool for PPV prediction in mining operations, contributing to safer and more efficient blasting practices.
... Many researchers have used ANN and supporting vector machines to estimate the peak particle velocities PPV and airbrush. The empirical and AIM methods only provide a maximum amplitude estimation of particle speed, and give no information about the complete seismic waveform [9,10]. ...
Purpose. To study the vibrations waves generated by blasting in a tunnel using the signal processing tools. Methodology. Field tests are carried out to measure vibration wave during blasting operations at different locations in the tunnel and its immediate environment. Results of the measurements are processed by the autocorrelation method, which consists of filter ing based on signal shape recognition. A comparison is accomplished between the peak particle velocities (PPV) measured and those obtained after filtering. Findings. The results obtained after filtering gave a significant reduction in PPV of the measured vibration amplitudes in com parison to those obtained after treatment for the three components: longitudinal, transversal and vertical ones. Good knowledge of vibration source is important for amplitude attenuation regarding the observed difference between the recorded seismogram during explosion of a single unit charge and other standard explosions. Originality. The work introduces signal processing methods for filtering vibration signals related to blasting, which is insuffi ciently studied. Practical value. This study shows that the treatment of blasting vibrations by a filtering method should reduce the peak velocity of the particles by separating the signals and eliminating the interference in the initial signal.
... These methods can effectively handle the complexities that arise from the nonlinear relationships between the variables that influence blasting. Methods that have been explored to improve the study of PPV include: ➤ Artificial neural networks (ANNs) (Amnieh, Mozdianfard, and Siamaki, 2010;Amnieh, Siamaki, and Soltani, 2012;Azimi, Khoshrou, and Osanloo, 2019;Das, Sinha, and Ganguly, 2019;Jiang et al., 2019;Kamali and Ataei, 2010;Kosti et al., 2013;Ragam and Nimaje, 2019;Sayadi et al., 2013) ➤ Other machine-learning studies (Lawal, Olajuyi, and Kwon, 2021;Longjun et al., 2011) ➤ Numerical methods (Ducarne et al., 2018;Kumar et al., 2020;Nguyen and Gatmiri, 2007) ➤ Multivariate analysis (Hudaverdi, 2012) ➤ Empirical analysis (Hu and Qu, 2018) ➤ Bayesian approach (Aladejare, Lawal, and Onifade, 2022). ...
The accurate estimation of peak particle velocity (PPV) is crucial during the design of bench blasting operations in open pit mines, since the vibrations caused by blasting can significantly affect the integrity of nearby buildings and other structures. Conventional models used to predict blast-induced vibrations are not capable of capturing nonlinear relationships between the different blasting-related parameters. Soft computing techniques, i.e., techniques that are founded on the principles of artificial intelligence, effectively model these complexities. In this paper, we use the random forest (RF) algorithm to develop a model to predict blast-induced ground vibrations from bench blasting using 48 data records. The model was trained and tested using WEKA data-mining software. To build this model, a feature selection process using several combinations of Attribute Evaluators and Search Methods under the WEKA Select Attributes tab was performed. The correlation coefficient of the actual data and RF model-predicted data was 0.95, and the weighted average of the relative absolute error (RAE) was 10.9%. The RF model performance was also compared to the equivalent-path-based (EPB) equation on the testing data-set, and it was seen that the RF model can effectively be used to predict PPV. The study also demonstrates that the EPB equation is a suitable empirical method for predicting PPV.
... Based on the number of parameters employed to generate an ANN, their proposed models are divided into two (i.e., training and test) categories. In addition to the ANN technique, different researchers have developed a variety of artificial intelligence methods, such as the fuzzy model [11,12], linear regression, decision tree [13], random forest [14], and deep learning, to predict and estimate blast-induced vibrations. The review study by Dumakor-Dupey et al. [15] revealed an excessive number of ML approaches being used to predict the PPV, with artificial neural network (ANN), support vector machine (SVM), and the adaptive neuro-fuzzy inference system (ANFIS) being the most frequently used algorithms (ANFIS). ...
Rock blasting is one of the most common and cost-effective excavation techniques. However, rock blasting has various negative environmental effects, such as air overpressure, fly rock, and ground vibration. Ground vibration is the most hazardous of these inevitable impacts since it has a negative impact not only on the environment of the surrounding area but also on the human population and the rock itself. The PPV is the most critical base parameter practice for understanding, evaluating, and predicting ground vibration in terms of vibration velocity. This study aims to predict the blast-induced ground vibration of the Mikurahana quarry, using Bayesian neural network (BNN) and four machine learning techniques, namely, gradient boosting, k-neighbors, decision tree, and random forest. The proposed models were developed using eight input parameters, one output, and one hundred blasting datasets. The assessment of the suitability of one model in comparison to the others was conducted by using different performance evaluation metrics, such as R, RMSE, and MSE. Hence, this study compared the performances of the BNN model with four machine learning regression analyses, and found that the result from the BNN was superior, with a lower error: R = 0.94, RMSE = 0.17, and MSE = 0.03. Finally, after the evaluation of the models, SHAP was performed to describe the importance of the models’ features and to avoid the black box issue.
... They compared the obtained results with those of the seven models proposed by the US Bureau of Mines, Ambraseys-Hendron, Langefors-Kihlstrom, general predictor, Ghosh-Daemen predictor, cardiac magnetic resonance imaging (CMRI) predictor, Bureau of Indian Standards, as well as a multiple linear regression. Based on their results, the ANN method with regression and the mean squared error of 0.9971 and 0.08133, respectively, predicted PPV values with higher accuracy and precision than the other models (Ragam and Nimaje 2018). Arthur et al. (2020) collected the vibrations of 210 blast operations at an open-pit mine in Ghana and used the Gaussian Process Regression (GPR) approach to predict PPV values for future blast operations at the mine. ...
Ground vibration is one of the destructive effects of rock blasting. The prediction and control of the blast-induced ground vibrations are very important in mining and construction projects. Therefore, the maximum possible efficiency of the operation should be determined by considering the safety range. In this paper, to estimate the effect of surface and underground blast-induced ground vibration on concrete structures in the area of Gotvand Olya Dam, 30 and 14 records of 21 surface and 4 underground blasting operations (totally 44 data), respectively, were recorded using PG-2002 seismographs. The ground vibrations from the blasting operations were estimated using two empirical relationships with the accuracy of 0.93 and 0.88 for the surface and underground operations, respectively. With the help of a Genetic Algorithm (GA), the correlation of these relationships has increased to 0.96 and 0.9, respectively. Considering the elapsed time of concrete and using the U. S. Army Corps of Engineers standard, the allowable charge weight per delay was calculated for both operations for a hypothetical distance of 50 m. Based on the results of this study, the surface and underground blasting operations can be operated using Cordtex and Nonel systems, while the empirical relationships allow only the Nonel system with limited delay times or a limited number of blast holes. It is also recommended that, if possible, no blasting operations be carried out in the first 24 h of concreting. In case of exigency, the operation is scheduled in the first 7 h of concreting.
... Different empirical predictor equations. 8 Predictor name Equation ...
... List of performance metrics.8 ...
... Noise & Vibration Worldwide 53(7)(8) ...
Over recent decades, ambiguous ground vibration induced by blasting operation can cause extensive damage to structures, lives and fields in and around mine premises. As a consequence, it is indispensable to measure the ambiguous ground vibration intensity levels for assessing and reduce their perilous impact. In this investigation, estimation and evaluation of blast-induced ground vibration in terms of peak particle velocity (PPV) through the ensemble machine learning intelligent algorithms were carried out. One hundred and 21 experimental and blasting events were monitored to collect the real-time field data in Mine-A, India. The collected data was randomly split into training and testing to generate models. Eight input parameters include number of holes, burden, spacing, hole diameter, hole depth, top stemming, maximum explosive charge per delay and the distance were selected for development of ensemble machine learning algorithms. An eXtreme gradient boosting (XGBoost) and random forest (RF) ensemble model, Decision Tree were developed to assess the PPV levels. In addition to that, four empirical predictor models proposed by the US Bureau of Mines, Langefors–Kihlstrom, Central Mining Research Institute, and Bureau of Indian Standards were applied to derive a relation between PPV and its influencing parameters. The accuracy and efficiency of developed models can be determined by performance evaluation metrics chosen as the coefficient of determination (R ² ), and root mean square error (RMSE). Among all models, yielded results evidence that the Decision Tree ensemble model with the R ² of 0.9549, and RMSE of 0.0444 was more precise optimum model to assess the PPV. Besides, a sensitivity analysis method was applied in this current study to know the role of the input parameters in estimating PPV. The determined results inferred that burden, number of holes and top stemming are more influenced parameters on the intensity of PPV levels.
... Among the AI methods, the artificial neural network (ANN) algorithm has strong ability of nonlinear processing, learning, association and selfadaptation [53,54], which provides a powerful way for PPV prediction. Various models based on ANN and optimized-ANN methods have been proposed to estimate PPV and have achieved good prediction results [37,44,[55][56][57][58][59][60][61][62][63][64]. But with the extensive and deep application, the ANN algorithm also exposes some obvious defects, such as easily falling into local minimum, slow convergence speed and weak generalization performance [65]. ...
The prediction model of blasting vibration has always been a hot and difficult topic because of the very complex nonlinear relationship between the blasting vibration and its influencing factors. A novel algorithm of Nested-ELM for predicting blasting vibration was proposed in this paper. Nested-ELM algorithm can quickly select the optimal input weights and biases of hidden nodes by setting MSE as the fitness function and combining with RWS method. And the algorithm can also quickly determine the optimal number of hidden nodes by setting its initial value according to the empirical formulas and selecting MAPE as the diffusion search index. The feasibility and superiority of Nested-ELM algorithm for predicting blasting vibration were proved by the application of Nested-ELM model on four different types of blasting vibration samples. This paper can provide a novel improved ELM algorithm for predicting blasting vibration with good performance in operation efficiency, prediction accuracy, generalization and sample-number independence.
... Some notable techniques applied in literature include: artificial neural network (ANN), fuzzy logics, adaptive neuro-fuzzy inference systems (ANFIS), support vector machine (SVM), Gaussian process regression (GPR), multivariate adaptive regression splines (MARS) and hybrid soft computing techniques. Previous studies reviewed indicate that ANN methods have received the widest of attention (Khandewal and Singh 2006, 2009Amnieh et al. 2010;Monjezi et al. 2010Monjezi et al. , 2011Monjezi et al. , 2013Dehghani and Ataee-pour 2011;Mohamad et al. 2012;Xue and Yang 2014;Saadat et al. 2014;Álvarez-Vigil et al. 2012;Görgülü et al. 2013Görgülü et al. , 2015Lapčević et al. 2014;Shahri and Asheghi 2018;Iramina et al. 2018;Ragam and Nimaje 2018;Arthur et al. 2020a, b;Nguyen et al. 2019). A considerable amount of attention has also been received by SVM, fuzzy logic and ANFIS in predicting blast-induced ground vibration (Khandelwal 2011;Mohammadnejad et al. 2012;Mohamadnejad et al. 2012;Hasanipanah et al. 2015;Iphar et al. 2008;Mohammed 2011;Fişne et al. 2011;Ghasemi et al. 2013;Ataei and Kamali 2013;Armaghani et al. 2015;Ghoraba et al. 2016;Xue et al. 2017;Jiang et al. 2018;Jelušič et al. 2021). ...
There is a collective demand for the mining industry to accurately predetermine or predict blast-induced ground vibration to support effective blast control management. It is, therefore, desirable to explore and develop accurate forecasting tools that can meet environmental and safety standards. In this study, the prediction capabilities of two proposed models, namely least squares support vector machine (LSSVM) and relevance vector machine (RVM) were explored and compared with support vector machine (SVM) which has found a wide application in blast-induced ground vibration prediction. The prediction results of these techniques were ranked to identify the best using mean square error (MSE), root-mean square error (RMSE), correlation coefficient (R) and model efficiency of Loague and Green (ELG). The ranking results revealed that LSSVM was superior to the SVM and RVM. The results produced by LSSVM model were further compared to three benchmark ANN methods (backpropagation neural network (BPNN), radial basis function neural network (RBFNN) and generalised regression neural network (GRNN)) and five empirical predictor models (United State Bureau of Mines (USBM), Ambraseys-Hendron, Langefor-Kihlstrom, Indian standard and Central Mining Research Institute (CMRI)). The comparative analysis revealed that the LSSVM was the best prediction approach because it achieved the lowest MSE and RMSE results and recorded the highest R and ELG values of 0.0215, 0.1467, 0.8542 and 0.7273, respectively. The application of Bayesian Information Criterion (BIC) for model selection confirmed LSSVM as the best among all the methods applied because it produced the least BIC value of − 285.2312.
... The proposed model was developed based on a dataset of 85 blasting events where the PPV was recorded as a measure of the blast-induced vibrations. Using 25 blast records from an iron ore mine in India, Ragam and Nimaje (2018) estimated the PPV by applying ANN. Mokfi et al. (2018) utilized a database of 112 blasting operations at a mine in Penang to present the group method of data handling (GMDH) for predicting the PPV. ...
Ground vibration is one of the most significant issues resulting from the blasting operation. This paper presents two empirical relationships based on gene expression programming (GEP) and teaching–learning-based optimization (TLBO) algorithms for predicting blast-induced peak particle velocity (PPV) at Galali Iron Mine, western Iran. For this purpose, data on 13 parameters were collected from 34 blasting blocks in the studied mine before having the data processed using statistical methods. Eventually, four parameters, including burden, mean hole depth, charge per delay ratio, and distance to monitoring station, were identified as the most effective factors. PPV was also considered as the output parameter of the model. Then, exploring the best curve-fitting relationships between input and output data, an empirical relationship was developed by applying the GEP algorithm. Afterward, the TLBO algorithm was utilized to optimize the developed relationship. A comparative analysis based on statistical parameters such as correlation coefficient (R2), root mean square error (RMSE), and mean absolute percentage error (MAPE) indicated the superior accuracy of TLBO algorithm compared to the GEP method. Finally, a blasting pattern was formulated to attenuate the PPV at the center of the Galali Village from 10 mm/s to 1 mm/s while increasing the mine production from 5500 tons to 17,500 tons per blasting block.
... Reference USBM √ ⁄ [44] Langefors-Kihlstrom / / ⁄ [45] General predictor [43] Ambraseys-Hendron ⁄ [46] Indian Standard / ⁄ [46] Ghosh-Daemen 1 ...
Rock fragmentation in mining and construction industries is widely achieved using drilling and blasting technique. The technique remains the most effective and efficient means of breaking down rock mass into smaller pieces. However, apart from its intended purpose of rock breakage, throw, and heave, blasting operations generate adverse impacts, such as ground vibration, airblast, flyrock, fumes, and noise, that have significant operational and environmental implications on mining activities. Consequently, blast impact studies are conducted to determine an optimum blast design that can maximize the desirable impacts and minimize the undesirable ones. To achieve this objective, several blast impact estimation empirical models have been developed. However, despite being the industry benchmark, empirical model results are based on a limited number of factors affecting the outcomes of a blast. As a result, modern-day researchers are employing machine learning (ML) techniques for blast impact prediction. The ML approach can incorporate several factors affecting the outcomes of a blast, and therefore, it is preferred over empirical and other statistical methods. This paper reviews the various blast impacts and their prediction models with a focus on empirical and machine learning methods. The details of the prediction methods for various blast impacts—including their applications, advantages, and limitations—are discussed. The literature reveals that the machine learning methods are better predictors compared to the empirical models. However, we observed that presently these ML models are mainly applied in academic research.
