Blast-induced Ground Vibration Research Articles

Measurement of Blast Induced Ground Vibration using Arduino based SW-420 Vibration Sensor their Comparison with the Existing Vibration Monitoring Instrument

Measurement of Blast Induced Ground Vibration using Arduino based SW-420 Vibration Sensor their Comparison with the Existing Vibration Monitoring Instrument

Chaos Game Optimization-Hybridized Artificial Neural Network for Predicting Blast-Induced Ground Vibration

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.

Prediction of blast-induced ground vibration using multivariate statistical analysis in the opencast chromite mines of the Indian State of Odisha

Prediction of blast-induced ground vibration using multivariate statistical analysis in the opencast chromite mines of the Indian State of Odisha

A Scientific Exploration of Blast-Induced Ground Vibration Mitigation Strategies for Sustainable Coal Mining in India

A Scientific Exploration of Blast-Induced Ground Vibration Mitigation Strategies for Sustainable Coal Mining in India

Data processing in blast-induced ground vibration measurement issue with Kalman filter

Measurement data processing plays an important role in acquisition systems. Completely eliminating the error caused by noise is a difficult issue because correct data exists in noise. Besides, it is impossible to distinguish them clearly. For this reason, solutions to process and standardize data are always applied in the condition of paying close attention to the peculiarity and specific properties of the measured object and the factors affecting it. Blast-induced Ground Vibration wave data is one of the extremely important information in mining operations. It is used to evaluate the impact of the blasting on construction. Besides, it presents the efficiency of explosives as well as of the blasting and mining operation in general. For many particular cases, the physical and mechanical properties of the object to be broken (geological structure, physical and mechanical properties of the rock in the blasting area) are relatively image by knowing its waves. Furthermore, the parameters of the next blasting are also improved based on this data. Due to its important role, the requirement for automatic and highly accurate recording of blast-induced ground vibration data is an urgent issue in mining operations in Vietnam. In this paper, solutions to apply the Kalman filter to handle ground vibration wave data due to blasting were introduced. The work is done based on considering the properties and factors affecting the amplitude, frequency, and characteristics of ground vibration waves. Data is taken directly from the sensor without any processing. Therefore, it carries a lot of noise from many different causes such as the sensor structure, gravity, rock structure, the other sources of vibration at the same time, etc. The used sensor is a product of Nation Instrument (NI). The content of the article is the result of researching and building an automatic blast-induced ground vibration wave measuring device, which will be applied in the field of mining and quarrying in Vietnam.

Evaluation and Prediction of Blast-Induced Ground Vibrations: A Gaussian Process Regression (GPR) Approach

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.

Simulation of blast-induced ground vibrations using a machine learning-assisted mechanical framework

Simulation of blast-induced ground vibrations using a machine learning-assisted mechanical framework

Predicting different components of blast-induced ground vibration using earthworm optimisation-based adaptive neuro-fuzzy inference system

ABSTRACT This study focuses on addressing the complexity inherent in various amplitude components of blast-induced ground vibration (BIGV), encompassing vertical, radial, transversal, and the vectoral sum of PPVs of particle velocity. It takes into account their nonlinearity across diverse quarry environments, and aims to present an enhanced nonlinear intelligent system for accurate prediction of these components. Multiple artificial intelligence models were explored and developed for this purpose, including a support vector machine (SVM), an adaptive neural network based on the fuzzy inference system (ANFIS), and a novel hybrid model that combines earthworm optimisation (EO) and ANFIS (EO-ANFIS). The study also leverages the empirical model offered by the United States Bureau of Mines. The outcomes highlighted that the predictions of the three individual components prove to be more accurate compared to the vectoral sum of PPVs of particle velocity. However, the latter remains a valuable metric for evaluating the magnitude of BIGV in open-pit mines. Notably, the hybrid EO-ANFIS model emerges as the most accurate, achieving an impressive ~ 75% accuracy across 10 quarries characterised by distinct geological conditions.

Knowledge mapping of research progress in blast-induced ground vibration from 1990 to 2022 using CiteSpace-based scientometric analysis.

Blasting constitutes an essential component of the mining and construction industries. However, the associated nuisances, particularly blast vibration, have emerged as significant concerns that pose threats to operational stability and the safety of the surrounding areas. Given the increasing emphasis on sustainability, ecological responsibility, safety, and geo-environmental practices, the impact of blast vibration has garnered heightened attention and scrutiny. Nevertheless, the field still lacks comprehensive phase analysis studies. Therefore, it is imperative to elucidate the research progress on blast vibration and discern its current frontiers of investigation. To address this need, this study employs bibliometric methods and the CiteSpace 6.1.R2 software to analyze 3093 papers from the Web of Science database. Through this comprehensive analysis, the study aims to chronicle the developmental trajectory, assess the present research status, and identify future trends in the field of blast vibration. The findings of this study reveal that research on "blasting vibration" is advancing rapidly, with the number of citations exhibiting a J-shaped growth curve over time. China emerges as the leading contributor to this research, followed by India, and the foremost institution in this field is Central South University in China. Cluster analysis identifies the effects of ground vibration, numerical simulation, blast load, blasting vibration and rockburst hazard as the most prominent research areas presently. The primary research directions in this domain revolve around therock fragmentation, compressive strength, particle swarm optimization, and ann. The emergence of these keywords underscores a dynamic shift towards a more holistic and multidisciplinary approach in the field of blasting-induced ground vibration. Furthermore, this study provides a concise overview of blast vibration, discusses prediction techniques, and proposes measures for its control. Additionally, the discussion delves into the social significance of intelligent blasting systems within the context of artificial intelligence, aiming to address the hazards associated with blast-induced ground vibrations.

Assessment of Charge Initiation Techniques Effect on Blast Fragmentation and Environmental Safety: An Application of WipFrag Software

Blast charge initiation procedures have a significant impact on both mining safety and production rates. In this study, the inventory benefit of an electric initiation system was investigated to assess its influence on both fragmentation and blast-induced damages. The WipFrag software was used to examine the size distribution and productivity of 12 small-scale blasts initiated by both nonelectric and electric detonators. All blast rounds were initiated with plain-type electric and NONEL detonators. The average burden, spacing, stemming length, and charge weight were, respectively, 0.85 m, 1.10 m, 0.66 m, and 1.1 kg. The results showed that the mesh through which 80% of the blast fragments passed for the electric blast was smaller than the mesh through which the material products from the NONEL blast passed. The results also demonstrated that the generated blast-induced ground vibration (PPV) from all blast rounds for electric blast varied from 0.4–1.2 mm/s and 80–105 dB, while that for nonelectric blast ranged from 0.05–0.2 mm/s and 72–95 dB. As a result, the electric blast initiation technique was found to produce good fragmentation, with a higher percentage of optimum fragment sizes on spec than nonelectrically initiated blasts.

Effect of blast induced vibration on coal mine overburden dump slope through discrete element method

Blast induced ground vibration (BIGV) is a serious threat for the coal mine overburden (OB) dump slopes. The problem becomes intricate considering heterogeneity of the OB dump materials. The performance of an OB dump slope under BIGV is investigated considering Voronoi tessellation scheme of universal distinct element code (UDEC) for the heterogeneity of the OB materials. Variations of BIGV, expressed in terms of peak particle acceleration, velocity and displacement, with the density of explosive and the distance of OB dump slope from the blasting source are comprehensively reported considering single-benched as well as double-benched OB dump slopes. Nonlinear dynamic analyses are carried out considering the expected BIGV at the base of the OB dump slope and effects of blasting parameters on the performance of the dumps were investigated considering different monitoring points along the surface of the slope. The effects of heterogeneity on the dynamic response of OB dump slopes are also presented in comparison with the homogeneous condition. A comparative study based on the performance of single-benched and double-benched slopes under BIGV is also presented. Finally, the failure state of the OB dumps were inferred from the permanent ground displacement experienced by the slopes considering BIGV. The methodology and the presented results are supposed to be beneficial for the mining authority in framing the safety guidelines for OB dump slopes in coal mines.

Prediction of Blast-Induced Ground Vibration Using Gene Expression Programming (GEP), Artificial Neural Networks (ANNs), and Linear Multivariate Regression (LMR)

Prediction of Blast-Induced Ground Vibration Using Gene Expression Programming (GEP), Artificial Neural Networks (ANNs), and Linear Multivariate Regression (LMR)

Enhancing predictions of blast-induced ground vibration in open-pit mines: Comparing swarm-based optimization algorithms to optimize self-organizing neural networks

The objective of this paper is to present a method for predicting blast-induced ground vibration in open-pit mines that is based on the use of self-organizing neural networks (SONIA) and metaheuristic algorithms. In order to improve the accuracy of the SONIA model, several metaheuristic algorithms were employed, including the Manta Ray Foraging Optimization (MRFO), Hunger Games Search (HGS), Aquila Optimization (AO), and Naked Mole-Rat Algorithm (NMRA). Additionally, the k-fold cross-validation technique was used to identify the best parameters for the algorithms, which were then used to retrain the models for predicting blast-induced ground vibration. The effectiveness of the proposed method was evaluated using a case study of an open-pit coal mine in Vietnam, which had 288 blasting events. The study found that SONIA was a suitable neural network for predicting blast-induced ground vibration due to its self-organizing structure, even with a small dataset containing complex relationships. However, the SONIA model could be further optimized using the metaheuristic algorithms to improve its accuracy. The study found that the MRFO-SONIA model was the most reliable and accurate, with the lowest error (MAE = 0.379, RMSE = 0.453, MAPE = 0.08) and the highest reliability (R2 = 0.896). Meanwhile, the HGS-SONIA, AO-SONIA, and NMRA-SONIA models provided lower performance, with MAE values of 0.455, 0.500, and 0.492, RMSE values of 0.552, 0.603, and 0.580, MAPE values of 0.100, 0.112, and 0.111, and R2 values of 0.845, 0.815, and 0.829, respectively. The results of this study demonstrated the potential of using metaheuristic-based SONIA models to enhance predictions of blast-induced ground vibration in open-pit mines. This approach could prove useful in other operations in open-pit mines where there is a need to predict vibrations or other disturbances/negative effects resulting from specific mining activities.

Prediction and Optimization of Blasting-Induced Ground Vibration in Open-Pit Mines Using Intelligent Algorithms

Prediction and parameter optimization are effective methods for mine personnel to control blast-induced ground vibration. However, the challenge of effective prediction and optimization lies in the multi-factor and multi-effect nature of open-pit blasting. This study proposes a hybrid intelligent model to predict ground vibrations using a least-squares support vector machine (LSSVM) optimized by a particle swarm algorithm (PSO). Meanwhile, multi-objective particle swarm optimization (MOPSO) was used to optimize the blast design parameters by considering the vibration of particular areas and the bulk rate of blast fragmentation. To compare the prediction performance of PSO-LSSVM, a genetic-algorithm-optimized BP neural network (GA-BP), unoptimized LSSVM, and BP were used, by applying the same database. In addition, the root-mean-squared error (RMSE), the mean absolute error (MAE), and the correlation coefficient (r) were regarded as the evaluation indicators. Furthermore, the optimization results of the blasting parameters were obtained by quoting the established vibration prediction model and bulk rate proxy model in MOPSO and verified by field tests. The results indicated that the PSO-LSSVM model provided the highest efficiency in predicting vibrations with an RMSE of 1.954, MAE of 1.717, and r of 0.965. Furthermore, the blasting vibration can be controlled by using the two-objective optimization model to obtain the best blasting parameters. Consequently, this study can provide more specific recommendations for vibration hazard control.

A method for predicting blast-induced ground vibration based on Mamdani Fuzzy Inference System

Blast-induced ground vibration is highly possible to result in serious losses such as destroyed buildings. The crucial parameter of the mentioned vibration is peak particle velocity (PPV). Many equations have been developed to predict PPV, however, worse performance has been reported by multiple literatures. This paper developed a method for predicting PPV based on Mamdani Fuzzy Inference System. Firstly, Minimum Redundancy Maximum Relevance was employed to identify the blasting design parameters which significantly contribute to the PPV induced by blasting. Secondly, K-means method was applied to determine the value ranges of the selected parameters. The selected parameters and corresponding value ranges were combined to input into Mamdani Fuzzy Inference System for obtaining predicted PPV. Totally, 280 samples were collected from a blasting site. 260 out of them were used to train the proposed method and 20 were assigned for test. The proposed method was tested in the comparison with empirical equation USBM, multiple linear regression analysis, pure Mamdani Fuzzy Inference System in terms of the difference between predicted PPV and measured PPV, coefficient of correlation, root-mean-square error, and mean absolute error. The results from that showed that the proposed method has the better performance in PPV prediction.

Characteristics and Energy Distribution of Blast-Induced Ground Vibration in Deep-Hole Blasting

This study proposes an incremental extreme extraction method based on the waveform characteristics of ground vibration signals obtained from open-pit mines to investigate the distribution and characteristics of ground vibration from deep-hole blasting. Firstly, an incremental extreme extraction method is proposed based on the waveform characteristics of borehole blasting vibration signals in open-pit mines. The proposed method could extract and screen the extreme values of blasting vibration signals and effectively improve the utilization rate of the data. The space vector of particle vibration is introduced to analyze the angle change between the particle velocity vector and the ground surface when the extreme value increases. Finally, the relation between the particle velocity vector and the angle between the ground plane and the increasing extremum position of several sets of measured signals is studied. Based on the statistical analysis, the results show that the particle velocity in the vertical direction has a significant advantage over that of the other two directions, and the angle between the extreme particle velocity vector direction and the ground plane is primarily distributed in the range of 60°~90°. After an unstable distribution of particle velocities in the transition zone, the particle velocities in each direction gradually attain a relatively balanced and stable attenuation condition as the distance increases. This proves the reliability of the proposed vector analysis of particle velocity in understanding the mechanism of rock blasting.

Evaluation of underground blast-induced ground motions through near-surface low-velocity geological layers

Surface ground motion produced by underground blasts is significantly influenced by near-surface geological conditions. However, near-surface low-propagation velocity layers were always ignored in past analyses of ground motions due to their thin thickness. With the rising concern about surface ground motions produced by the ascendant scale and frequentness of underground excavation and mining, close attention is gradually paid to ground blast vibrations. Therefore, systemic experiments were conducted and took seven months in an underground mine to clarify the variation of motion from underground rock to surface ground. The attenuation of surface ground peak particle velocities (PPVs) is compared to that in underground rock, and horizontal amplitudes are compared to vertical amplitudes. Differences between bedrock and surface ground vibrations are analyzed to illustrate the site effect of near-surface lower-propagation velocity layers. One-dimensional site response analysis is employed to quantify the influence of different geological profiles on surface ground vibrations. The experimental data and site response analysis allowed the following conclusions: (1) geological site effects mainly produce decreasing dominant frequency (DF) of surface ground vibrations; (2) the site amplification effect of blast vibration needs to be characterized by peak particle displacement (PPD); (3) shear waves (S-waves) begin to dominate and surface Rayleigh waves (R-waves) develop as blast-induced ground vibrations travel upward through rock and lower-velocity layers to the surface. The comparison of response relative displacement to a critical value is best to assess the potential for cracking on surface structures.

Reliability and availability artificial intelligence models for predicting blast-induced ground vibration intensity in open-pit mines to ensure the safety of the surroundings

Reliability and availability artificial intelligence models for predicting blast-induced ground vibration intensity in open-pit mines to ensure the safety of the surroundings

Application of Bayesian Neural Network (BNN) for the Prediction of Blast-Induced Ground Vibration

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.

Study on blast-induced ground vibration velocity limits for slope rock masses

Seismic waves induced by blasting can cause damage to slope rock masses and thus lower the stability of the slope. The vibration standards of many countries do not specify the allowable blasting vibration velocity for slope rock masses. “Blasting Safety Regulations” (GB 6722-2014) in China recommends the allowable blasting vibration velocity of slope rock masses. However, the strength of rock mass and geometric characteristics of slope are not considered in the criterion. Therefore, based on the wave equation of Rayleigh wave, the mathematical relationship between the additional dynamic stress of the rock mass near the slope surface and the peak particle velocity (PPV) on the ground is analyzed. Under the control conditions that no tensile and shear damage occur in the slope, a calculation method of allowable blasting vibration based on a simple slope model is proposed, in which the strength of rock mass and the angle of the slope are considered. The theoretical basis of the proposed method is verified by numerical experiments. Based on the field test results of the slope of Xiaowan Hydropower Station, the proposed method was verified further. The damage control target of the rock mass determined by the proposed method was in good agreement with the actual damage detection result.