Rock Blasting Research Articles

Flyrock in surface mining-Part 3: Shock wave, stress wave, blasthole expansion

The generally accepted view in rock blasting is that the sources of energy for the fracture and movement of rock reside in the shock wave and gas action resulting from the explosion, and yet the mechanisms by which these sources interact with the rock have remained unclarified. It has also been noted that up to 50% of the work capacity of an explosive released in a blast cannot be accounted for by field measurements of energy partitioning. In this study, we describe a physical model that details the response of rock to both shock wave and gas action. An analytical model based on momentum conservation is derived to describe the dynamics of shock-driven expansion of the blasthole. Radial expansion of the hole is the key parameter that permits the derivation of the following characteristics of rock response to shock loads: hole expansion time; volume of displaced rock; energy consumed per unit volume; expansion energy efficiency; stress wave pulse length; gas pressure in enlarged hole. Soon after the completion of hole expansion, the shock wave degenerates to an elastic stress wave that runs through the burden. Blasthole expansions of between 50% and 300% of diameter are completed in under 1 ms and, depending on rock properties, consume 32% to 42% of the detonation energy or about 55% of the available mechanical (Gurney) energy. Gas pressure in the enlarged holes in five rock types is between 35 MPa and 650 MPa, and drives the mass movement of burden rock.

Investigating the Impact of Air-Deck Techniques on Cavity Volume and Fragmentation in Multi-Hole Rock Blasting

Investigating the Impact of Air-Deck Techniques on Cavity Volume and Fragmentation in Multi-Hole Rock Blasting

Application Practice of SHPB Experimental Technology in "Blasting Engineering" Teaching

The course of "Blasting Engineering" is a highly practical professional course in the field of civil engineering. In order to help students deeply understand and comprehend the propagation and attenuation mechanism of stress waves during rock blasting process, as well as the influence of rock dynamic characteristics on blasting rupture effect, SHPB impact compression experimental technology is introduced into the teaching of "blasting engineering". Firstly, the structure, experimental process, and principles of SHPB equipment were introduced, allowing students to have a better understanding of the experimental principles of SHPB and the theoretical calculation methods of rock dynamic parameters. Then, by combining SHPB experiments with the theoretical course of "Blasting Engineering", the teaching mode can solve students' difficulties in learning blasting theory, exercise their scientific research and exploration abilities, expand their innovative thinking, and cultivate their ability to propose and solve key scientific problems. This helps to unleash students' subjective initiative in conducting experimental research independently and improve teaching effectiveness.

Data-driven machine learning approaches for simultaneous prediction of peak particle velocity and frequency induced by rock blasting in mining

The vibrations generated by rock blasting are a serious and hazardous outcome of these activities, causing harmful effects on the surrounding environment as well as the nearby residents. Both the local ecology and human communities suffer from the consequences of these vibrations. Assessing the severity of blasting vibrations necessitates a thorough evaluation of Peak Particle Velocity (PPV) and frequency, which are essential parameters for measuring vibration velocity. Accurate prediction of vibration occurrence is critically important. Therefore, this study employs five machine learning models for predicting the PPV and frequency resulting from quarry blasting. This work compares five machine learning models (XGBoost, Catboost, Bagging, Gradient Boosting, and Random Forest Regression) to choose the most efficient performance model. The performance evaluation of each five machine learning models demonstrates each model achieved a performance of more than 0.90 during the testing phase, there was a strong correlation observed between the actual and the predicted ones. The analysis of performance metrics shows Catboost regression model demonstrate better performance prediction comparing with the other models with R2 = 0.983, MSE = 0.000078, RMSE = 0.008, NRMSE = 0.019, MAD = 0.004, MAPE= 35.197 in the PPV prediction, and R2 = 0.975, MSE = 0.000243, RMSE = 0.015, NRMSE = 0.031, MAD = 0.008, MAPE = 37.281 for the frequency prediction. This study will help mining engineers and blasting experts to select the best machine learning model and its hyperparameters in estimating ground vibration, and frequency. In the context of the mining and civil industry, the application of this study offers significant potential for enhancing safety protocols and optimizing operational efficiency. By employing machine learning models, this research aims to accurately predict and assess ground vibrations with frequency resulting from rock blasting.

Formation mechanisms of different kinds of blast-induced cracks and their extension characteristics in rock mass

Different kinds of cracks, including radial cracks, circumferential cracks, and spalling cracks, are formed in rock blasting. Understanding the extension characteristics of these cracks is essential for improving rock fragmentation results. However, few publications have comprehensively focused on various blast-induced cracks, and their formation mechanisms and extension characteristics are still unclear. In this study, the formation mechanisms of different kinds of blast-induced cracks are theoretically analyzed from the viewpoint of inner and outer blasting effects. Numerical models are subsequently developed to study the extension characteristics of different kinds of blast-induced cracks. Finally, based on the tunnel blasting excavation, the extension of cracks induced by cutting and surrounding blastholes is numerically simulated. The results show that rock blasting can be classified into inner and outer blasting effects. Under the inner blasting effect, radial cracks are formed in various stress states, with circumferential cracks occurring specifically in the tensile-tensile state. Under the outer blasting effect, spalling cracks form around the free surface of rock mass. In brittle rocks subjected to blast loading with higher peak values, circumferential cracks occur more frequently. In practice, it is advisable to improve rock fragmentation results by adjusting the formation of different kinds of blast-induced cracks.

Investigation on rock damage associated with ice-filling borehole blasting

Rock excavation and ore extraction in cold regions (e.g., alpine and high-altitude regions) are often conducted using the drill and blasting method. Ice may be presented in boreholes drilled in cold regions due to the potential freezing of water (e.g., water in rock fractures and pores) flowing into boreholes from the surrounding ground. The performance of rock blasting is inevitably affected by the presence of ice and no study has examined the effect of ice in boreholes on rock blasting performance. The present study investigates rock damage induced by the ice-filling borehole blasting. The damage modes and mechanisms of rock mass under the ice-filling borehole blasting are analyzed. The differences of rock damage induced by the sole ice-filling borehole blasting and the ice-water and ice-air mixed filling borehole blasting are identified. The effects of ice volumes in boreholes on blast-induced rock damage are examined. It is found that blast-induced rock damage is greatly reduced as water in boreholes turns into ice. In addition, the blasting with the ice volume greater than 6.7 % filled at the bottom of borehole can induce less rock damage compared to full-coupled charge blasting. To improve the performance of rock blasting with ice-filling boreholes, the effects of two methods, i.e., changing ignition locations and using different types of explosives on rock damage induced by the ice-filling borehole blasting are investigated. An empirical formula of rock damage volume incorporating ice volume, explosive properties, and rock properties is finally proposed as the reference for the design of ice-filling borehole blasting in cold regions.

Impact of spatially varying rock disturbance on rock slope stability

Degradation of rock mass produced by rock blasting, stress relief, and other causes is an important factor in the assessment of rock strength. Quantified as a disturbance factor, such degradation varies depending on blasting control, stress state and stress relief, and rock mass quality. This study focuses on the impact of disturbance on the safety of slopes. The disturbance in the rock mass is characterized by the geometry of the disturbed zone, its size, the magnitude, and the decaying rate with the distance away from the slope surface. A method accounting for decay of rock disturbance is presented. A study of the impact of rock disturbance characteristics on the quantitative stability measures of slopes was carried out. These characteristics included disturbed zone geometry, its thickness, the maximum magnitude of the disturbance factor, and the rate of disturbance decaying. The thickness of the disturbed zone and the maximum factor of disturbance were found to have the greatest impact. For example, the factor of safety for a 45° slope in low-quality rock mass can decrease from 1.96 to 1.09 as the thickness of the disturbed zone increases from 1/4 of slope height H to the double of H and the maximum disturbance factor increases from 0.5 to 1. Uniform thickness of a disturbed zone was found to yield more conservative outcomes than the triangular zones did. The critical failure surfaces were found to be shallow for high rates of disturbance decay, and they were the deepest for spatially uniform disturbance factors.

Machine learning based prediction of flyrock distance in rock blasting: A safe and sustainable mining approach

Flyrock is a significant environmental and safety concern in mining and construction. It arises from various geological and blast design factors, posing risks to workers, machinery, and nearby structures. This study examined how these factors affect the rate and distance of flyrock projections caused by blasts. To address this issue, advanced machine learning (ML) models were used to predict flyrock distances in the Akoko Edo dolomite quarries. The models examined included bidirectional recurrent neural networks (BRNNs), support vector regression (SVR) with different kernels (SVR-S, SVR-RBF, SVR-L, SVR-P), long short-term memory (LSTM) networks, and random forest (RF) algorithms. A case study was conducted using 258 blasting data samples to develop these models. Key factors influencing flyrock were identified: blast hole burden distance, maximum instantaneous charge, and rock brittleness index. Using these factors, a flyrock possibility assessment chart was created to enhance the safety of small-scale mining operations. The model’s prediction accuracy was evaluated using correlation coefficients and four performance metrics. The LSTM model stood out, achieving the highest coefficient of correlation (R2 = 0.99) for both training and testing datasets. This indicates that the LSTM model accurately predicts blast-induced flyrock distance. The study also revealed that the Gaussian-RBF kernel SVR has high prediction accuracy when compared to other SVR variants (SVR-S, SVR-L, and SVR-P). In conclusion, the study compared various ML models for flyrock reduction and found that the LSTM model was the most effective in estimating blast-induced flyrock distances.

Predicting ground vibration during rock blasting using relevance vector machine improved with dual kernels and metaheuristic algorithms

The ground vibration caused by rock blasting is an extremely hazardous outcome of the blasting operation. Blasting activity has detrimental effects on both the ecology and the human population living in proximity to the area. Evaluating the magnitude of blasting vibrations requires careful evaluation of the peak particle velocity (PPV) as a fundamental and essential parameter for quantifying vibration velocity. Therefore, this study employs models using the relevance vector machine (RVM) approach for predicting the PPV resulting from quarry blasting. This investigation utilized the conventional and optimized RVM models for the first time in ground vibration prediction. This work compares thirty-three RVM models to choose the most efficient performance model. The following conclusions have been mapped from the outcomes of the several analyses. The performance evaluation of each RVM model demonstrates each model achieved a performance of more than 0.85 during the testing phase, there was a strong correlation observed between the actual ground vibrations and the predicted ones. The analysis of performance metrics (RMSE = 21.2999 mm/s, 16.2272 mm/s, R = 0.9175, PI = 1.59, IOA = 0.8239, IOS = 0.2541), score analysis (= 93), REC curve (= 6.85E−03, close to the actual, i.e., 0), curve fitting (= 1.05 close to best fit, i.e., 1), AD test (= 11.607 close to the actual, i.e., 9.790), Wilcoxon test (= 95%), Uncertainty analysis (WCB = 0.0134), and computational cost (= 0.0180) demonstrate that PSO_DRVM model MD29 outperformed better than other RVM models in the testing phase. This study will help mining and civil engineers and blasting experts to select the best kernel function and its hyperparameters in estimating ground vibration during rock blasting project. In the context of the mining and civil industry, the application of this study offers significant potential for enhancing safety protocols and optimizing operational efficiency.

Experimental study on dynamic response of hard rock blasting under in-situ stress

Deep mine rock mass is in high static stress and dynamic disturbance coupling conditions, its mechanical properties and failure mode is different from the shallow rock mass, which leads to low rock blasting efficiency and engineering geology hazards. In-depth research on the dynamic response of rock blasting under in-situ stress will help to optimize the blasting design, improve the blasting efficiency and safety of blasting operations, and provide theoretical support for rock blasting in deep mines. In this study, the blasting experiment was conducted on granite specimens under different biaxial static stress conditions. Meanwhile, the dynamic response of rock blasting was monitored, collected, and analyzed using a high-speed digital image correlation (DIC) measurement system, a strain wave acquisition system, and an acoustic emission (AE) system. The results show that small and medium pre-static loads inhibit blast crack propagation, at which time the cumulative AE hits from dynamic loads (CAECd) are more than those from pre-static loads (CAECs), but large pre-static loads promote crack propagation, at which time CAECs are more than CAECd. Secondly, as pre-static load increases, the specimen's maximum strain (εmax) decreases first and then increases, but as lateral pressure coefficient (K) increases, the εmax in the direction of lower static stress decreases gradually and the εmax in the direction of higher static stress remains constant. In addition, the confining pressure magnitude and K affect the area and shape of the failure zone of the specimen, as well as the size and propagation direction of the radial crack. Especially when the confining pressure is high, the specimen will undergo shear failure, and the smaller K is the more serious the shear failure. Finally, the failure criterion of rock under dynamic-static coupling conditions is proposed based on the energy index, and different failure types of rock are discussed.

Optimizing Mean Fragment Size Prediction in Rock Blasting: A Synergistic Approach Combining Clustering, Hyperparameter Tuning, and Data Augmentation

Accurate estimation of the mean fragment size is crucial for optimizing open-pit mining operations. This study presents an approach that combines clustering, hyperparameter optimization, and data augmentation to enhance prediction accuracy using the Xtreme Gradient Boosting (XGBoost) regression model. A dataset of 110 blasts was divided into 97 blasts for training and testing, whereas a separate set of 13 new, unseen blasts was used to evaluate the robustness and generalization of the model. Hierarchical Agglomerative (HA) and K-means clustering algorithms were used, with HA clustering providing a higher cluster quality. To address class imbalance and improve model generalization, a synthetic minority oversampling technique for regression with Gaussian noise (SMOGN) was employed. Hyperparameter tuning was conducted using HyperOpt by comparing Random Search (RS) with the Advanced Tree-structured Parzen Estimator (ATPE). The combination of ATPE with HA clustering and SMOGN in an expanded search space produced the best results, achieving superior prediction accuracy and reliability. The proposed HAC1-SMOGN model, which integrates HA clustering, ATPE tuning, and SMOGN augmentation, achieved a mean squared error (MSE) of 0.0002 and an R2 of 0.98 on the test set. This study highlights the synergistic benefits of clustering, hyperparameter optimization, and data augmentation in enhancing machine learning models for regression tasks, particularly in scenarios with class imbalance or limited data.

Mean Block Size Prediction in Rock Blast Fragmentation Using TPE-Tree-Based Model Approach with SHapley Additive exPlanations

Mean Block Size Prediction in Rock Blast Fragmentation Using TPE-Tree-Based Model Approach with SHapley Additive exPlanations

Experimental Study for the Matching of Explosives and Rocks Based on Rock Hydrophysical Properties

The study of the hydrophysical properties of rocks is indispensable for the development of hydraulic engineering, especially for blasting operations in water. Reasonable matching between explosives and rocks increases the utilization of explosive energy and improves the blasting performances. Based on the energy law in the rock blasting process, the matching relationship between explosives and rock is studied by combining experimental and theoretical methods for the hydrophysical properties of the rock itself. Firstly, the theoretical solutions for crushing-zone energy, fragmentation energy and fragment-throwing energy are derived. Subsequently, concrete blocks are prepared with four types of cement–sand ratios, and four types of emulsion explosives are used to carry out single-hole blasting tests in which a high-speed camera is used to capture the trajectory of the blasting fragments that are later collected. Finally, the crushing energy, fracturing energy and fragment-throwing energy are calculated according to the test results and the basic parameters of the used explosives and concrete models. The results show that the size and distribution pattern of blasting blocks are significantly affected by the hydrophysical properties of concrete and explosive properties; the higher the energy consumption in the rupture zone, the smaller the size of the fragments and the more uniform the distribution. Moreover, the median utilization efficiency of explosive energy on rock breaking is 26.4%, the energy consumption in the crushing zone is approximately 8.4%, that in the rupture zone is approximately 10.9%, and that in the throwing energy of fragments accounts for approximately 7.1%. It is also found that the traditional wave impedance matching theory fails to obtain the best explosive energy utilization. On the contrary, the concrete specimen had the best fracturing effect and the highest energy utilization of 30.77% when the impedance ratio of concrete to explosives is 1.479.

Experimental investigation on rockburst characteristics of highly stressed D-shape tunnel subjected to impact load

Rockburst has always been a challenge for the safe construction of deep underground engineering. This study investigated the rockburst characteristics in highly-stressed D-shape tunnels under impact loads from rock blasting and other mining-related dynamics disturbances. The biaxial Hopkinson pressure bar was utilized to apply varying biaxial prestress and the same impact loads to cube specimens with D-shape hole. High-speed camera and digital image correlation (DIC) were used to capture the failure process and strain field of specimen. The test results demonstrate that the D-shape hole specimen experience rockburst under coupled static stress and impact load. Under this circumstance, the rockburst mechanism of the D-shaped hole specimens involves spalling in sidewall induced by impact load, indicating dynamic tensile failure. The high static prestress provides the initial stress field, while the impact load disrupts the stress equilibrium, result in the stress or strain concentration in the sidewall of the D-shape hole, inducing rockburst. Moreover, the rockburst process can be divided into (1) calm stage, (2) crack initiation, propagation, and coalesce stage, (3) spalling stage and (4) rock fragments ejection stage. Impact load triggers rockburst occurrence, while vertical stress further determines the rockburst characteristics. The influence range and magnitude of strain concentration zone and displacement deformation of the tunnel surrounding rock increases with increasing vertical stress, thus inducing more severe rockburst.

Assessment of Health Impacts of Rock Blasting Activities on Ntotoroso and Gyedu Communities, Ahafo Region, Ghana

Assessment of Health Impacts of Rock Blasting Activities on Ntotoroso and Gyedu Communities, Ahafo Region, Ghana

The Role of Delays in the Performance of Blasting

When researching rock blasting, the design parameters typically used for analysis are the geometric and charging parameters. This study focuses on a different approach based on the effect of timing, specifically the role of delays in the initiation sequence. Data were obtained from the results of full-scale blasts. The experimental setting and location allowed us to consider all parameters, other than the number of delays, as constants. The experimental results were analyzed, relating the delay variables to the fragmentation and KPIs of downstream operations. It was found that increasing the number of delays per unit of blasted rock and reducing simultaneous adjacent holes produces finer fragmentation, reduces the amount of fines, facilitates secondary operations, and reduces the risk of flyrock.

A Case Study of Fragmentation Improvement by Primer Placement in Kevitsa Mine

The effect of detonator placement on rock fragmentation by blasting was studied in the Kevitsa open-pit mine. Considering rock mass properties, explosives properties, and blast parameters, a comprehensive analysis of 37 production blasts confirms that the middle detonator yields finer fragmentation than the bottom detonator in the full range of investigated sizes. Meanwhile, using the LS-DYNA code, a bench model with a single blasthole was built in a three-dimensional setting. A single detonator placed at two different positions—the middle and bottom—of the explosive column was analyzed in this study. By analyzing principal stress states on the plane through the blasthole axial and vertical to the bench face, the numerical results show that the middle detonator creates a stress concentration area near the bench face characterized by triaxial tension, while the bottom detonator does not. Furthermore, the middle detonator generates a larger stress concentration area with biaxial tension near the bench face than the bottom detonator. Considering that triaxial and biaxial tensions are more favorable to fragmentation than uniaxial tension, it can be concluded from simulation results that these areas with triaxial tension and biaxial tension in the middle detonator are a significant factor contributing to achieving better fragmentation, compared to the bottom detonator. The middle detonator position has replaced the bottom detonator position in the Kevitsa mine.

Performance of different fibre types in alkaline-activated concrete produced with industrial wastes: volcanic rock powder and blast furnace slag

The objective of the study is to determine the effects of different fibre types and ratios on the mechanical and physical properties of alkali-activated concrete (AAC) and to optimise important properties such as workability, unit weight, water absorption, compressive strength, modulus of elasticity and abrasion resistance of these concretes. Concrete samples were produced using steel and polyester fibres at ratios of 0.25%, 0.50%, 0.75% and 1%. The fibre ratios were varied, and various tests were conducted to determine the mechanical and physical properties of the concrete samples. The experiments clearly revealed the effects of fibre types and ratios on the performance of concrete. These results clearly demonstrate the effects of different fibre types and ratios on the mechanical and physical properties of AACs. Particularly noteworthy is the water-retaining property of polyester fibres, their effect on workability and their enhancing effect on abrasion resistance. The effects of steel fibres on compressive strength and modulus of elasticity are more pronounced, but they may present disadvantages in terms of workability and abrasion resistance. It is observed that mixed fibre usage provides a good balance in some properties. This study provides a roadmap for evaluating sustainable material options in the concrete industry and guiding future research.

Investigating the influence of discontinuity parameters on blast-induced fragmentation

ABSTRACT Fragmentation by blasting is a critical process in hard rock mining. The interaction between the rock mass and blast energy influences the outcomes, specifically on the rock mass response to higher stresses and loading rates, and the properties of structures. In this study, LS-DYNA is used to model the nonlinear dynamic fracturing of the rock to understand the effects of rock mass properties on the blast outcomes. The results are verified and compared with the case study, and they show that the formation of damage zones around the charge is highly influenced by rock mass strength and discontinuity properties.

Fostering sustainable mining practices in rock blasting: Assessment of blast toe volume prediction using comparative analysis of hybrid ensemble machine learning techniques

Blast toe volume, pivotal in explosive engineering, underpins explosive energy efficient utilization, blast safety and mine production sustainability. While current research explores the use of artificial intelligence (AI) model to maximize toe volume prediction, gaps persist in understanding the application of ensemble learning algorithm techniques like hybrid and voting techniques in addressing toe volume problem. Bridging these gaps promises enhanced safety and optimization in blasting operations. This study performs AI model hybrid and voting to enhance toe volume prediction model robustness by leveraging diverse algorithms, mitigating biases, and optimizing accuracy. The study combines separate models, looks for ways that hybrid approaches can work together, and improves accuracy through group voting in order to give more complete information and more accurate predictions for estimating blast toe volume in different approaches. To develop the models, 457 blasting data was collected at the Anguran lead and zinc mine in Iran. The accuracy of the developed models was assessed using nine indices to compare their prediction performance. To understand the input relationship, multicollinearity, Spearman, Pearson, and Kendall correlation analyses show that there is a strong link between the size of the toe and the explosive charge per delay. Findings from the model analysis showed that the light gradient boosting machine (LightGBM) was the most accurate of the eight traditional models, with R2 values of 0.9004 for the training dataset and 0.8625 for the testing dataset. The Hybrid 6 model, which combines LightGBM and classification and regression trees (CART) algorithms, achieved the highest R2 scores of 0.9473 in the training phase and 0.9467 in the testing phase. The Voting 8 models, consisting of LightGBM, gradient boosting machine (GBM), decision tree (DT), ensemble tree (ET), random forest (RF), categorical boosting (CatBoost), CART, adaptive boosting (AdaBoost) and extreme gradient boosting (XGBoost) had the greatest R2 scores of 0.9876 and 0.9726 in both the training and testing stages. Using novel modelling tools to forecast blast toe volume in this study allows for resource extraction optimization, decreases environmental disturbance through mine toe smoothening, and improves safety, supporting sustainable mining practices and long-term sustainability.