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To deeply understand the rock failure characteristics under actual engineering condition, in which static geo-stress and dynamic disturbance usually act simultaneously, impact tests were conducted on sandstone subjected to axial static pre-stresses varying from 0 to 75 MPa by a modified split Hopkinson pressure bar. The fracturing process of specimens was recorded by a high speed camera. Dynamic parameters of sandstone, such as strain rate, dynamic strength and energy partition were acquired. Fracture mechanisms of pulverized specimens were identified by the method combining the displacement trend line and digital image correlation technique. Moreover, fragments of failed specimens were sieved to obtain the fragment size distribution. Test results revealed that, under the same incident energy, the dynamic compressive strength increases first, then decreases slowly and at last drops rapidly with the increase of pre-stress, and reaches the maximum under 24.4% of uniaxial compressive strength due to the closure of initial defects. Four final patterns were observed, namely intact, axial split, rock burst, and pulverization. The rock burst only occurs when the pre-stress lies in the elastic deformation stage or initial stable crack growth stage and the incident energy is intermediate. For pulverized specimens, the fracture mechanism is transformed into shear/tensile equivalent from tensile-dominated mixed mode as the pre-stress increases. Specimens with 75 MPa pre-stress release strain energy during failure process, contrary to specimens with lower pre-stresses absorbing energy from outside. The crushing degree of pulverized specimens exhibits a positive correlation with the pre-stress as a consequence of higher damage development in rock.

The worldwide mining industry consumes a vast amount of energy in reduction of fragment size from mining to mineral processing with an extremely low-energy efficiency, particularly in ore crushing and grinding. Regarding such a situation, this article describes the effects of rock fragmentation by blasting on the energy consumption, productivity, minerals’ recovery, operational costs in the whole size reduction chain from mining to mineral processing, and the sustainability of mining industry. The main factors that influence rock fragmentation are analysed such as explosive, initiator, rock, and energy distribution including blast design, and the models for predicting rock fragmentation are briefly introduced. In addition, two important issues—fines and ore blending—are shortly presented. Furthermore, the feasibility of achieving an optimum fragmentation (satisfied by a minimum cost from drilling-blasting to crushing-grinding, maximum ore recovery ratio, high productivity, and minimum negative impact on safety and environment) is analysed. The analysis indicates that this feasibility is high. Finally, the measures and challenges for achieving optimum fragmentation are discussed.HighlightsThe effects of rock fragmentation on the whole size reduction chain from mining to mineral processing are described.The main factors influencing rock fragmentation by blasting are analysed.Main models for predicting rock fragmentation are briefly introduced and commented on.The feasibility, measures, and challenges of achieving optimum fragmentation are analysed.

With the increasing number of long tunnelling and urban subway constructions, mixed-face ground conditions are frequently encountered. Rock fragmentation mechanism under disc cutter cutting in TBM tunneling through the mixed-face ground is complex and can lead to engineering difficulties. During TBM tunneling in mixed-face ground with soft rock in upper layer and hard rock in the lower layer, reduction of the advance rate and reduced rotational speed of cutter head occur compared with homogeneous ground. As a result, the muck in the working chamber cannot be replaced timely, leading to the formation of mud cake. Additionally, the disc cutters cannot rotate normally and are worn eccentrically and severely. Finally, the cutters collide with hard rock periodically at the interface between soft and hard rock, thus being subject to a huge impact load, even overload on some cutters, resulting in chipping of the cutter ring and damage to the cutter holder. This paper presents numerical analysis of the disc cutter cutting process considering the difference of rock-cutting behaviors of disc cutters in the mixed-face ground with the aid of PFC3D code. Based on the forces imposed on the disc cutter and rock crack propagation, TBM tunneling in the mixed-face ground is investigated. The decrease of the mean rolling force of the disc cutter causes rotation hindering in the disc cutter in soft rock stratum leading to flat cutter wear. The gap of the normal force between the soft rock and hard rock generates the overturning moment of the cutter head, which causes the eccentricity and vibration of the cutter head.

Microwave energy can be used to assist mechanical rock breakage for civil and mining engineering operations. To assess the industrial applicability of this technology, microwave heating of basalt specimens in a multi-mode cavity (a microwave chamber) at different power levels was followed by conventional mechanical strength and fragmentation effect tests in the laboratory. X-ray diffraction and scanning electron microscopy/energy-dispersive X-ray spectroscopy were used to determine the mineral composition and distribution of the basalt, to aid interpretation of crack propagation patterns and the associated strength reduction mechanism. These analyses demonstrated that cracks mainly occurred around olivine grains, primarily as intergranular cracks between olivine and plagioclase grains and intragranular cracks within olivine grains. Strength reduction during microwave fracturing of basalt is driven by heat from enstatite (a microwave-sensitive mineral) and volumetric expansion of olivine (a thermally expansive mineral). Uniaxial compressive, Brazilian tensile, and point load strengths all decreased with increasing microwave irradiation time at rates that were positively related to the power level. For a given power level, mechanical strength reduction can be estimated from linear relationships with irradiation time. On the other hand, a power function best described burst time (the irradiation time at which the specimen burst into fragments) vs. power level (for a given specimen size) and burst time vs. specimen size (for a given power level) relationships. Microwave-induced hard rock fracturing can be an integral part of new methods for rock breakage and tunnel excavation.

The main focus of the present work is to offer an auto-tuning model, called cat swarm optimization (CSO), to predict rock fragmentation. This population-based method has a stochastic formation involving exploration and exploitation phases. CSO is a robust and powerful meta-heuristic algorithm inspired by the behaviors of cats; it is composed of two search modes: seeking and tracing, which can be joined by mixture ratio parameter. CSO is applied to large-scale optimization problems like rock fragmentation to have good forecasting parameters in D80 formulas (D80 is a common descriptor that evaluates rock fragmentation). To evaluate the efficiency of the proposed CSO model, its obtained results were compared to those of the particle swarm optimization (PSO) algorithm. In the modeling, two forms of CSO and PSO models, including power and linear forms, were developed. The comparative results showed that CSO models outperformed the rival in terms of the task defined. The precision of the proposed models was computed using statistical evaluation criteria. Comparison results concluded that CSO-power model with the root mean square error (RMSE) of 0.847 was more computationally efficient with better predictive ability compared to CSO-linear, PSO-linear and PSO-power models with the RMSE of 1.314, 1.545 and 2.307, respectively. Furthermore, the sensitivity analysis revealed the effect of the stemming parameter upon D80 in comparison with other input parameters.

Poor fragmentation is one of the most side effects induced by blasting operations. Therefore, risk assessment and prediction of rock fragmentation are essential to reduce the mentioned effects. In the present study, an attempt has been made to evaluate the risk associated with rock fragmentation as well as its prediction at Sarcheshmeh copper mine, Iran, proposing the rock engineering system (RES) technique. A total number of 52 blasting events were collected and considered and the values of 10 key effective parameters in rock fragmentation were carefully measured in the mine. These 10 key parameters were only related to blasting design and rock mass properties were not considered in the analysis of this study due to some limitations regarding their measurements in the mine. The RES result showed that the level of overall risk, based on the considered blast events, is in the range of medium–high. Furthermore, it was found that the burden is the most interaction factor in the rock fragmentation. In case of rock fragmentation prediction, all of datasets were divided randomly to training and testing datasets for proposing RES model. For comparison purpose, non-linear multiple regression (NLMR) was also employed for estimating rock fragmentation. The performances of the proposed predictive models were examined according to three performance indices, i.e. coefficient of determination ( R 2 ), root mean square error (RMSE) and variance account for (VAF). The obtained results of this study indicated that the RES is a reliable method to predict rock fragmentation with a higher degree of accuracy in comparison to NLMR model. For instance, RMSE values of 1.95 and 4.002 for testing datasets of RES and NLMR models, respectively, suggest the superiority of the RES model in predicting rock fragmentation compared to other developed model.

In blasting works, the aim is to provide proper rock fragmentation and to avoid undesirable environmental impacts such as back-break. Therefore, predicting fragmentation and back-break is a significant step in achieving a technically and economically successful outcome. In this paper, considering the robustness of artificial intelligence methods utilized in engineering problems, an artificial neural network (ANN) was applied to predict rock fragmentation and back-break; an artificial bee colony (ABC) algorithm was also utilized to optimize the blasting pattern parameters. In this regard, blasting parameters, including burden, spacing, stemming length, hole length and powder factor, as well as back-break and fragmentation were collected at the Anguran mine in Iran. Root mean square error (RMSE) values equal to 2.76 and 0.53 for rock fragmentation and back-break, respectively, reveal the high reliability of the ANN model. In addition, ABC algorithm results suggest values of 29 cm and 3.25 m for fragmentation and back-break, respectively. For comparison purposes, an empirical model (Kuz-Ram) was performed to predict the mean fragment size in the Anguran mine. A mean fragment size of 33.5 cm shows the ABC algorithm can optimize rock fragmentation with a high degree of accuracy.

Cut blasting with single free surface is the first step of the large-diameter long-hole (LDL) mining method, which creates more relief space for subsequent blasting procedures. The minimum burden (W) is a critical factor affecting the cut blasting effect, but its influence on rock fragmentation and damage, and stress wave attenuation is not completely clear. In this study, nine groups of small-scale model tests were designed to explore the effect of W on rock fragmentation and damage, and stress wave attenuation in cut blasting. First, the strain caused by blasting in each model test was measured. Then the degrees of rock fragmentation and rock damage range were calculated. The results show that when the stemming length is the same, with the increase of W, the total mass of the fragments, the maximum fragment size, the mean radius and area of the crater, and the fragmentation energy consumption all first rise and then decrease. As W increases, the fragmentation energy density and fractal dimension both decrease, implying that the rock fragmentation effect becomes worse. The damage factor shows a power function attenuation with the increase of the distance from the explosion source, and the damage radius decreases with the increase of W. When the W is the same, 4 cm stemming improves the utilization rate of fragmentation energy consumption by 2.79–3.58 times compared with no stemming. The tensile strain has a more severe attenuation than the compressive strain, and the reflected tensile wave plays a significant role in the formation of blasting crater. This study is helpful to understand the rock fragmentation and stress wave attenuation in cut blasting of LDL stopes.HighlightsThe closer the blasting crater is, the more significant the tensile strain is. The sharply changing reflected tensile wave plays a significant role in the process of crater formation and crack propagation. The attenuation of tensile strain with distance is more significant than that of compressive strain. The fragmentation energy consumption utilization rate of 4 cm stemming is 2.79–3.58 times that without stemming.The damage factor shows a power function attenuation with the increased distance from the explosion source. The damage radius decreases with the increase of burden.

Desired rock fragmentation is the main goal of the blasting operation in surface mines, civil and tunneling works. Therefore, precise prediction of rock fragmentation is very important to achieve an economically successful outcome. The primary objective of this article is to propose a new model for forecasting the rock fragmentation using adaptive neuro-fuzzy inference system (ANFIS) in combination with particle swarm optimization (PSO). The proposed PSO–ANFIS model has been compared with support vector machines (SVM), ANFIS and nonlinear multiple regression (MR) models. To construct the predictive models, 72 blasting events were investigated, and the values of rock fragmentation as well as five effective parameters on rock fragmentation, i.e., specific charge, stemming, spacing, burden and maximum charge used per delay were measured. Based on several statistical functions [e.g., coefficient of correlation ( R 2 ) and root-mean-square error (RMSE)], it was found that the PSO–ANFIS (with R 2  = 0.89 and RMSE = 1.31) performs better than the SVM (with R 2  = 0.83 and RMSE = 1.66), ANFIS (with R 2  = 0.81 and RMSE = 1.78) and nonlinear MR (with R 2  = 0.57 and RMSE = 3.93) models. Finally, the sensitivity analysis shows that the burden and maximum charge used per delay have the least and the most effects on the rock fragmentation, respectively.

Accurately predicting the particle size distribution of a muck-pile after blasting is always an important subject for mining industry. Adaptive neuro-fuzzy inference system (ANFIS) has emerged as a synergic intelligent system. The main contribution of this paper is to optimize the premise and consequent parameters of ANFIS by firefly algorithm (FFA) and genetic algorithm (GA). To the best of our knowledge, no research has been published that assesses FFA and GA with ANFIS for fragmentation prediction and no research has tested the efficiency of these models to predict the fragmentation in different time scales as of yet. To show the effectiveness of the proposed ANFIS-FFA and ANFIS-GA models, their modelling accuracy has been compared with ANFIS, support vector regression (SVR) and artificial neural network (ANN). Intelligence predictions of fragmentation by ANFIS-FFA, ANFIS-GA, ANFIS, SVR and ANN are compared with observed values of fragmentation available in 88 blasting event of two quarry mines, Iran. According to the results, both ANFIS-FFA and ANFIS-GA prediction models performed satisfactorily; however, the lowest root mean square error (RMSE) and the highest correlation of determination (R2) values were obtained from ANFIS-GA model. The values of R2 and RMSE obtained from ANFIS-GA, ANFIS-FFA, ANFIS, SVR and ANN models were equal to (0.989, 0.974), (0.981, 1.249), (0.956, 1.591), (0.924, 2.016) and (0.948, 2.554), respectively. Consequently, the proposed ANFIS-GA model has the potential to be used for predicting aims on other fields.