When conducting blasting under deep water, the explosive will be affected by high water pressure and long-term water immersion conditions due to the long charging time. Only when the performance of the explosive meets the technical requirements can the blasting effect be guaranteed. Taking the waterway regulation project of Liantuo river between the Three Gorges Dam and Gezhouba Dam as the research background, it was found the original emulsion explosives composition was weak in resisting deep water pressure during the reef blasting. The detonation distance was small, and the intensity was insufficient during the construction at a depth of 40 m. The performance of water gel explosives, chemical-sensitized explosives, glass microsphere-sensitized explosives, and perlite-sensitized explosives were studied by experiments in the laboratory adjusted the hydrostatic surface pressure to simulate the deep-water condition by using the deep-water measurement method. A relationship between the explosive performance and immersion time was explored under different water depth conditions. Meanwhile, a relationship between the explosive performance and water depth was built under different immersion time conditions. Comparing the experimental results, the study shows that the performance of glass microsphere-sensitized explosives, chemical-sensitized explosives, perlite-sensitized explosives, and water gel explosives all slightly decrease and meet the engineering requirements when the water depth is 0~20 m. It is recommended to use glass microsphere-sensitized explosives and perlite-sensitized explosives when the water depth is 20~40 m, and the glass microsphere-sensitized explosives are suitable when the water depth is 40~50 m. Besides, the glass microsphere-sensitized explosives have relatively small performance degradation under high pressure and long-term immersion conditions, and it is suitable for underwater drilling and blasting construction in deep water conditions.

With the gradual increase of mining depth, the engineering geological conditions of deep broken surrounding rock mass become complex and changeable, greatly affecting underground projects' construction process and subsequent use period's safety. In order to ensure the safety and quality of deep broken soft rock roadway during the construction process, advanced roof control reinforcement and controlled blasting technology for soft rock roadway were put forward. In view of the characteristics of highly developed fissures and poor stability of rock mass at the No. 6 intersection of-550 m level in Zhongjiu iron mine, it was proposed to adopt advanced roof control measures to strengthen the surrounding rock mass of the roof and improve the bearing capacity of deep-buried broken soft rock roadway. In order to facilitate the excavation construction, 17 excavation areas were divided along the northeast side of the No. 6 intersection, and a four-step method was used for segmented construction. To realize the hole-by-hole shot and reduce the influence of blasting vibration, the detonation interval between two adjacent digital electronic detonators was randomly set to 3~5 ms. According to the overall lithology of different excavation areas, the support methods (pipe shed support, W-shaped steel belt, anchor cable support, etc.) were optimized to ensure the safety of the subsequent use of the No. 6 intersection. The test results show that the advanced roof control reinforcement and controlled partition blasting technology can reduce the roof deflection and subsidence of broken soft rock roadway, which ensures the forming effect of the No. 6 intersection section and reduces the cost of support and shotcrete by 8.7%.

In view of larger holes and higher density operators with the traditional blasting of packed explosives, this paper launched 60 on-site blasting experiments in a plateau tunnel. The charging efficiency of different numbers of exploders was summarized, the hole network parameters were optimized and smooth blasting effect of peripheral holes were improved. Compared with the smooth blasting effect, the number of holes dropped from 151 to 124. The spacing of peripheral holes was gradually optimized from 45 cm to 60 cm based on the advantages of good mechanical and coupling charging on-site mixed explosives. The result shows that the on-site mixed loading blasting in plateau tunnel can improve more than 40% efficiency per operator and reduce 90% of labor intensity compared with the traditional blasting of packed explosives. It can significantly reduce the number of operators and improve the efficiency of drilling and loading, saving more than 15% of drilling quantity and explosive equipment. Lastly, more than 3% of the utilization of circular footage has been improved, which shows that on-site mixed-loading blasting can accelerate construction progress.

Blasting Engineering is an essential core course for civil engineering and mining majors. To improve the students' understanding on dynamic mechanical response and damage mechanism of rock materials, the experimental teaching contents of explosion and impact dynamics of rock materials were set up in a training plan to achieve the teaching goal of the course of Blasting Engineering. In view of students' lack of theoretical knowledge and experimental basis of impact dynamics in blasting engineering, the split Hopkinson pressure bar (SHPB) experiment technology and two-dimensional plate blasting (TDPB) experiment technology were applied to the practical teaching of Blasting Engineering. Firstly, the experimental system compositions and calculation principles of SHPB and TDPB were introduced. Secondly, the course contents of SHPB impact compression experiment and TDPB central blasting experiment of rock materials were designed. Thirdly, the dynamic mechanical behavior and energy evolution characteristics of sandstone material under the SHPB experiment and the strain wave evolution and dynamic damage and fracture behavior mechanism of sandstone-like material under the TDPB experiment were analyzed. Finally, the students′ in-depth discussion on the critical problems of rock impact dynamics was guided. The innovative combination of SEM testing technology and impact dynamics experimental technology revealed the damage mechanism of rock materials under SHPB and TDPB experiments to students from the micro-level, which gave the students a clear understanding of meso-damage and macro-failure. The effect of teaching practice shows that the student's theoretical and practical ability is exercised by combining the experimental course of SHPB and TDPB with the theoretical course of Blasting Engineering, which leads to the improvement of students' scientific research ability and the sense of teamwork, and the fulfillment of the teaching goals.

In order to study the energy evolution and fracture characteristics of sandwich composite rocks under impact loads, six sets of sandwich composite rock specimens were created using three materials: sandstone, marble, and granite. Dynamic fracture impact tests were then conducted using the Hopkinson pressure bar system to analyze crack propagation morphology, stress wave waveform characteristics, crack tip stress field, and energy loss relationship. The DLSM simulation results were also used to analyze the propagation law of stress waves and the evolution process of kinetic energy during dynamic fracture processes. The results showed a good fit between the dynamic fracture process captured by high-speed cameras and the crack propagation process simulated by DLSM. It was observed that the occurrence of cracks depends on the failure of weak bedding planes. Under the same impact conditions, the lower the strength of the weak bedding planes, the shorter and more advanced the crack propagation time, and the more energy is used for crack propagation. Additionally, under the same incident energy conditions, using a specimen with higher hardness as the impact end material results in a stronger reflection effect, greater reflection energy, weaker transmission effect, smaller transmission energy, and greater energy dissipation of the specimen. The bedding plane was found to have a significant hindering effect on the propagation of stress waves.

In order to investigate the mechanical properties and energy transfer law of jointed deep tuff under one-dimensional dynamic loading, seven kinds of tuff specimens with specified natural joint inclinations were tested by SHPB test device. During the tests, the dynamic process was recorded by high-speed camera in real time. The influence of joint inclination angle on the dynamic response characteristics of deep tuffs was systematically analyzed in terms of dynamic strength, energy dissipation and macroscopic damage. The results show that tuffs has the lowest dynamic strength when the joint inclination is 45°, and the dynamic compressive strength and peak strain show a tendency of decreasing firstly and then increasing in the range of joint inclination angle increasing from 0° to 90°. Besides, the final damage modes of the specimens are controlled by the nodal inclination, and the final damage modes of tuff specimens with different nodal inclinations can be classified into tensile damage, tensile-shear composite damage and shear damage. When the incident energy is roughly the same, the reflection energy ratio and transmission energy ratio of tuff present a decrease-increase trend with the increase of natural joint inclination angle, with the lowest energy ratio at 45°. However, the trend of the dissipation energy ratio is opposite. The energy-time density increased first and then decreased with the increase of joint inclination angle. When the direction of load and joint is within 45°~60°, more energy is absorbed and used for self-crack propagation.

The excavation of deep buried karst tunnel will cause special damage and failure forms of the surrounding rock mass, and the damage zone will also affect the seepage field of the surrounding rock mass and the water inflow condition of the tunnel boundary. To understand the impact of blasting excavation on surrounding rock damage and seepage, a numerical model was created using COMSOL Multiphysics software. The model included a stress-seepage-damage coupling equation for calculations. The stress distribution of surrounding rock during tunnel excavation was calculated using both analytical and numerical methods. The results showed that there was consistency between the two methods. Large tensile stress was observed near the shoulder and foot of the tunnel due to the blasting load. Additionally, a tooth-shaped damage zone was formed in the water-resisting rock mass after blasting, leading to increased infiltration velocity in the shoulder and foot area, which can aid in determining the direction of the cave. Furthermore, changes in cave spacing and diameter, as well as water pressure, can influence the “tooth” extension angle, maximum water inflow position, and water inflow at the tunnel boundary. By considering the extension direction of the “teeth”, a reasonable position for detecting the damage zone can be determined. Moreover, adjustments in water inflow prevention measures and key prevention and control areas can be made based on changes in the maximum water inflow position and water inflow on the tunnel boundary.

Husab Uranium Mine is a super-large-scale open-pit uranium mine. Currently, the mine adopts a “one-time design, long-term use” approach to blasting production, leading to issues such as a lack of dynamic adjustment of blasting parameters, high explosive consumption, and unsatisfactory blasting results. To address these issues, a solution can be achieved through dynamic blastability classification management of blasting blocks and feedbackcontrolled blast design. This study utilizes the production history big data of the mine's blasting blocks. It proposes a method to calculate the blasting index K using drilling rate (α), explosive consumption per unit volume (β), and fragmentation index (γ). Here, α represents the drill hole cross-sectional area per unit area, where a higher value indicates more drilling required and higher drilling costs. β represents the amount of explosives required per unit volume of crushed rock, where a higher value implies a more significant amount of explosives required and higher blasting costs. γ represents the distribution of fragment size after ore blasting, where a higher value indicates worse blasting effects, higher transportation costs, and greater difficulty in blasting. Based on the value of the blasting index K, the blastability of historical blasting blocks is classified into different levels. Uniaxial compressive strength (UCS) of the blasting blocks, rock quality designation (RQD) of the ore, and geological strength index (GSI) of the ore deposit are used as blastability indicators, establishing a dataset correlating blastability indicators with blastability levels. The dataset consists of 69 sets of historical data, with 20 sets classified as level one (easily blastable), 24 sets as level two (relatively difficult to blast), and 25 sets as level three (difficult to blast). Subsequently, a deep learning neural network model is constructed, comprising an input layer, five hidden layers, a dropout layer, and an output layer. The model is trained using blastability indicators as inputs and blastability levels as outputs. The traditional SVM model is used for comparison, revealing that the trained deep learning neural network model achieves higher prediction accuracy on the test set than the traditional SVM model. Finally, the reliability and accuracy of the trained deep learning neural network model in predicting the blastability level of blasting blocks are verified through on-site experiments, optimizing the blast design and blasting effects. The research findings indicate that the trained deep learning neural network model, based on a large amount of historical production data from Husab Uranium Mine, can be used for blastability classification of blasting blocks and optimization of blasting effects.

To investigate the impact of a radially uncoupled charge structure on energy transfer and blasting effects of explosives, with the goal of improving energy efficiency and enhancing rock crushing, dinitrodiazophenol was placed in a standard shale specimen with a diameter of 50 mm and a height of 100 mm. A blasting model experiment was conducted using four radial uncoupled charge coefficients-1, 1.5, 2 and 2.5. The strain waveforms in the axial direction of the specimen were analyzed using the ultra-dynamic strain testing system and the complementary set empirical mode decomposition method. The strain behavior of different sections of the specimen was studied, along with the damage fractal dimension and crack development in these sections. The analysis of the explosive energy propagation laws, combined with the strain curve, revealed that the tensile strain peak values were generally higher than the compressive strain peak values. The specimen eventually failed after experiencing significant and repeated tensile and compressive stresses. Importantly, when the radial uncoupling coefficient was 1.5, the energy utilization of the explosive was significantly improved, along with the prolonged action time of the detonating gas. Additionally, the damage fractal dimension of the specimen section with an uncoupled charge structure changed from top to bottom in an “n-type” manner, resulting in the most uniform damage distribution across each section, a fully expanded crack area, and the best blasting effect.

To accurately predict the peak particle velocity (PPV) and effectively reduce the hazards of blasting vibration, a prediction model was built by BP neural network based on the blasting project of Xingguang No. 1 openpit mine. Seven influencing factors as core distance, plugging length, minimum resistance line, explosives unit consumption, maximum single-hole charge, total extension time, and maximum single-delay charge, were selected as input variables, and the correlation between each factor and PPV was evaluated by using the grey correlation analysis method. The Sparrow Search Algorithm (SSA) optimized the BP neural network to predict the three-way peak vibration velocity. By comparing and analyzing the prediction results of the BP neural network model, the average errors of the prediction results of the SSA-BP neural network model were 6.08%, 7.34%, and 1.91%, respectively, and that of the prediction results of the BP neural network model was 22.19%, 54.01%, and 25.29%, respectively. The results show that the SSA-BP neural network model comprehensively considers the influence of multiple blasting design parameters on the peak vibration velocity. The sparrow search optimization algorithm can effectively solve the problem of the traditional BP neural network model, which quickly falls into the local optimum. The prediction results are more accurate, and the vibration velocity monitoring value is more consistent with smaller errors. Meanwhile, it can significantly shorten the learning and training time of the sample data to speed up the convergence speed of BP. Additionally, it can also significantly shorten the training time of sample data and accelerate the convergence speed of the BP neural network prediction model.