To study the damage law of calcareous conglomerate under blasting, firstly, the damage fracture process and mechanism of calcareous conglomerate under blasting load were revealed based on the theory of damage fracture mechanics. A meso-scale model of conglomerate, including filler, conglomerate and interfacial transition zone (ITZ), was established by using LS-DYNA and Fortran programming, and the propagation law of explosive stress wave and its damage characteristics were analyzed. The damage fracture process of calcareous conglomerate under blasting can be divided into four stages, namely: compression damage in both gravel and fill; tensile damage in gravel and compression damage in fill; tensile damage in both gravel and fill; and tensile damage at the intersection of gravel and fill. Numerical results show that under blasting loads, the gravel has higher equivalent stresses, the fill has the lowest, stress concentration is evident at the ITZ, and the stress gap between the gravel and the fill decreases as the distance increases. The conglomerate sustains relatively minor damage, with a notable phenomenon of damage occurring around it. However, the filler experiences significant damage. The expansion of blasting crack in calcareous conglomerate forms mainly along the direction of stress wave propagation. Cracks tend to develop along the filler with lower physical and mechanical properties, as well as along the junction surfaces. The damage to the gravel is comparatively less severe. Blasting blockiness is mainly manifested as the filler wrapping gravel, and the distribution of blasting blockiness is affected by the bonding force at the intersection surface and the distribution of gravel.

In order to solve the problems of large displacement, high boulder yield and low shovel loading efficiency in the front row of Husab mine blasting operations, a series of buffer blasting trials were designed and conducted. The project has successively completed the buffer blasting tests in 5 large blocks, and each section has 360 000 tons to 530 000 tons of ores, 15~21 rows of holes, and 5~40 meters wide buffer materials on the free surface. The muck pile shape, shovel productivity, fragmentation and floor elevation deviation were measured and compared with the non-buffer blasting results in the same period. The test results show that there are forward pounce and uplift phenomenon to the blasting pile under the impact of explosion. Meanwhile, the muck pile surface presents a shape with unequal heights, and the maximum height difference varies from 4.06 m to 5.85 m. The productivity of a hydraulic shovel is 2722 t/h in the buffer blasting blocks, which is 4.69% higher than that in the non-buffer blasting blocks in the same period. Moreover, the results of fragmentation analysis reveal that the buffer blasting is better than non-buffer blasting on the fragmentation performance, and the buffer blasting has the advantage in reducing boulders. Finally, the test results show that the buffer blasting has certain advantages in floor elevation control. It is the first time to promote buffer blasting technology to the blasting of large sections with multiple rows of holes(15 to 21 rows), which is not only in line with the objective needs of large-scale mine production, but also an inevitable trend as large-scale equipment is used.

To reduce the loss and dilution of ore in Husab Mine, a blasting movement monitoring(BMM) system was introduced and tested in three production blocks with 177 mm diameter drilling holes and a 7.5 m step height. During the field test, 6, 8, and 5 monitoring holes were arranged in each test block. Two displacement monitoring balls were placed in each monitoring hole at a 3.5 m and 9m depth to record the rock body's vertical and horizontal movement after blasting. The results show that the movement of the ore body due to blasting can be detected by BMM, and the average horizontal displacement of the upper ore body of the three test blocks is 6.55 m, 6.97 m, and 9.24 m, respectively. The average horizontal displacement of the lower ore body is 3.2 m, 3.9 m, and 4.0 m, respectively. The average vertical displacement of the upper ore body is 4.1 m, 2.0 m, and 3.2 m, respectively. The average vertical displacement of the bottom ore body is 0.72 m, 0.98 m, and 0.84 m, respectively. The ore body always moves in the direction with the least resistance during blasting. Whether horizontal or vertical displacement, the displacement of the upper ore body is always more significant than that of the lower ore body. In addition to changes in the boundaries between the ore and rock due to horizontal displacement, vertical displacement also has a significant influence on the loss and dilution of ore, and the bottom ore body may also move to the middle or the upper part of the ore body, and vice versa. The blast zone of open pit mines often consists of a variety of rocks, and both horizontal and vertical displacements of the ore body after completing the blast design based on geological information are the leading causes of ore grade reduction. Using the post-blast rock boundaries obtained from the BMM system monitoring to guide the excavation and transportation operations, the average ore dilution rate has been reduced by 1.2%. The average loss rate has been reduced by 1.5%, which can create more than 10 million RMB of economic benefits cumulatively over the entire life of the Husab Uranium Mine. This technology can accurately define the ore-rock boundary after blasts, which is an important technical means to reduce the ore dilution rate, ore loss rate and ore grade classification errors in open pit mines.

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 feedback-controlled 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.