The size distribution of rock fragments significantly influences subsequent operations in geotechnical and mining engineering projects. Thus, accurate prediction of this distribution according to the relevant blasting design parameters is essential. This study employs artificial intelligence methods to predict the fragmentation of open-pit bench blasting. The study employed a dataset comprising 97 blast fragment samples. Random forest and XGBoost models were utilized as base learners. A prediction model was developed using the stacking integrated strategy to enhance predictive performance. The model’s performance was evaluated using the coefficient of determination (R²), the mean square error (MSE), the root mean square error (RMSE), and the mean absolute error (MAE). The results indicated that the model achieved the highest prediction accuracy, with an R² of 0.943. In the training set, the model achieved MSE, RMSE, and MAE values of 0.00269, 0.05187, and 0.03320, while in the testing set, these values were 0.00197, 0.04435, and 0.03687, respectively. The model was validated using five sets of actual blasting block data from a northeastern mining area, which yielded more accurate prediction results. These findings demonstrate that the stacking strategy effectively enhances the prediction performance of a single model and offers innovative approaches to predicting blasting block size.

In this study, immersion experiments were conducted on the geopolymer mortar (GPM) by using artificial seawater, and the effects of alkali equivalent (AE) and waterglass modulus (WGM) on the resistance of geopolymer mortar (GPM) to seawater immersion were analyzed. The test subjected 300 specimens to 270 days of artificial seawater immersion and periodic performance tests. Alkali equivalent (AE) (3–15%) and waterglass modulus (WGM) (1.0–1.8) were employed as influencing factors, and the mass loss and uniaxial compressive strength (UCS) were used as the performance evaluation indexes, combined with X-ray diffraction (XRD) and scanning electron microscopy (SEM) to analyze the time-varying pattern of geopolymer mortar (GPM) performance with seawater immersion. The findings demonstrated a general trend of initially growing and then declining in the uniaxial compression strength (UCS) of geopolymer mortar (GPM) under seawater immersion. The resistance of geopolymer mortar (GPM) to seawater immersion decreased with both higher or lower alkali equivalent (AE), and the ideal range of alkali equivalent (AE) was 9–12%. The diffusion layer of the bilayer structure of the waterglass particle became thinner with an increase in waterglass modulus (WGM), which ultimately led to the reduction in the resistance of the geopolymer structure to seawater immersion. Additionally, a support vector regression (SVR) model was developed based on the experimental data to predict the uniaxial compression strength (UCS) of GPM under seawater immersion. The model performed better and was able to achieve accurate prediction within 1–2 months, and provided an accurate approach to predicting the strength of geopolymer materials in a practical offshore construction project.

Dynamic impact experiments based on high-speed photography and digital image correlation (DIC) techniques were carried out on sandstone specimens containing arched holes to investigate the effect of the incident angle. In addition, the complex function method based on conformal mapping was used to theoretically calculate the transient dynamic stress distributions around the arched holes. The test results indicated that the strength and modulus of elasticity of the specimens under dynamic impact decreased and then increased with the increase of the inclination angle of the holes from 0 to 90° at intervals of 15°, reaching a minimum value at 60°, due to the large stress concentration at this angle leading to the shear failure of the specimen. During the experiment, rock debris ejections, spalling, and heaving were observed around the holes, and the rock debris ejections served as an indicator to identify the early fracture. The damage mechanism around the holes was revealed theoretically, i.e., the considerable compressive stress concentration in the perpendicular incidence direction around the arched hole and the tensile stress concentration on the incidence side led to the initiation of the damage around the cavity, and the theoretical results were in satisfactory agreement with the experimental results. In addition, the effect of the initial stress on the dynamic response of the arched tunnel was discussed.

Based on complex conformal mapping and Duhamel’s integral method the steady and transient dynamic stress distribution around an elliptical cavity subjected to plane P-wave was obtained. The influence of incident angle(θ0), axial ratio(k) and normalized wave length (t0) on the dynamic stress concentration factor (DSCF) was evaluated. Further, the finite element method (FEM) software LS-DYNA was utilized to validate the analytical solution. The results indicated that the maximum compression DSCF increased with θ0, except θ0=0, and decreased with k. When θ0=0 the maximum tensile DSCF increase with the decrease of k. The position of maximum compression and tensile DSCF varied with incident θ0. Under the transient incident condition DSCF was affected by normalized wave length t0, with the increasing of t0 the DSCF gone up then down finally gone closed to the static stress concentration factor. Further, the stress state around the elliptical cavity under transient impact loading was compared with the experimental results of elastic stress distribution and plastic failure and got a good agreement, and possible dynamic failure modes of elliptical cavity were discussed.