To quantitatively describe the damage degree and failure process of the cemented backfill (CB) under dynamic loading, this paper performed numerical split Hopkinson pressure bar (SHPB) impact experiments on CB samples using the ANSYS/LS-DYNA. The damage pattern and failure process of CB samples with four mix ratios (cement-to-sand (c/s) ratios of 1:4, 1:6, 1:8, and 1:10) at different impact velocities (v) (1.5, 1.7, 1.8, and 2.0 m/s) were numerically investigated using the micro-crack density method to define the damage variable (d). The results revealed that the use of a waveform shaper in the numerical simulation yielded a more ideal rectangular wave to ensue uniform stress distribution across the sample’s plane without stress concentration. Numerical simulations effectively depicted the dynamic failure process of the CB, with the overall failure trend exhibiting edge spalling followed by the propagation and interconnection of internal cracks. When the v increased from 1.7 m/s to 1.8 m/s, the d increased by more than 10%. As the v increased from 1.5 m/s to 2.0 m/s, the d for c/s ratios of 1:4, 1:6, 1:8, and 1:10 ranged from 0.238 to 0.336, 0.274 to 0.413, 0.391 to 0.547, and 0.473 to 0.617, respectively. A significant “leap” phenomenon in damage was observed when the c/s ratio changed from 1:6 to 1:8.

In order to ensure the safe operation of adjacent buried pipelines under blast vibration, it is of great practical engineering significance to accurately predict the peak vibration velocity ofburied pipelines under blasting loads. Relying on the test results of the buried steel pipe blast model test, a sensitivity analysis of relevant influencing factors was carried out by using the gray correlation analysis method. A least squares support vector machine (LS-SVM) model was established to predict the peak vibration velocity of the pipeline and determine the best parameter combination in the LS-SVM model through a local particle swarm optimization (PSO), and the results of the PSO-LSSVM model were predicted. These were compared with BP neural network model and Sa’s empirical formula. The results show that the fitting correlation coefficient (R2), root mean square error (RMSE), average relative error (MRE), and Nash coefficient (NSE) of the PSO-LSSVM model for the prediction of pipeline peak vibration velocity are 91.51%, 2.95%, 8.69%, and 99.03%, showing that the PSO-LSSVM model has a higher prediction accuracy and better generalization ability, which provides a new idea for the vibration velocity prediction of buried pipelines under complex blasting conditions.

To investigate damage range and damage degree of PMMA under different decoupling charge coefficients ( K ) after blasting, a reasonable range of decoupling coefficient was theoretically determined. Experimental images after blasting were obtained through two-dimensional (2D) model experiment. Algorithms for computing damage variables and fractal dimensions of cracks in different zones were developed in MATLAB software, and the relationship between fractal dimension and blasting damage was established. The results indicated that as the decoupling coefficient increased, the length of the longest primary crack and the total length of cracks decreased gradually. Damage variable ( ω ) of the decoupled charge blasting decayed exponentially with increasing distance, ultimately approaching zero. Meanwhile, the attenuation rate increased with increasing decoupling coefficient. The relative sizes of fractal dimensions based on the box-counting dimension algorithm correlated with the macroscopic damage variables. Fractal dimensions in the blasting zones I, II, and III decreased sequentially as the decoupling coefficient increased, resulting in an overall reduction in overall fractal dimension. A prediction model for blasting damage in PMMA material under model experiment conditions was established. The calculated blasting damage degree based on fractal dimension can be used to quantitatively evaluate the spatial distribution characteristics of blasting-induced cracks, which provides a new approach to study the evolution patterns of blasting damage of rocks.