In the context of deep mining and green low-carbon transition, this study characterizes the thermo-mechanical evolution and fracture mechanisms of cemented tailings backfill (CTB) through systematic experiments conducted at 20–60 °C across 3–28 days. Results demonstrate that strength and elastic modulus follow a unimodal dependence on temperature, peaking at 40 °C. Gaussian modeling reveals that curing times narrow the thermal tolerance window, with the elastic modulus exhibiting higher sensitivity to overheating. A consistent “pre-peak activity window” is identified in AE responses, characterized by b-value drops and an increase in tensile event proportions from 66% to 83%. A composite AE damage index (ADI) is introduced to systematically precede macroscopic failure, with thresholds of ADI ≥ 0.60 and 0.70 indicating accelerated crack propagation and imminent instability, respectively. Microstructural analysis confirms that 40 °C promotes C-S-H and fine ettringite bridging, whereas temperatures ≥ 50 °C induce Ca(OH)₂ coarsening and enhanced pore connectivity, triggering early tensile-dominated degradation. This study establishes a “temperature → hydration/porosity → AE response → mechanical evolution” pathway, providing an optimal curing window of 40 ± 5 °C and an ADI-based early-warning criterion for temperature-adaptive CTB design and on-site safety management.

In backfill mining of metal mines, the sedimentation and shrinkage of backfill slurry critically affect roof-contact quality and stope stability. This study investigates these characteristics for unsorted tailings backfill slurry from the Daye Iron Mine through laboratory experiments and develops a prediction model for the shrinkage rate. Tests examined the effects of cement-to-tailings ratio (1:4 to 1:20) and slurry concentration (65% to 71%) on shrinkage rate over curing time. A crested porcupine optimizer-backpropagation (CPO-BP) neural network model was developed to predict the shrinkage rate and compared with the traditional BP model. The robustness and generalization capability of the model were further verified by repeated five-fold cross-validation and by comparison with the GA-BP, PSO-BP, random forest, support vector regression, and XGBoost models. Results show that shrinkage predominantly occurs within the first 8 h and stabilizes after 24 h; high-concentration slurries stabilize earlier. At a fixed concentration, the shrinkage rate increases with decreasing cement-to-tailings ratio; at a fixed ratio, it decreases with increasing concentration. The CPO-BP model significantly outperforms the BP model, achieving a mean squared error (MSE) of 3.0767 × 10⁻⁶, a mean absolute error (MAE) of 1.4528 × 10⁻³, and a coefficient of determination (R²) of 0.94946 on the test set. For the Daye Iron Mine, a slurry concentration of 69–71% reduces shrinkage height and improves roof-contact rate. This study reveals the evolution of shrinkage rate with curing time, cement-to-tailings ratio, and concentration, demonstrates the effectiveness of the CPO-BP model, and optimizes the backfilling scheme, providing a theoretical basis for roof-contact quality control in practice.

Numerous factors influence the formation of blasting craters in engineering blasting. Based on the actual parameters of the Daye Iron Mine, this study established six sets of single-hole blasting crater numerical models with different borehole diameters using ANSYS(19.0)/LS-DYNA(R13) software. The variation in blasting crater volume with the scaled depth was analyzed to determine the optimum scaled depth for each borehole diameter, and a functional relationship between the optimum scaled depth and borehole diameter was derived through curve fitting. Furthermore, using a borehole diameter of 0.076 m as a case study, a double-hole blasting crater was developed to investigate the effect of varying hole spacing on blasting crater volume and to determine the optimal hole spacing. The blasting parameters were optimized based on the numerical simulation results. The results show that within the range of borehole diameters considered, the blasting crater volume initially increases and then decreases with increasing scaled depth of the explosive charge. The fitted relationship between the optimum scaled depth and borehole diameter is y = −180.7197x³ + 86.3754x² − 9.5504x + 1.0782. For a borehole diameter of 0.076 m, the optimum scaled depth is 0.7278 m/kg^1/3, and the optimal hole spacing is 0.52 m. Based on blasting similarity theory, the calculated optimum burial depth of the explosive charge is 0.59 m, the critical burial depth is 1.1 m, and the recommended row spacing ranges from 0.95 m to 1.18 m. The findings of this study provide a theoretical basis for optimizing blasting parameters at the Daye Iron Mine and similar mining operations.