The evaluation and classification of goaf stability are fuzzy and random. To address this problem, a new classification method is proposed. A cavity monitoring system is used to detect the goaf, 3DMine and FLAC3D software are used to conduct the 3D visual modeling of the scanning results, and numerical simulation analysis is performed on the goaf. According to the analysis results, the stability classification standard of the goaf is constructed, and the characteristics of each classification are described. The evaluation indicator system of goaf stability is constructed in accordance with similar engineering experience, and the evaluation indicator is weighted by using the analytic hierarchy process. The cloud–element coupling evaluation model is built, the field measured values of indicators are collected, the cloud correlation degree of goafs belonging to each stability level is calculated, the stability level is evaluated according to the principle of maximum membership degree, and the results are compared with the numerical simulation to analyze the reasons for the differences in the stability evaluation results obtained by the two methods and to improve the accuracy of the evaluation of goaf stability. The pillar stress and surrounding rock deformation are monitored in Room 1# of the inclined mining area of Shirengou Iron Mine. The monitoring results are consistent with the evaluation results, which proves the accuracy of the proposed goaf stability classification method.

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.

Roof-contact backfilling is a critical determinant of stope stability in cut-and-fill mining, and the rheological properties of backfill slurry decisively influence the quality of roof contact. To investigate the flow characteristics of unclassified tailings backfill slurry and their effect on rheological parameters, this study uses the Daye Iron Mine as its engineering case. It adopts a combined laboratory and numerical simulation approach. The physicochemical characteristics of the unclassified tailings and the rheological behavior of the slurry were systematically characterized using particle-size analysis, density measurements, spreadability tests, and rheometer measurements. Subsequently, a numerical model of the L-type flow tester was developed in COMSOL Multiphysics (6.4) to simulate the flow process at varying concentrations. Based on the simulation results, a Gaussian process regression (GPR)-based inversion model for rheological parameters was proposed, and the predictive performance of different kernel functions was compared and evaluated. Finally, the existing backfilling scheme at the Daye Iron Mine was optimized based on the obtained rheological characteristics to improve the roof-contact rate. The results indicate that the unclassified tailings from the Daye Iron Mine have a median particle size of 12.1 μm and a density of 2855 kg·m⁻³, with CaO, Al₂O₃, and MgO as the primary active components. Under the same cement-to-tailings ratio, slurry flowability decreases markedly with increasing concentration. The rheological curves exhibit three stages, with the third conforming to the Bingham model; both yield stress and viscosity increase exponentially with concentration. Evaluation of the inversion results demonstrates that the GPR model with the Rational Quadratic (RQ) kernel achieves optimal performance. The recommended slurry concentration for the Daye Iron Mine is determined to be in the range of 69–71%, and the recommended spacing between filling pipelines is 13.34–18 m. This study reveals the flow evolution patterns of unclassified tailings backfill slurry, demonstrates the potential of the GPR-based inversion approach, and optimizes the roof-contact backfilling scheme, offering a scientific reference for flow characterization and backfill optimization in analogous mining operations.