Unconfined compressive strength (UCS) is the most significant mechanical index for cemented backfill, and it is mainly determined by traditional mechanical tests. This study optimized the extreme gradient boosting (XGBoost) model by utilizing the whale optimization algorithm (WOA) to construct a hybrid model for the UCS prediction of cemented backfill. The PT proportion, the OPC proportion, the FA proportion, the solid concentration, and the curing age were selected as input variables, and the UCS of the cemented PT backfill was selected as the output variable. The original XGBoost model, the XGBoost model optimized by particle swarm optimization (PSO-XGBoost), and the decision tree (DT) model were also constructed for comparison with the WOA-XGBoost model. The results showed that the values of the root mean square error (RMSE), coefficient of determination (R²), and mean absolute error (MAE) obtained from the WOA-XGBoost model, XGBoost model, PSO-XGBoost model, and DT model were equal to (0.241, 0.967, 0.184), (0.426, 0.917, 0.336), (0.316, 0.943, 0.258), and (0.464, 0.852, 0.357), respectively. The results show that the proposed WOA-XGBoost has better prediction accuracy than the other machine learning models, confirming the ability of the WOA to enhance XGBoost in cemented PT backfill strength prediction. The WOA-XGBoost model could be a fast and accurate method for the UCS prediction of cemented PT backfill.

The problem of backfilling pipeline invalidation has become a bottleneck restricting the application and development of backfilling technology. This study applied the whale optimization algorithm and random forest (WOA–RF) to predict the invalidation risk of backfilling pipelines based on 59 datasets from actual mines. Eight influencing factors of backfilling pipeline invalidation risk were chosen as the input parameters of the WOA–RF model, and the risk level was selected as the output parameters of the WOA–RF model. Furthermore, random forest, decision tree, artificial neural network, k-nearest neighbor, and support vector machine models were also established according to the collected datasets. The prediction performance of the six classification models was compared. The evaluated results showed that the established WOA–RF hybrid model has the best prediction performance and the highest accuracy (0.917) compared to other models, with the highest kappa value (0.8846) and MCC value (0.8932). In addition, the performed sensitivity analysis showed that the deviation rate is the most important influencing factor, followed by the internal diameter of the pipeline. Eventually, the WOA–RF hybrid model was used to predict the failure risk level of the backfilling pipelines of nine actual mines in Sichuan, China. The field datasets were collected through field investigation, and engineering verification was carried out. The research results show that the WOA–RF hybrid model is reasonable and effective for backfilling pipeline invalidation risk, and it can provide a novel solution for backfilling pipeline invalidation, with good engineering practicability.

A comprehensive understanding of the mechanical behavior of backfill under compression is crucial for optimizing its design, improving stope stability and enhancing resource recovery. Laboratory testing and numerical simulation were conducted to study the mechanical properties and damage mechanism of cemented tailings backfill (CTB) with different cement-to-tailings (c/t) ratios under uniaxial compression. Laboratory testing was used to investigate the strength and deformation characteristics, macroscopic failure modes, and energy evolution patterns of CTB, while simulation with Particle Flow Code (PFC) was employed to explore the distribution of microcracks and mesoscopic damage mechanisms. A constitutive model accounting for the initial compaction stage was proposed, validated, and applied to practical engineering. The results show that as the c/t ratio decreases, the failure mode of CTB transforms from shear failure to combined tensile–shear failure, and tensile failure. Mesoscopically, a higher c/t ratio leads to more bond contacts, which increases the bearing capacity and consequently causes more cracks to damage CTB. From an energy standpoint, the damage mechanism of CTB is further analyzed and the development of energy is characterized by four stages. Moreover, to explore the failure mechanism of CTB, an innovative constitutive model was proposed and verified through experiments. The matching coefficients, based on the novel constitutive model, indicate that CTB with a c/t ratio of 1:6 is qualified for all current mining depths, and a c/t ratio of 1:10 is sufficient to depths below 300 m.

Soil vapor extraction (SVE) technology has strong potential value in the decontamination of soils dominated by volatile contaminants. In this paper, in order to evaluate in detail the influence of the main factors on the efficiency of SVE, L₉(3⁴) orthogonal tests and response surface analysis were carried out using a self-developed one-dimensional SVE system model. A first-order kinetic reaction model was also employed to analyze the relationship between pollutant concentration and time. The thermal reaction unit of SVE technology with a scale consistent with the soil column of the indoor test was simulated using COMSOL simulation software. The obtained results indicate that the most important factors affecting the performance of SVE are time, temperature, and contaminant concentration, while the influence of the extraction flow rate is not significant. A first-order kinetic reaction model can be used to predict the half-life of contaminant concentrations. Combined with the desirability function, the optimal conditions for the removal of ethylbenzene from soil were: time 180 min, temperature 20 °C, extraction flow 6000 mL/min, and contaminant concentration 2%. The developed numerical model, 3D-SVE, nicely simulates laboratory findings. These results can provide ideas to improve the efficiency of SVE.