To facilitate rapid and precise predictions of pile bearing capacity, a Back Propagation (BP) neural network model has been developed utilizing data sourced from existing literature. The model incorporates several input parameters, including pile length, pile diameter, average effective vertical stress, and undrained shear strength. To enhance the optimization of the BP neural network’s hyperparameters, five distinct optimization algorithms were employed: the Sine Cosine Optimization Algorithm (SCA), Snake Optimization Algorithm (SO), Pelican Optimization Algorithm (POA), African Vulture Optimization Algorithm (AVOA), and Chameleon Optimization Algorithm (CSA). The efficacy of the proposed model was validated using a randomly selected, previously unused subset of data and assessed through various evaluation metrics. Furthermore, the prediction outcomes were analyzed in conjunction with the SHAP interpretability method to address the inherent “black box” nature of the model. This analysis allowed for a visualization of the SHAP values associated with the input parameters, thereby elucidating their significance and impact on the predictions of pile capacity. The results indicated that the R² values for the BP-SCA, BP-SO, BP-POA, BP-AVOA, and BP-CSA models were 0.9859, 0.9909, 0.9954, 0.9964, and 0.9954, respectively, with the BP-AVOA model demonstrating the highest accuracy, stability, and predictive performance. The SHAP analysis further revealed that undrained shear strength and average effective vertical stress are the most influential parameters affecting pile bearing capacity, followed by pile length and pile diameter. Overall, the model effectively captures the complex nonlinear relationships among the characteristic parameters, thereby providing a robust foundation for further investigations into pile bearing capacity.

Accurate identification of natural and mine earthquakes in mining areas is of great significance to the construction of secondary disaster warning networks. Based on 490 records of natural and mine earthquakes in the Hegang area from 2006 to 2017, this paper compares and analyzes the ground motion characteristics of the research samples (150 earthquake records and 200 mine earthquake records) and selects the key identification parameters of dominant frequency, Pm/Tc, and Sm/Tc. The correct identification rate of the test samples (60 seismic records and 80 mine earthquake records) is 95.7%, 91.4%, and 93.6%, respectively, and the actual threat rate is 90.8%, 83.3%, and 86.3%, respectively. Finally, based on the selected key identification parameters, a “three-parameter comprehensive gradient discriminant method” is proposed. The correct identification rate and actual threat rate are 99.3% and 98.4%, respectively, which can basically accurately identify natural and mine earthquakes. It provides a certain method and theoretical support for the mining area vibration identification method, safety production, and disaster warning.

Heilongjiang Province, the largest commercial grain base in China, experiences significant challenges due to the environmental effects on its soil. The freezing and thawing cycle in this region leads to the transport of water and heat, as well as the exchange and transfer of energy. Consequently, this exacerbates the flooding disaster in spring and severely hampers farming activities such as plowing and sowing. To gain a better understanding of the freezing and thawing mechanisms of farmland soil in cold regions and prevent spring flooding disasters, this study focuses on Heilongjiang Province as a representative area in northeast China. The research specifically investigates the frozen and thawed soil of farmland, using a large-scale low-temperature laboratory to simulate both artificial and natural climate conditions in the cold zone. By employing the similarity principle of geotechnical model testing, the study aims to efficiently simulate the engineering prototypes and replicate the process of large-span and long-time low temperatures. The investigation primarily focuses on the evolution laws of key parameters, such as the temperature field and moisture field of farmland soil during the freeze–thaw cycle. The findings demonstrate that the cooling process of soil can be categorized into three stages: rapid cooling, slow cooling, and freezing stabilization. As the soil depth increases, the variability of the soil temperature gradually diminishes. During the melting stage, the soil’s water content exhibits a gradual increase as the temperature rises. The range of water content variation during thawing at depths of 30 cm, 40 cm, 50 cm, and 80 cm is 0.12% to 0.52%, 0.47% to 1.08%, 0.46% to 1.96%, and 0.8% to 3.23%, respectively. To analyze the hydrothermal coupling process of farmland soil during the freeze–thaw cycle, a theoretical model of hydrothermal coupling was developed based on principles of mass conservation, energy conservation, Darcy’s law of unsaturated soil water flow, and heat conduction theory. Mathematical transformations were applied after defining the relative degree of saturation and solid–liquid ratio as field functions with respect to the relative degree of saturation and temperature. The simulated temperature and moisture fields align well with the measured data, indicating that the water–heat coupling model established in this study holds significant theoretical and practical value for accurately predicting soil temperature and moisture content during the spring sowing period, as well as for efficiently and effectively utilizing frozen soil resources in cold regions.