In deep coal mine strata, characterized by high ground stress and extensive fracturing, predicting the strength of fractured rock masses is crucial for stability analysis of the surrounding rock in coal mine strata. In this study, rock samples were obtained from construction sites in deep coal mine strata and intact, as well as fissured, rock specimens were prepared and subjected to triaxial compression tests. A numerical model based on the discrete element method was then established and the micro-parameters were calibrated. A total of 288 triaxial compression tests on the rock specimens under different conditions of confining pressure, loading rate, fissure dip angle, and fissure length, were conducted to obtain the triaxial compressive strength of the fractured rock specimens under different conditions. To address the limitations of traditional back propagation (BP) neural networks in solving stochastic problems, a modified BP neural network model was developed using a random factor and an interlayer mean square error corrected network model evaluation function. The traditional and modified BP neural network models were then employed to predict the triaxial compressive strength of the fractured rock specimens. Through comparative analysis, it was found that the modified BP neural network prediction model exhibited smaller errors and significantly reduced overfitting, making it an effective tool for predicting the strength of fractured rocks in deep coal mine strata.

The rainfall infiltration model plays a key role in the fields of seepage theory and geological hazard evaluation. To reflect the change in the moisture content in soil during rainfall infiltration, the infiltration process was segmented into stages, and an improved unsaturated wetting front extension model was proposed by introducing unsaturated soil parameters on the basis of the traditional infiltration model. The variation rules of surface moisture content and wetting front depth were revealed under the condition of uniform or non-uniform moisture content distribution in soil. The results indicate that the surface moisture content and wetting front depth increase nonlinearly with time during the infiltration process, and the proposed model is in good agreement with the numerical simulation results, the maximum error is less than 5%, while the growth rate under two hypotheses shows obvious differences for different rainfall intensities. Moreover, due to the variation in the moisture content with time, the proposed model calculates a lower moisture content than that of the traditional model under the same conditions, but the expansion depth of the wetting front is higher and the range of influence on soil is greater.