The Xiashu loess exhibits expansion when in contact with water and contraction when water is lost, making it highly susceptible to the influence of rainfall. Therefore, it is essential to investigate the infiltration behavior of rainwater in Xiashu loess slopes under various conditions. The depth of infiltration in slopes directly affects the depth of landslide failure and serves as an important indicator for studying slope infiltration characteristics; only a handful of academics have delved into its study. This article is based on on-site rainfall experiments on Xiashu loess slopes, using three main factors, rainfall intensity, rainfall duration, and slope angle, as discrimination indicators for the infiltration depth of Xiashu loess slopes. The particle swarm optimization algorithm is employed to optimize the BP neural network and establish a PSO-BP neural network prediction model. The experimental data are accurately predicted and compared with the multivariate nonlinear regression model and traditional BP neural network models. The results demonstrate that the PSO-BP neural network model exhibits a better fit and higher prediction accuracy than the other two models. This model provides a novel approach for rapidly determining the infiltration depth of Xiashu loess slopes under different rainfall conditions. The results of this study lay the foundation for the prediction of the landslide damage depth and infiltration of Xiashu loess slopes.

The Xiashu loess in the middle and lower reaches of the Yangtze River with typical aeolian characteristics is widely distributed in the hilly areas and is the main kind of landslides in this area. The paper reveals the infiltration process of the Xiashu loess slope under different rainfall intensity conditions on the basis of on-site artificial rainfall simulation tests and laboratory experiments. The distribution of pore water pressure inside the Xiashu loess slope under different rainfall intensity conditions were compared and analyzed. The relationship between rainfall intensity and water content at different depths was clarified, and the relation function among rainfall intensity conditions, slopes, and the ultimate depth and critical rainfall intensity of the Xiashu soil slope landslide is proposed. The research results indicate that: (1) the infiltration rate and depth of the soil at the foot of the slope are greater than those at the top and middle of the slope; (2) an increase in the rainfall duration is found to cause an increase in slope infiltration depth; (3) an increase in the rainfall duration can lead to a more significant influence of the infiltration depth under the slope angle; (4) the infiltration depth of rainfall with low intensities and long durations is larger than that of high intensities and short durations. Finally, the ultimate rainfall infiltration depth under different slope angles and rainfall conditions was determined. The research results can be used to forecast the Xiashu loses soil landslide scale, providing theoretical basis for early warning of instability of Xiashu loess slope under different unfavorable conditions.