Biomineralization technology is a promising method for soil cementation, enhancing its mechanical properties. However, its application in mitigating slope surface erosion caused by rainfall has not been fully explored. This study experimentally examined the feasibility of using plant-based enzyme-induced carbonate precipitation (PEICP) to reduce slope surface rainfall erosion through simulated rainfall tests. The effects of biotreatment cycles (N) and rainfall intensity (Ri) on erosion resistance were evaluated. The results demonstrated that increasing the biotreatment cycles improved the bio-cementation level, as evidenced by enhanced surface strength, increased calcium carbonate content (CCC) and thicker crust layers. Specifically, as the biotreatment cycles (N) increased from 2 to 6, the crust layer thickness expanded from 5.2 mm to 15.7 mm, with surface strength rising from 38.3 kPa to 244.3 kPa. Likewise, the CCC increased significantly from 1.09% to 5.32%, further reinforcing the soil structure and enhancing erosion resistance. Slopes treated with six biotreatment cycles exhibited optimal erosion resistance across rainfall intensities ranging from 45 to 100 mm/h. Compared to untreated slopes, biotreated slopes showed significant reductions in soil loss, with a decrease to below 10% at N = 4 and near-complete erosion resistance at N = 6. These findings highlight the potential of PEICP technology for improving slope stability under rainfall conditions.

In order to study the force characteristics and reinforcement mechanisms of the bank protection capacity of micropile groups under rain seepage, two different scale models were employed using model tests and the finite element method. Focusing on the stress within the micro-piles, the lateral soil pressure against the piles, the displacement at the pile tops, and the overall stability of the embankment reinforced by the micro-piles, engineers can assess the performance and durability of the structure during rainwater scouring. The study shows that rainfall leads to increased soil saturation, which in turn reduces the soil's shear strength. When subjected to loading after rain, micropiles within the same row exhibit similar strains. The thrust from potential landslides at the top of the slope causes the rear row of piles to experience greater flexural deformation. The difference between the soil pressure values of the same row after rainwater infiltration is small, and the overall soil pressure value increases in a stepwise manner with the increase of loading volume. The micropile support helps reduce soil displacement. The displacement of the middle and front row of piles is significantly lower compared with the back row by 32.3 % and 35.7 %, respectively. The pile group can limit the soil displacement within a certain range, which is beneficial for improving the stability of bank slopes under rainfall scouring. The micropile group enhances the overall slip resistance of the bank slope and can inhibit the development of the slip and crack surface of the bank slope to a certain extent. In engineering design, it is crucial to determine an appropriate pile spacing. A too small spacing can prevent the piles from achieving their optimal bending strength, whereas too large a spacing may lead to the risk of the bank slope as a whole experiencing overturning damage.

Rainfall erosion can cause environmental and economic damage by decreasing the storage capacity of water reservoirs because of the detachment of soil particles. The purpose of this study was to develop a one-dimensional physicomathematical model that can help predict the effects of rainfall erosion on the banks of water reservoirs. The model was developed using the Mein-Larson model to describe water infiltration, the kinematic wave approximation to represent overland flow generation, and the steady state sediment continuity equation to estimate sediment transport. The model was validated using rainfall simulator tests and lateritic soil samples with a bimodal soil-water retention curve. The results showed conformity with the experimental data, identifying a threshold in the models for discharge per unit area and sediment yield rate, as well as a linear increase in the models for total runoff and sediment load per unit area. However, the model failed to capture the peak in sediment yield rate owing to raindrop impact during the initial minutes of rainfall. Parametric analysis highlighted the impact of increasing the calibration constant of splash erosion, erodibility coefficient, and critical shear stress on the slope of the sediment load per unit area model. Despite its limitations, the model demonstrates satisfactory predictive capability for sediment load per unit area under high-intensity rainfalls, achieving an R2 greater than 0.92 in five of the six cases examined.

Erosion is the main cause of damage to unpaved roads. This study utilized rainfall simulators to quantify erosion on unpaved roads, controlling variables such as rainfall intensity and slope. A laboratory model of an unpaved road was utilized to evaluate soil loss in an experimental setup. A total of 72 tests were conducted to compare simulated conditions on unpaved roads for three soil types with three slope variations, and eight rainfall intensities. The impact of each variable (soil type, slope, and rainfall intensity) on soil loss was analyzed for 30-minute rainfall events. Analysis of variance (ANOVA) was employed to assess soil erosion response to terrain slope for the three soil types, revealing statistical differences in soil loss between low slopes (2%) and steep slopes (7%) with p-values of .04 (sandy soil), .00007 (sandy silt soil), and .00008 (loam silt soil). Correlation analysis demonstrated a strong relationship between rainfall intensity and soil loss (R2 = .76) for sandy soil and sandy silt soil. Analysis of covariance (ANCOVA) indicated a linear relationship between soil loss and rainfall intensity, with significant differences (p < .05). The findings suggest that soil loss on unpaved roads is positively correlated with slope and rainfall intensity. However, this relationship is not always linear; sandy soil exhibited a nonlinear relationship, especially with high rainfall intensities, whereas sandy silt soil showed a linear relationship with evaluated rain intensities. The type of soil influences erosion process, with higher erosion rates observed in sandy silt soils compared to loam silt soils. This paper analyzed the factors essential for addressing erosion on unpaved roads, identifying key elements to minimize soil loss.