Unpaved roads are essential for transportation infrastructure, particularly for forest industry. Traditionally, unpaved roads are composed of layers using local soils. Poor local soils need to be replaced with gravel, crushed aggregate, or amixture of materials. Due to traffic and weather conditions, unpaved roads require frequent maintenance and repair. To reduce the amount of quality materials and the frequency of maintenance operations, reinforcements can be used (synthetic or natural). This paper focussed on the behaviour of a fine soil reinforced with natural fibres from the forest value chain (pine needles), to assess their use on unpaved forest roads. Cyclic CBR tests were carried out to assess the resilient response of the soil (unreinforced and reinforced); the tests included initial monotonic loading, followed by cyclic loading. The force-penetration response and CBR value improved with the inclusion of pine needles; the best response corresponded to a percentage of incorporation of 1% (mass). For the cyclic loading phase, the permanent displacement decreased with the number of cycles, approaching a resilient response. The reinforcement with pine needles led to an improved elastic response, represented by an equivalent stiffness modulus. The best behaviour was, again, obtained for a percentage of incorporation of 1% (mass). The addition of fibres led to reduced displacements during the test, relatively to the unreinforced soil. The results showed that for unpaved forest roads, where the investment in soil characterisation is often very limited, cyclic CBR tests can be a promising approach in obtaining design parameters.

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.