Search Results (3)

Soil erodibility (K) refers to the inherent ability of soil to withstand erosion. Accurate estimation and spatial prediction of K values are vital for assessing soil erosion and managing land resources. However, as most K-value estimation models are empirical, they suffer from significant extrapolation uncertainty, and traditional studies on spatial prediction focusing on individual empirical K values have neglected to explore the spatial pattern differences between various empirical models. This work proposed a universal framework for selecting an optimal soil-erodibility map using empirical models enhanced by machine learning. Specifically, three empirical models, namely, the erosion-productivity impact calculator model (K_EPIC), the Shirazi model (K_Shirazi), and the Torri model (K_Torri) were used to estimate K values. Random Forest (RF) and Gradient-Boosting Decision Tree (GBDT) algorithms were employed to develop prediction models, which led to the creation of three K-value maps. The spatial distribution of K values and associated environmental covariates were also investigated across varying empirical models. Results showed that RF achieved the highest accuracy, with R² of K_EPIC, K_Shirazi, and K_Torri increasing by 46%, 34%, and 22%, respectively, compared to GBDT. And distinctions among environmental variables that shape the spatial patterns of empirical models have been identified. The K_EPIC and K_Shirazi are influenced by soil porosity and soil moisture. The K_Torri is more sensitive to soil moisture conditions and terrain location. More importantly, our study has highlighted disparities in the spatial patterns across the three K-value maps. Considering the data distribution, spatial distribution, and measured K values, the K_Torri model outperformed others in estimating soil erodibility in the plateau lake watershed. This study proposed a framework that aimed to create optimal soil-erodibility maps and offered a scientific and accurate K-value estimation method for the assessment of soil erosion. Full article

Soil erodibility research is of theoretical and practical significance to the prediction and prevention of regional soil erosion. At present, the study on soil erodibility in the lateritic red soil area of eastern Guangdong province is relatively lacking. Taking the forest land soil of the Yinna mountainous area as the research object, the physical and chemical properties (organic matter mass fraction, texture, moisture, bulk density, pH, aggregate content) of soil samples at different altitudes were measured with field survey sampling and indoor analysis. Soil erodibility K values were simulated with different models (the EPIC model, the Torri model, and the Shirazi model) and the regional applicability of the K simulation models was discussed. The influence of soil properties on soil erodibility was analyzed. The results showed that: (1) K values in the Yinna mountainous area are between 0.0250 and 0.0331 t·hm²·h/MJ·mm·hm², and the K value in the subsoil layer (20–40 cm) is higher than that of the topsoil layer (0–20 cm). These values decreased significantly with the increase of altitude. The soil in the study area belongs to low–medium to medium erodible soil types. (2) The three models have certain applicability in the Yinna mountainous area, but the simulation results still lack validation. (3) Soil particle size composition is the most important factor affecting the K value in the study area. As far as the topsoil is concerned, K values increase with the increase of clay and silt content and decrease with the increase of sand content and aggregate stability. Soil erodibility has no significant correlation with pH and bulk density and has no clear relationship with the content of soil organic carbon and soil moisture. The research results can provide basic data for regional soil and water conservation and the construction of K value databases of different soil types in China. Full article

This study investigates the nonlinear dynamic response of the masonry bridge ‘Ponte delle Torri’ in Spoleto, aiming at assessing the seismic performance of the structure and evaluating the occurring damaging mechanisms. A 3D Finite Element (FE) macromechanical procedure implemented in the FE program FEAP is adopted to model the bridge. To reproduce the typical nonlinear microcracking process evolving in masonry material when subjected to external loads, an isotropic damage model is used. This is based on a scalar damage variable introduced in the stress-strain constitutive law and equally degrading all the components of the elastic constitutive operator. A nonlocal integral definition of the damage associated variable, that is the equivalent strain measure governing its evolution, is adopted to overcome the mesh dependency problems of the FE solution typically occurring in the presence of strain softening behavior. Based on the results of a recent study by some of the authors, a single equivalent pier is analyzed, whose geometry and boundary conditions are selected so that its response can provide useful information on the out-of-plane dynamic behavior of the overall bridge. To perform the seismic assessment, a set of recorded accelerograms is properly selected to simulate the seismic history of the Spoleto site. The nonlinear dynamic response of the structure is evaluated and monitored in terms of top displacement time history, evolution of the global damage index, and distribution of the damage variable. First, a set of analyses is performed by imposing the selected ground motions one by one on the initial undamaged configuration for the structure with the aim of emphasizing the damaging effects on its dynamic response. Then, the accelerograms are arranged in sequence to reproduce the seismic history of the site and analyze the influence of accumulated damage on the dynamic amplification of the response. A critical comparison of the bridge response to the sequence of accelerograms and the single records is made, and the interaction between the damaged structure dynamic response and the signal characteristic is highlighted, as well. Full article