Understanding slope stability is crucial for effective risk management and prevention of slides. Some deterministic approaches based on limit-equilibrium and numerical methods have been proposed for the assessment of the safety factor (SF) for a given soil slope. However, for risk analyses of slides of earth dams, a range of SFs is required due to uncertainties associated with soil strength properties as well as slope geometry. Recently, several studies have demonstrated the efficiency of artificial neural network (ANN) models in predicting the SF of natural and artificial slopes. Nevertheless, such techniques operate as black-box models, prioritizing predictive accuracy without suitable interpretability. Alternatively, multivariate polynomial regression (MVR) models offer a pragmatic interpretability strategy by combining the analysis of variance with a response surface methodology. This approach overcomes the difficulties associated with the interpretability of the black-box models, but results in limited accuracy when the relationship between independent and dependent variables is highly nonlinear. In this study, two models for a quick assessment of slope SF in earth dams are proposed considering the MVR and the ANN models. Initially, a synthetic dataset was generated considering different soil properties and slope geometries. Then, both models were evaluated and compared using unseen data. The results are also discussed from a geotechnical point of view, showing the impact of each input parameter on the assessment of the SF. Finally, the accuracy of both models was measured and compared using a real-case database. The obtained accuracy was 78% for the ANN model and 72% for the MVR one, demonstrating a great performance for both proposed models. The efficacy of the ANN model was also observed through its capacity to reduce false negatives (a stable prediction when it is not), resulting in a model more favorable to safety assessment.

The number of studies concerning the shear strength of resedimented alluvial soils is extremely limited compared to the studies conducted on fine-grained marine sediments, since alluvial soils are generally tested in remolded or reconstituted state especially in the studies investigating their liquefaction potential. In this study, estimation models were developed to predict cohesion (c) and internal friction angle (phi) parameters of a fine-grained alluvial soil using resedimented samples. A total of 60 undisturbed soil samples were obtained from Bafra district of Samsun province (Turkiye) by core drilling. A cone penetration test with pore water pressure measurement (CPTu) was also carried out alongside each borehole to determine the over-consolidation ratios of the samples. Physical-index property determinations and triaxial tests were conducted on the undisturbed samples. 20 sample sets were created with known physical, index, and strength characteristics. The samples are classified as CH, CL, MH, and ML according to the Unified Soil Classification System, with liquid and plastic limits ranging from 31.6-75% and 19.3 to 33.6% respectively. The c and phi values of the samples varied from 4.1 to 46.1 kPa and 26 to 35 degrees respectively. The samples were then resedimented in the laboratory under conditions reflecting their original in-situ properties, and triaxial tests were repeated. The c and phi values of the resedimented samples ranged from 5.3 to 24.5 kPa and 28 to 32 degrees respectively. The results indicate that the c values of the resedimented samples are generally lower than those of the undisturbed samples, whereas upper and lower bounds for phi values are similar. Multivariate regression analyses (MVR) were utilized to develop estimation models for predicting c and phi using strength and physical properties of 20 soil samples as independent variables. Three estimation models with R-2 values varying between 0.723 and 0.797 were proposed for c and phi which are statistically significant for p <= 0.05. Using artificial neural networks (ANN), the estimation models developed by MVR were replicated to validate the models. ANN yielded very similar results to the MVR, where the R-2 values for the correlations between c and phi values predicted by both methods varied from 0.852 to 0.955. The results indicate that c and phi values of undisturbed samples can be estimated with acceptable accuracy by determining basic physical and index properties of the disturbed samples and shear strength parameters of the resedimented samples. This approach, which enables the reuse of disturbed soil samples, can be used when undisturbed soil samples cannot be obtained from the field due to economic, logistical, or other reasons. Further research on the shear strength parameters of resedimented alluvial soils is needed to validate the estimation models developed in this study and enhance their applicability to a wider range of alluvial soils.

Background and AimsRoots of plants have been shown to be effective in reinforcing soils against slope failures. Two key mechanical properties in such reinforcement are the root's tensile strength (TS) and elastic modulus (EM). However, knowledge on the combined effects of root moisture content (RMC) and root diameter on these properties is scarce. The study aims to quantify these relationships for root samples of four native Australian tree (A. costata, B. integrifolia, E. reticulatus, and E. racemosa).MethodsA series of tensile tests were conducted and the root diameter at the fracture point and RMC were measured immediately after each test. Data were analysed using both univariate and multivariate analyses.ResultsBoth TS and EM declined with increasing diameter. Power-law expressions were found to describe the relationship between TS and diameter moderately well, but less so the one between TS and RMC. Multivariate analyses yielded a double power-law for TS versus diameter and RMC with a stronger fit than univariate ones. A weaker power-law was found between EM and these 2 variables. Of the four trees tested, A. costata exhibited the highest tensile strength and elastic modulus at a 1 mm diameter, while B. integrifolia yielded the lowest.ConclusionConsidering both diameter and RMC as explanatory variables of TS and EM yield better accounts of experimental data. This work contributes to a better understanding of reinforcement capacity of trees generally, as well as the specific performance of roots of four native Australian trees.