A Determination Method of Rainfall Type Based on Rainfall-Induced Slope Instability

This work presents a determination method of rainfall types based on rainfall-induced slope instability to eliminate the current dilemma of the inconsistent classification of rainfall types. Firstly, 5,808 scenarios of slope instability are simulated with 11 kinds of soil properties under 528 designed intensity-duration (I−D) conditions. Then through analysis of the I−D conditions when slope failure occurred, rainfall is classified into two types: short-duration − high-intensity (SH) type, and long-duration − low-intensity (LL) type. According to the analysis results, it indicates that rainfall types affect the initiation of slope failure, i.e., different I−D conditions will affect the slope failure initiation under LL type rainfall, while the slope failure initiation will not be affected by the change of I−D conditions under SH type rainfall. In addition, the results show that the classification of rainfall types does not depend on the soil shear strength parameters (cohesion and internal friction angle), although the change of soil shear strength parameters will cause the shift of threshold curve of slope failure in the I−D conditions two-dimensional (2D) plane. The findings in this study benefit to understanding the effect of rainfall type on the mechanism of slope failure initiation, which will promote the development of an early warning system of slope failure in the future by considering the identification of rainfall types.

Modeling of soil shear strength using multiple linear regression (MLR) at Penang, Malaysia

This paper presents multiple linear regression (MLR) soil shear strength models developed from electrical resistivity and seismic refraction tomography data. The MLR technique is used to estimate the value of dependent variables of soil shear strength based on the value of two independent variables, namely, resistivity and velocity. These parameters were regressed using regression statistics technique for generating MLR model. The results of MLR model, which is based on the estimation of model dependent parameters (Log10 resistivity and Log10 velocity), calculated for p-value, are less than 0.05 and VIF value less than 10 for cohesion and friction angle models. This result shows that there is a statistically significant relationship between cohesion and friction angle with geophysical parameters (independent variables). The estimation accuracy of the MLR models is also conducted for verification, and the result shows that RMSE value for predicted cohesion and predicted friction angle is 0.77 kN/m2 and 1.73° which is close to zero. Meanwhile, MAPE value was found to be 4.57 % and 7.61 %, indicating highly accurate estimation for the MLR models of predicted cohesion and predicted friction angle. Based on the application of near surface, the study area was successfully classified into two regions, namely, medium and hard clayey sand. Thus, it is concluded that MLR method is suitable in estimating the subsurface characterization that covered more regions compared to the traditional method (laboratory test).

Improvement of Organic Soil Shear Strength through Calcite Precipitation Method Using Soybeans as Bio-Catalyst

Organic soil has a high content of water and compressibility. Besides that, it has a low specific gravity, density, and shear strength. This study evaluates the applicability of the soybean crude urease for calcite precipitation (SCU-CP) method and its effectiveness in organic soil as a soil-amelioration technique. Various soybean concentrations were mixed with a reagent composed of urea and calcium chloride to produce the treatment solution. Its effect on the hydrolysis rate, pH, and amount of precipitated calcite was evaluated through test-tube experiments. SEM-EDS tests were performed to observe the mineralogy and morphology of the untreated and treated samples. The treatment solution composed of the reagent and various concentrations of soybeans was applied to organic soil. The increasing strength of the organic soil was evaluated using direct shear (DS) and unconfined compression (UCS) tests. The test-tube results show that a hydrolysis rate of 1600 u/g was obtained when using 50 g/L of soybeans with a precipitation ratio of 100%. The mechanical tests show a significant enhancement in the parameters of the organic soil’s shear strength. A shear strength improvement of 50% was achieved in this study. A UCS of 148 kPa and cohesion of 50 kPa was obtained in the treated samples of organic soil. This research elucidates that the SCU-CP is an effective technique for improving organic soil’s shear strength.

The Effects of Seismic Coefficient Uncertainty on Pseudo-Static Slope Stability: A Probabilistic Sensitivity Analysis

The method of pseudo-static analysis has been widely used to perform seismic slope stability, in which a seismic coefficient is used to represent the earthquake shaking effect. However, it is important but difficult to select the magnitude of seismic coefficients, which are inevitably subjected to different levels of uncertainties. This paper aimed to study the influences of seismic coefficient uncertainties on pseudo-static slope stability from the perspective of probabilistic sensitivity analysis. The deterministic critical slope height was estimated by the method of upper-bound limit analysis with the method of pseudo-static analysis. The soil shear strength parameters, the slope geometrical parameters (including slope inclinations, slope heights, and the slope widths), the horizontal seismic acceleration coefficient, and the unit weight of soil masses were considered as random variables. The influences of their uncertainty degrees, the correlation relations, and the distribution types of random variables on probabilistic density functions, failure probabilities, and sensitivity analysis were discussed. It was shown that the uncertainty degrees greatly impact the probability density distributions of critical slope heights, the computed failure probabilities, and Sobol’ index, and the horizontal seismic coefficient was the second most important variable compared to the soil shear strength parameters.

Finite Element Modeling of a Mechanically Stabilized Earth Trial Wall

Polymer geogrid reinforced soil retaining walls have become commonplace, with routine design generally carried out by limiting equilibrium methods. Finite element analysis (FEA) is becoming more widely used to assess the likely deformation behavior of these structures, although in many cases such analyses over-predict deformation compared with monitored structures. Back-analysis of unit tests and instrumented walls improves the techniques and models used in FEA to represent the soil fill, reinforcement and composite behavior caused by the stabilization effect of the geogrid apertures on the soil particles. This composite behavior is most representatively modeled as enhanced soil shear strength. The back-analysis of two test cases provides valuable insight into the benefits of this approach. In the first case, a unit cell was set up such that one side could yield thereby reaching the active earth pressure state. Using FEA a test without geogrid was modeled to help establish appropriate soil parameters. These parameters were then used to back-analyze a test with geogrid present. Simply using the tensile properties of the geogrid over-predicted the yield pressure but using an enhanced soil shear strength gave a satisfactory comparison with the measured result. In the second case a trial retaining wall was back-analyzed to investigate both deformation and failure, the failure induced by cutting the geogrid after construction using heated wires. The closest fit to the actual deformation and failure behavior was provided by using enhanced fill shear strength.

Estimation of Soil Shear Strength Indicators Using Soil Physical Properties of Paddy Soils in the Plastic State

Saturated soil shear strength is a primary factor that reflects the driving resistance of agricultural machinery in paddy soils. The determination of soil shear strength indicators, such as cohesion and internal frictional angle, is crucial to improve the walking efficiency of agricultural machinery in paddy soils. However, the measurement of these indicators is often costly and time-consuming. Soil moisture content, density, and clay content are crucial factors that affect the cohesion and internal friction angle, while very limited studies have been performed to assess the interactive effects of the three factors on soil shear characteristics, especially on paddy soils. In this study, eight soil samples were taken from eight paddy fields in Southeastern China, and the central composition rotatable design was used to classify the soil samples into five levels based on different clay content (X1), moisture content (X2), and density (X3). The direct shear tests were carried out indoors on the remolded paddy soil using a self-made shear characteristic measuring device. Then, both individual and interactive effects of X1, X2, and X3 on soil cohesion and internal friction angles on paddy soils were systematically investigated and analyzed using the regression analysis method in the data processing software Design-Expert. Our results indicated that the effects of the three environmental factors on soil cohesion were in the order of X1 > X2 > X3, while the order was X2 > X3 > X1 for the impact on internal friction angle. The interactive effects were in the order of X1X2 > X1X3 > X2X3 for cohesion and X1X2 > X2X3 > X1X3 for internal friction angle. Two prediction models were successfully established to quantify the soil cohesion and internal friction angle as affected by soil physical properties, and the coefficient of determination (R2) was 0.91 and 0.89 for the two equations, respectively. The model validations using new soil samples suggested that the models were capable of predicting the shear characteristic parameters under different physical parameters effectively, with errors between predicted and measured soil shear strength indicators within 15% and relative root mean square error less than 11%.