Expansive Yazoo clay soil is susceptible to volumetric deformation and is dominant in central Mississippi and other neighboring southern states of the United States. Recurring shrink-swell behavior causes a significant problem to infrastructures in the area. Although Yazoo clay causes a significant problem in the deep southern states, limited study has been conducted on the behavior of Yazoo clay, especially in the presence of rainfall. The objective of this current study is to investigate the coupled effect of changes in void ratio due to wet-dry cycles and rainfall on the stability of highway slopes made of Yazoo clay. The finite element method in Plaxis 2D by Bentley System (https://www.plaxis.com/) has been utilized to investigate the coupled effect of changes in mechanical properties and rainfall using flow-deformation and stability analysis. Reconstituted expansive clay soil samples were used for the laboratory experiment. The reconstituted Yazoo clay samples were subjected to 3, 5, and 7 wetting and drying cycles in an enclosed chamber for a 24-h period. The axial deformation of the samples and the change in void ratios at each number of the cycle was closely monitored. The strength change at each wet and dry cycle was also investigated and used for slope stability analysis in the presence of rainfall. The test results indicate that the void ratio increases with the increasing number of wet-dry cycles. A continuous increment in void ratios from 0.99 in an undisturbed state with no wet-dry cycle to 1.49 at the 7th wet-dry cycle, indicating a 48.9% increase, as the wetting and drying cycle increases was recorded; in turn, decreasing the cohesion of the soil by 77%. The factor of safety considering the effect of two total rainfall periods of Rv = 126.2 mm (2 h) and Rv = 271.7 mm (3 days) reduced from 1.7 to 1.2 and 1.68 to 1.02, considering the effect of the 7th wet-dry cycle at the topsoil. The changes in the void ratio due to the wetting and drying cycle of Yazoo clay soil reduces the shear strength to a fully softened condition, increasing the possibility of slope failure. This condition further worsens in the presence of a perched water condition due to the infiltration of rain water.
With flooding and other weather events intensifying, more cost-effective erosion and flood control systems are needed. Vetiver (Chrysopogon zizanioides (L.) Roberty), is part of an arsenal of sustainable, low cost, and green infrastructure tools to reduce the risks of erosion, landslides, and flooding. This study investigates vetiver and its broader application to transportation planning. Based on a literature review and interviews with experts, vetiver as a green infrastructure tool is summarized. An evaluation framework was devised in which the plant’s effectiveness to stabilize hillsides and manage stormwater is investigated. This framework is applied to a recent highway flooding case where vetiver could have been used. While site-specific conditions and roadway requirements are critical to its effectiveness as a mitigation tool, additional pathways to understanding, acceptance, and use of vetiver to support transportation resilience requires convergence in engineering, design, and planning disciplines. Understanding barriers to the adoption of vetiver will also support efforts to increase other green infrastructure tools in transportation planning. Improvements in policies, standards, guidance and training and education on vetiver and green infrastructure will support the mitigation of transportation disruptions and community resilience.
Recycled plastic pin (RPP) has been proved to be an effective and inexpensive solution for shallow slope stabilization. Current practice suggests conducting numerical modeling to find out the desired factor of safety (FS) using RPP in the design of landslide repair. While the slope stability is heavily dependent on soil strength parameters and slope geometry, RPP length and spacing can also play a significant role in reaching the target factor of safety for the highway slope. During this study, a safety prediction model was developed using both statistical and machine learning (ML) approaches to use RPP in slope stabilization. Initially, parametric study was conducted using five different soil strengths, six slope heights, three slope ratios, three RPP lengths, and five RPP spacing configurations. Using the strength reduction techniques of Finite Element Modeling Software PLAXIS 2D, FS was determined for more than 1,000 combinations. Afterwards, a statistical approach was undertaken to determine a safety prediction model containing all possible parameters. Finally, an ML approach was conducted for safety model. The ML approach was found to be more accurate than the classical statistical approach with 85% accuracy of predicting the FS for an RPP reinforced highway slope. The developed model was validated against the values obtained from the numerical modeling, which indicated that the SF obtained from the developed model was in good agreement with those from finite element method (FEM) analysis.
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Near-surface soil moisture content variation is a major factor in the frequent shallow slope failures observed on Mississippi’s highway slopes built on expansive clay. Soil moisture content variation is monitored generally through borehole sensors in highway embankments and slopes. This point monitoring method lacks spatial resolution, and the sensors are susceptible to premature failure due to wear and tear. In contrast, Unmanned/Uncrewed Aerial Vehicles (UAVs) have higher spatial and temporal resolutions that enable more efficient monitoring of site conditions, including soil moisture variation. The current study focused on developing two methods to predict soil moisture content (θ) using UAV-captured optical and thermal combined with machine learning and statistical modeling. The first method used Red, Green, and Blue (RGB) color values from UAV-captured optical images to predict θ. Support Vector Machine for Regression (SVR), Extreme Gradient Boosting (XGB), and Multiple Linear Regression (MLR) models were trained and evaluated for predicting θ from RGB values. The XGB model and MLR model outperformed the SVR model in predicting soil moisture content from RGB values. The R2 values for the XGB and MLR models were >0.9 for predicting soil moisture when compared to SVR (R2 = 0.25). The Root Mean Square Error (RMSE) for XGB, SVR, and MLR were 0.009, 0.025, and 0.01, respectively, for the test dataset, affirming that XGB was the best-performing model among the three models evaluated, followed by MLR and SVR. The better-performing XGB and MLR models were further validated by predicting soil moisture using unseen input data, and they provided good prediction results. The second method used Diurnal Land Surface Temperature variation (ΔLST) from UAV-captured Thermal Infrared (TIR) images to predict θ. TIR images of vegetation-covered areas and bare ground areas of the highway embankment side slopes were processed to extract ΔLST amplitudes. The underlying relationship between soil surface thermal inertia and moisture content variation was utilized to develop a predictive model. The resulting single-parameter power curve fit model accurately predicted soil moisture from ΔLST, especially in vegetation-covered areas. The power curve fit model was further validated on previously unseen TIR, and it predicted θ with an accuracy of RMSE = 0.0273, indicating good prediction performance. The study was conducted on a field scale and not in a controlled environment, which aids in the generalizability of the developed predictive models.
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