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.

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.

Disasters have different shapes, and one of them is sudden landslides, which can put the safety of highway users at risk and result in crucial economic damage. Along with the risk of human losses, each day a highway malfunctions causes high expenses to citizens, and repairing a failed highway is a time- and cost-consuming process. Therefore, correct highway functioning can be categorized as a high-priority reliability factor for cities. By detecting the failure factors of highway embankment slopes, monitoring them in real-time, and predicting them, managers can make preventive, preservative, and corrective operations that would lead to continuing the function of intracity and intercity highways. Expansive clay soil causes many infrastructure problems throughout the United States, and much of Mississippi’s highway embankments and fill slopes are constructed of this clay soil, also known as High-Volume Change Clay Soil (HVCCS). Landslides on highway embankments are caused by recurrent volume changes due to seasonal moisture variations (wet-dry cycles), and the moisture content of the HVCCS impacts soil shear strength in a vadose zone. Soil Matric Suction (SMS) is another indication of soil shear strength, an essential element to consider. Machine learning develops high-accuracy models for predicting the SMS. The current work aims to develop hybrid intelligent models for predicting the SMS of HVCCS (known as Yazoo clay) based on field instrumentation data. To achieve this goal, six Highway Slopes (HWS) in Jackson Metroplex, Mississippi, were extensively instrumented to track changes over time, and the field data was analyzed and generated to be used in the proposed models. The Artificial Neural Network (ANN) with a Bayesian Regularization Backpropagation (BR-BP) training algorithm was used, and two intelligent systems, Particle Swarm Optimization (PSO) and Butterfly Optimization Algorithm (BOA) were developed to optimize the ANN-BR algorithm for predicting the HWS’ SMS by utilizing 13,690 data points for each variable. Several performance indices, such as coefficient of determination (R²), Mean Square Error (MSE), Variance Account For (VAF), and Regression Error Characteristic (REC), were also computed to analyze the models’ accuracy in prediction outcomes. Based on the analysis results, the PSO-ANN outperformed the BOA-ANN, and both had far better performance than ANN-BR. Moreover, the rainfall had the highest impact on SMS among all other variables and it should be carefully monitored for landslide prediction HWS. The proposed hybrid models can be used for SMS prediction for similar slopes.