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Geographic information system (GIS) based landslide susceptibility mapping is a proven methodology for understanding and forecasting infrastructure impacts during significant weather events. While researchers worldwide have increasingly applied GIS and machine learning methods to study landslide susceptibility on hillside slopes affected by geomorphological and hydrological factors, there is a noticeable lack of focus on highway slope (HWS) failures in the literature. This research addresses this gap by comprehensively evaluating HWS failure susceptibility in central Mississippi counties. The study focused on developing an inventory of HWS susceptible to failure, susceptibility mapping, and model validation using probabilistic and statistical methods. Several supervised machine learning (ML) classification models, including artificial neural networks, were compared with random forest and logistic regression to solve the classification problem of HWS failure susceptibility mapping. Various data sources were utilized to develop causative factors, including Digital Terrain Models (DTM) created from Remote Sensing methods such as satellites, drone sensors, and terrestrial LiDAR. The failed slopes investigated in this study were from four counties in central Mississippi. The resolution used was 3 ft × 3 ft per pixel, representing an area of 9 ft2 per pixel. A ratio of 1:2 was maintained between failed and non-failed areas within the study area for developing the failure susceptibility prediction models. The causative factors considered in this study encompassed geotechnical and geomorphological attributes, such as slope, aspect, curvature, elevation, normalized vegetation difference index (NDVI), soil composition, and terrain from DTM. Hydrological factors were also incorporated, including precipitation, distance from the stream, groundwater depth, and Topographic Wetness Index (TWI). These causative factors were utilized as independent features to train the classification ML models for predicting vulnerable HWS. Based on the random forest model’s classification results of failed vs. non-failed assets on the unseen data set, the influence of the features was calculated. Among the top four influencing factors, ground elevation was the highest contributing factor, followed by distance from streams, NDVI, and precipitation. The results of this study can significantly contribute to transportation agencies by offering valuable insights to target preventative maintenance efforts and mitigate catastrophic failures caused by significant rainfall and weather events on road networks and highway slopes. The findings advocate for the integration of an AI/ML-based approach within asset management programs, enabling transportation agencies to rapidly detect at-risk infrastructure. This ML-based automated detection is especially beneficial when identifying vulnerable sites before a forecasted extreme event, providing value to infrastructure resiliency efforts. 

Highway slopes are susceptible to various geohazards, including landslides, rockfalls, and soil creep, necessitating early detection to minimize disruptions, prevent collisions, and ensure road safety. Conventional methods, such as visual inspections and periodic surveys, may overlook subtle changes or fail to provide timely alerts. This research aims to enhance slope movement and instability detection by leveraging advanced remote-sensing technologies such as interferometric synthetic aperture radar (InSAR), light detection and ranging (LiDAR), and uncrewed aerial vehicles (UAV). The primary objective is to develop an integrated approach combining multiple data sources to detect and predict highway slope movement effectively. InSAR offers surface deformation measurements over time, capturing nuanced slope movements, while LiDAR and UAVs provide high-resolution elevation information, including slope angles, curvature, and vegetation cover. This study explores methods to integrate these complementary data sets to validate the slope movement detection from InSAR. The research involves establishing a baseline ground motion scenario using historical open-access Sentinel-1 satellite data spanning 10 years (2018􀀐2024) for the central Mississippi region, characterized by expansive clay prone to volume changes, then comparing the ground motions with those observed from near-surface remote sensing. The baseline ground motion scenario is compared with ground truthing from near-surface remote sensing surveys conducted by LiDAR and UAV photogrammetry. The point cloud and imagery obtained from LiDAR and UAVs facilitated cross-verification and validation of the InSAR ground displacements. This study provides a comprehensive and innovative methodology for monitoring highway infrastructure using InSAR and near-surface remote sensing techniques such as LiDAR and UAV. Continuous ground motion analysis provides immediate feedback on slope performance, helping to prevent potential failures. LiDAR change detection allows for detailed evaluation of highway slopes and precise identification of potential failure locations. Integrating remote sensing techniques into geotechnical asset management programs is crucial for proactively assessing risks and enhancing highway safety and resilience. Future studies will use this data set to create finite-element-based numerical models, aiding in developing surrogate models for highway embankments based on observed InSAR ground motion patterns. This study will also serve as a foundation for future machine-learning classification models for detecting vulnerable geo-infrastructure assets. 

Soil bioengineering using Vetiver is a widely used vegetation-based slope failure mitigation technique. Though Sunshine Vetiver grass, also known as Chrysopogon zizanioides, grows 3 m in length inside the soil in tropical and subtropical climate conditions, the depth up to which Vetiver impacts the soil property has remained undetected. This study has investigated the subsurface influence zone of Vetiver grass based on nondestructive geophysical investigations Electrical Resistivity Imaging (ERI) and Multichannel Analysis of Surface Waves (MASW) in a high plasticity expansive clay soil slope in Mississippi, United States. ERI data collected on the slope revealed that the top 2 m of the high plasticity clay soil had a higher resistivity value with Vetiver (ranging from 4 to 60 􀀺m) compared to the soil without Vetiver (ranging from 2 to 28 􀀺m). MASW investigation results at the same slope have indicated a similar increase in shear wave velocity with Vetiver up to 2 m indicating enhanced soil stiffness while compared to the section without it. The combined geophysical approach using ERI and MASW reveals that the root system of the Vetiver grass enhanced the moisture content and increased the stiffness of soil within the top layers. Though the grass roots can grow more than 3 m inside the soil, the major influence was observed within the top 2 m from the slope surface. 

Climate change has been playing a crucial role in altering the precipitation patterns in the southern USA. States like Mississippi, Louisiana, and Alabama have seen increased numbers of extreme events like hurricanes, storms, and heavy rainfall. Therefore, rainfall-induced landslides have been very common in recent years. In Mississippi, due to the prevalence of highly expansive clay soil, slope failure has brought about a huge financial burden for the authority. In order to create resiliency in highway embankments, regular monitoring and early detection of landslide risks are important. The objective of the current study is to evaluate the landslide behavior of highway slopes under changed climatic conditions. One highway slope near Grenada, Mississippi, was selected for the study. The slope has a history of shallow landslide. Remote sensing technology like Light Detection and Ranging (LiDAR) has been utilized to compare the topographical surfaces in different seasons. Electrical Resistivity Imaging (ERI) was performed, and seasonal variations in subsurface moisture contents were obtained from the ERI profiles. In addition, rainwater data of the site location from available open sources were collected. Perched water zones have been detected through the ERI images when there were events of extreme rainfall. A drone mounted with an advanced LiDAR scanning system has been utilized to detect any trend of slope movement in the study site. The LiDAR scan gathered dense point cloud data to construct 3D surfaces and produce topographic maps of the slope. The integration of ERI and LiDAR provides a comprehensive understanding of the climate resilience of highway slopes in the face of climate change. 

The hydro-mechanical behavior of unsaturated soil, particularly expansive soil, is influenced significantly by cyclic wetting and drying. Understanding the soil parameters is crucial when evaluating the performance of infrastructures constructed on expansive clay. As a result of extreme rainfall events, highway slopes containing highly expansive Yazoo clay in Mississippi, U.S., become vulnerable to volume change. The phenomenon creates perched water zones within the slopes and poses a risk of slope failure. The soil-water characteristic curve (SWCC) defines the relationship between water content and soil suction, which can be obtained from different laboratory procedures. However, conventional laboratory methods have some limitations. To address this, various analytical and predictive models have been developed, but they can only offer estimates based on soil characteristics and lack seasonal variations occurring in field conditions. Studying seasonal SWCC through field measurements can help understand soil responses to changing moisture conditions. The current study utilized field data from six highway slopes in Mississippi and classified the data into different seasons: spring, summer, and fall. After obtaining van Genuchten parameters from the fitted curve for each season, the finite element method was applied to evaluate the parameters for accurate numerical analysis of infrastructures containing expansive clay. The study observed the variations in flow parameters with seasonal change that cannot be achieved when data from only one season is considered. The findings underscore the importance of field instrumentation data for developing SWCC and the significance of seasonal flow parameters in infrastructure design. 

Bioinspired slope improvements can achieve outcomes similar to traditional slope improvements for shallow slope failures, while incorporating plant material as a structural component and using a minimum of heavy equipment. Vetiver grass can mitigate the rain-induced slope instability of earthen infrastructure, such as levees, constructed using loess and clay soils. Vetiver grassroots can extend to depths greater than 3 m (10 ft), creating a new composite material with the grassroots and soil, thereby increasing shear strength to combat shallow slope failures. The objective of this study is to determine the feasibility of vetiver as a climate-resilient bioinspired slope stability improvement on a test levee constructed of loess in Vicksburg, Mississippi (MS). Vetiver was planted at 1 ft center-to-center intervals on a 9.1 m wide (30 ft) section of an approximately 12.2 m long (40 ft) downstream slope of a test levee and observed for 2.5 years. To consider the effect of extreme precipitation events, a finite element analysis was completed for a comparable clay slope using 500 year precipitation intensity–duration– frequency curves of Jackson, MS. Precipitation negatively impacts the collapsible and expansive nature of the local loess and clay, respectively. The results demonstrate that vetiver grass is a viable method to increase slope stability for earthen levees constructed with loess and clay, which are prevalent in Vicksburg and Jackson, respectively. Vetiver also holds promise as a climate resilient solution to combat raininduced shallow slope failures. 

An increase in precipitation due to climate change has given rise to the number of landslide occurrences. Vetiver, which is a perennial grass, is becoming increasingly popular all over the world as a vegetation-based soil bioengineering tool for preventing landslides. Sunshine Vetiver grass, also known as Chrysopogon zizanioides is noninvasive and does not compete with other indigenous plants growing in the area. Even though it is a tropical grass, Vetiver can grow in a wide range of climate conditions, including those that are quite harsh in terms of both soil and climate. The roots can grow up to 3 m in length in a dense bushy root network under optimal conditions. In this review, the authors have studied the impact of Vetiver on landslide mitigation as a climate-adaptive slope repair tool based on the research undertaken so far. Furthermore, the authors have addressed the future potential and constraints associated with the use of Vetiver for landslide mitigation. It is seen that the use of Vetiver reduces pore water pressure. The high tensile strength of Vetiver roots provides reinforcement for slopes and enhances soil shear strength. Vetiver increases saturated hydraulic conductivity and reduces surface runoff and slip surface depth. Being a vegetation-based climate-adaptive technology, this grass exhibits great promise in its ability to effectively address landslide problems. However, the magnitude of the root impact diminishes as the depth increases, rendering Vetiver a more promising remedy for shallow landslide occurrences. In addition, Vetiver grass has a wide range of practical uses due to its unique characteristics, which provide additional benefits. Employment of Vetiver is cost-effective compared with traditional engineering methods, and it requires less initial maintenance, which implies that community-based initiatives can effectively address landslide prevention through Vetiver implementation 

Due to cyclic wetting and drying, the hydro-mechanical behavior of unsaturated soil is impacted significantly. In order to assess the soil strength parameters, knowing the unsaturated behavior is important. Soil moisture content is an important parameter that can define the shear strength of the soil. Most of the highway slopes of Mississippi are built on highly expansive clay. During summer, the evaporation of moisture in the soil leads to shrinkage and the formation of desiccation cracks, while during rainfall, the soil swells due to the infiltration of water. In addition to this, the rainwater gets trapped in these cracks and creates perched conditions, leading to the increased moisture content and reduced shear strength of slope soil. The increased precipitation due to climate change is causing failure conditions on many highway slopes of Mississippi. Vetiver, a perennial grass, can be a transformative solution to reduce the highway slope failure challenges of highly plastic clay. The grass has deep and fibrous roots, which provide additional shear strength to the soil. The root can uptake a significant amount of water from the soil, keeping the moisture balance of the slope. The objective of the current study is to assess the changes in moisture contents of a highway slope in Mississippi after the Vetiver plantation. Monitoring equipment, such as rain gauges and moisture sensors, were installed to monitor the rainfall of the area and the moisture content of the soil. The data showed that the moisture content conditions were improved with the aging of the grass. The light detection and ranging (LiDAR) analysis was performed to validate the field data obtained from different sensors, and it was found that there was no significant slope movement after the Vetiver plantation. The study proves the performance of the Vetiver grass in improving the unsaturated soil behavior and stability of highway slopes built on highly expansive clay.