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1. Evaluation of Climate Resiliency of Highway Embankment Using LiDAR and Electrical Resistivity Imaging

   https://doi.org/10.1061/9780784485996  

   Rahman, Fariha; Zohuruzzaman, A; Khan, Sadik; Whyte, Tyra_Nicole (February 2025, American Society of Civil Engineers)

   Beauregard, Melissa S; Budge, Aaron S (Ed.)

   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. 

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2. Evaluating Machine Learning Models for Identifying At-Risk Highway Slope Assets

   Salunke, R; Khan, S (March 2025, American Society of Civil Engineers)

   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. 

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3. Impact of Vetiver Plantation on Unsaturated Soil Behavior and Stability of Highway Slope

   https://doi.org/10.3390/geosciences14050123  

   Rahman, Fariha; Chakraborty, Avipriyo; Khan, Sadik; Salunke, Rakesh (May 2024, Geosciences)

   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. 

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4. Distributed Cooperative Spectrum Sharing in UAV Networks Using Multi-Agent Reinforcement Learning

   https://doi.org/10.1109/CCNC.2019.8651796  

   Shamsoshoara, Alireza; Khaledi, Mehrdad; Afghah, Fatemeh; Razi, Abolfazl; Ashdown, Jonathan (February 2019, 16th IEEE Annual Consumer Communications & Networking Conference (CCNC))

   In this paper, we develop a distributed mechanism for spectrum sharing among a network of unmanned aerial vehicles (UAV) and licensed terrestrial networks. This method can provide a practical solution for situations where the UAV network may need external spectrum when dealing with congested spectrum or need to change its operational frequency due to security threats. Here we study a scenario where the UAV network performs a remote sensing mission. In this model, the UAVs are categorized to two clusters of relaying and sensing UAVs. The relay UAVs provide a relaying service for a licensed network to obtain spectrum access for the rest of UAVs that perform the sensing task. We develop a distributed mechanism in which the UAVs locally decide whether they need to participate in relaying or sensing considering the fact that communications among UAVs may not be feasible or reliable. The UAVs learn the optimal task allocation using a distributed reinforcement learning algorithm. Convergence of the algorithm is discussed and simulation results are presented for different scenarios to verify the convergence. 

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5. Mapping tundra ecosystem plant functional type cover, height and aboveground biomass in Alaska and northwest Canada using unmanned aerial vehicles, 2018-2019

   https://doi.org/10.18739/A2R785Q5B  

   Orndahl, Kathleen (January 2022, Arctic Data Center)

   Arctic landscapes are rapidly changing with climate warming. Vegetation communities are restructuring, which in turn impacts wildlife, permafrost, carbon cycling and climate feedbacks. Accurately monitoring vegetation change is thus crucial, but notable mismatches in scale occur between current field and satellite-based monitoring. Remote sensing from unmanned aerial vehicles (UAVs) has emerged as a bridge between field data and satellite imagery mapping. In this work we assess the viability of using high resolution UAV imagery (RGB and multispectral), along with UAV derived Structure from Motion (SfM) to predict cover, height and above-ground biomass of common Arctic plant functional types (PFTs) across a wide range of vegetation community types. We collected field data and UAV imagery from 45 sites across Alaska and northwest Canada. We then classified UAV imagery by PFT, estimated cover and height, and modeled biomass from UAV-derived volume estimates. Here we present datasets summarizing this data. 

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