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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.
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This content will become publicly available on February 27, 2026
Evaluation of Climate Resiliency of Highway Embankment Using LiDAR and Electrical Resistivity Imaging
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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- Award ID(s):
- 2046054
- PAR ID:
- 10584452
- Editor(s):
- Beauregard, Melissa S; Budge, Aaron S
- Publisher / Repository:
- American Society of Civil Engineers
- Date Published:
- ISBN:
- 9780784485996
- Format(s):
- Medium: X
- Location:
- Louisville, Kentucky
- Sponsoring Org:
- National Science Foundation
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