Search for: All records

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.