A Cross-Platform Approach Using Remote Sensing and Geophysical Monitoring to Streamline Geotechnical Asset Management

Abstract

ASCE Report Card of 2020 rated transportation infrastructure poorly for many states across the United States. Mississippi has its share of challenges regarding deteriorating multimodal infrastructure. Integral parts of the spatially distributed infrastructure are geotechnical assets such as highway embankments, retaining walls, subgrades, and bridge foundations. Traditionally, Transportation Asset Management (TAM) programs have primarily focused on pavement and bridge assessment with minimal attention to geotechnical assets. Geotechnical Asset Management (GAM) is still in the nascent design and implementation stage across the United States, including MS. There is a need for specific guidance involving modern techniques to evaluate geotechnical assets and prioritize maintenance funds to reduce risk to the infrastructure systems. Highway embankments are one of the most common and at-risk geotechnical assets in MS. The risks stem from high rainfall and cyclic weather patterns and are further compounded by expansive clay in many of the embankments in MS. This study investigated the performance of failed highway embankments across the central MS region using multiple approaches. Temporally spaced data was collected through remote sensing techniques such as light detection and ranging (LiDAR), geophysical investigations such as electrical resistivity, and in situ instrumentation such as an Inclinometer. 3D laser scanning was performed using the terrestrial LiDAR equipment Trimble X7. Dense point clouds data was acquired from a highway embankment that had previously experienced extreme surficial deformations and failure. Topographical surfaces were developed using the dense point cloud data, and the bare ground was extracted. Temporally spaced surface profiles of the failed and undamaged areas of the slopes were created for comparative analysis. The surface profiles and the subsurface resistivity images were matched against the inclinometer and instrumentation data collected across the same period. This comparative analysis provided valuable insights into surficial soil movement with varying weather patterns and subsurface moisture variations. Combining remote sensing and geophysical investigations will help develop a modern performance monitoring methodology for integration into the GAM framework. Furthermore, the results from this study promise a robust cross-platform assessment approach that can be expanded to other geotechnical assets and help streamline Geotechnical Asset Management.