A Framework for Assessing the Bearing Capacity of Sandy Coastal Soils from Remotely Sensed Moisture Contents

AbstractThe strength of partially saturated sand is critical for addressing engineering challenges such as beach trafficability or coastal erosion. Mapping strength remotely would allow for surficial geotechnical characterization despite reduced site access. A framework for estimating and mapping the bearing capacity of sandy beach soils from remotely sensed moisture contents and standard sand characteristics is presented. Toward this goal, the variability of relevant sediment parameters (d60, d10, dry unit weights, porosities, moisture contents, and angles of repose) were documented for the quartz sand beach located near the US Army Corps of Engineers Field Research Facility in Duck, North Carolina, and were assumed to follow normal distributions. Concurrently, in situ bearing capacity measurements were recorded using a portable free fall penetrometer (PFFP). Measured values were compared to bearing capacities derived using a model for partially saturated sands, resulting in a correlation coefficient, ρ, of 0.83 for stations where sediment samples were collected simultaneously. When sediment data was unknown, standard characteristics for medium sand were used to estimate the bearing capacity, yielding ρ=0.71 when compared to PFFP deployments. For stations where the estimated and measured bearing capacities did not match (residuals >10 kPa), impacts of upward-directed hydraulic gradients, rapid changes in moisture content, and unit weight estimates are discussed. Finally, the proposed framework is applied to spatially estimate bearing capacities using moisture contents derived from satellite-based data and medium sand characteristics to suggest the safest pathways for a test vehicle. Moisture contents were derived from an optical WorldView-4 image and an image from the X-band synthetic aperture radar Cosmo-SkyMed 2 satellite. The spatial estimates of moisture content and bearing capacity generally follow the expected trends for both images. Shortcomings of the framework for predicting the safest path are discussed, and an alternative analysis using moisture contents is presented.