There is a need for a simple, accurate soil density measurement system that does not require extensive calibration or significant health and safety measures for compaction quality control. This research describes the invention of a photogrammetric technique for obtaining the volume of an excavated hole in soil. This procedure requires a series of 8 to 16 digital photographs with a standard digital camera around the perimeter of an excavated hole with a reference scale in the scene. Algorithms convert the digital photographs into a colorized three-dimensional point cloud, which is automatically rotated into a plan view and displayed via the Matlab graphical user interface. Once the reference scale dimensions are input, the volume of the hole is calculated via a user selected ground plane. Once the mass of the excavated soil is input, the wet density of the soil is calculated by dividing by the volume of the hole. This procedure has been validated against both the nuclear density gauge and the sand cone apparatus and found to be equivalent in accuracy to both. This procedure enables soil density determination within 15 min with no replacement material, no specific excavated hole dimension, and no safety or health risks.

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This research developed an automated software technique for identifying type, size, and location of man-made airfield damage including craters, spalls, and camouflets from a digitized three-dimensional point cloud of the airfield surface. Point clouds were initially generated from Light Detection and Ranging (LiDAR) sensors mounted on elevated lifts to simulate aerial data collection and, later, an actual unmanned aerial system. LiDAR data provided a high-resolution, globally positioned, and dimensionally scaled point cloud exported in a LAS file format that was automatically retrieved and processed using volumetric detection algorithms developed in the MATLAB software environment. Developed MATLAB algorithms used a three-stage filling technique to identify the boundaries of craters first, then spalls, then camouflets, and scaled their sizes based on the greatest pointwise extents. All pavement damages and their locations were saved as shapefiles and uploaded into the GeoExPT processing environment for visualization and quality control. This technique requires no user input between data collection and GeoExPT visualization, allowing for a completely automated software analysis with all filters and data processing hidden from the user.