7 Jul 2022

Validation of the Automatic Dynamic Cone Penetrometer

Abstract: The U.S. military requires a rapid means of measuring subsurface soil strength for construction and repair of expeditionary pavement surfaces. Traditionally, a dynamic cone penetrometer (DCP) has served this purpose, providing strength with depth profiles in natural and prepared pavement surfaces. To improve upon this device, the Engineer Research and Development Center (ERDC) validated a new battery-powered automatic dynamic cone penetrometer (A-DCP) apparatus that automates the driving process by using a motor-driven hammering cap placed on top of a traditional DCP rod. The device improves upon a traditional DCP by applying three to four blows per second while digitally recording depth, blow count, and California Bearing Ratio (CBR). An integrated Global Positioning Sensor (GPS) and Bluetooth® connection allow for real-time data capture and stationing. Similarities were illustrated between the DCP and the A-DCP by generation of a new A-DCP calibration curve. This curve relates penetration rate to field CBR that nearly follows the DCP calibration with the exception of a slight offset. Field testing of the A-DCP showed less variability and more consistent strength measurement with depth at a speed five times greater than that of the DCP with minimal physical exertion by the operator.

4 May 2021

First Generation Automated Assessment of Airfield Damage from LiDAR Point Clouds

Abstract: 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.