FIELD OPERATION CENTER Coastal Hydraulics Lab. Field Data Collection and Analysis Branch Our Team Members

Transcription

1 FIELD OPERATION CENTER Coastal Hydraulics Lab Field Data Collection and Analysis Branch Our Team Members

2 Mobile/Static LIDAR and Photogrammetry Platforms and Equipment

3 Mobile/Static LIDAR and Photogrammetry Platforms and Equipment, cont. Riegl VMZ2000 Ladybug5 Spherical Camera Mobile LIDAR Setup at Dugway Proving Grounds, UT Velodyne 32

4 Structural Change Detection Lidar and Photogrammetry Thin Arch Dam Model 1:30 Scale, non-reinforced concrete Pre-test scans compared to post-test scans to determine maximum deviation in monoliths Microstation Topodot Wall Monitoring Tool used to determine deviations Microstation elements colored by maximum deviation from pre-test position of wall. White elements fall within tolerance/accuracy of scanner. Therefor minimal occurred.

5 Thin Arch Dam Photogrammetry vs LIDAR Photogrammetry compared to static lidar (2014) 117 total 5cm targets placed at corners of monolith sections Other considerations should be distribution of targets

6 Thin Arch Dam Deviations Downstream Downstream Upstream Upstream Pre-Test Pre-Test Post-Test Post-Test Deviation Deviation = = US Ft Deviation = US Ft Deviation = US Ft Deviation = = US Ft Deviation Deviation = = US US Ft Ft Deviation = US Ft Deviation = US Ft Ft Deviation = US US Ft Ft Deviation = US Ft Deviation = US Ft Deviation = = US Ft Ft Deviation = = US Ft Ft Deviation Deviation = = US US Ft Ft Deviation = US Ft Deviation = US Ft Deviation = US US Ft Ft Deviation = US Ft Deviation = US Ft Deviation Deviation = US US Ft Ft Deviation = 0.003US Ft

7 Chickamauga Lock and Dam Comparison of 2010 and 2016 Static LIDAR Surveys to try and detect concrete growth Scan positions tied together to provide XYZ global coordinate system files. Microstation TopoDOT used to set planes by referencing respective point clouds. TopoDOT Wall Monitoring Tool used to create spreadsheets of cross sections with the deviation measurements.

8 ERDC -CHL Bridge Pier Changes Detected Cross Section of Panel 69 showing that from 2010 to 2016, the landside bridge pier that is adjacent to the landside lockwall has tilted approximately m toward the river BUILDING STRONG

9 LIDAR used for Forensic Investigations

10 Photogrammetry Images taken by hand or from a drone Final Product is a pointcloud with RGB, Mosaic, or DTM Pointclouds need to be scaled Use of a known measurement from the images or pointcloud Use of surveyed targets 4K Video collected with a Quad Copter. converted video to 2 frame per sec for total of 108 images. Processing time took ~2 hrs 10 mins for a total of 2.79 million points (tank only) 4K Video collected with a Quad Copter. converted video to 1 frame per sec for total of 182 images. Processing time took ~5 hrs for a total of 3.4 million points Software Used by ERDC: Agisoft Photoscan Cloud Compare Meshlab QT Modeler RiSCAN Pro Context Capture in Microstation Some Issues: ISO settings Focal Length Alignment of photos Number of Images

11 The Mission Two-fold for Army and Air Force Use of Lidar and Photogrammetry for Automatic Detection Airfield Distresses Pavement Condition Index Airfield Damage Assessment Automate the PCI procedure and determine full airfield PCI Assess airfield equivalent to Kadena, AFB 110,000 linear feet of pavement Manual = 3 man team 3 to 4 days LiDAR = 2 man team 1 to 2 days LiDAR/Photo 1 man team Less than 1 day Objects to Identify All cracking Surface roughness Depressions, faulting Visual distresses Object Scale ~ 1/8 width ~ 1/4 height Objects to Identify Craters and extents Spalls and camoflets UXO position and ID Object Scale 2 to 10 diam. 0.5 to 1 diam. ~ 2 object min. Automatically detect, classify and quantify all ASTM distresses Timeframe 30 minutes to scan, process and output results

12 Technology Employed P L A T F O R M S Mobile LiDAR Reigl VZ-2000 Boom Camera Matrix-E Drone EO/IR IMU GPS 36mp Camera 36mp Camera GPS 24mp Camera Tau-Infrared LiDAR Generate 3-D point clouds with Intensity or RGB Photogrammetry Chunk point cloud data Sample units allow faster processing

13 MatLab tools allow development of automatic detection algorithms for: Tools Developed Airfield Roughness using Boeing Bump, Longitudinal and Traverse profiles and rut/depressions, Crack identification and visualization Airfield Damage Assessment Profiles Cracks Roughness

14 Airfield Damage Quickly obtain LiDAR point clouds and analyze for key airfield damage distresses: Crater size and extent, Spall fields & Camoflets UXO using LiDAR intensity Upload shapefiles with identified distresses via Gas server to GeoXPT Automatically detect crater/spall location and size with 2-phase analysis UXO detection via Intensity

15 Pavement Condition Index Detect cracks via integration of three methodologies Standard deviation of surface elevation Filling algorithm Shaving algorithm Extract crack map as a binary file color coded based on width Green, Yellow and Red for Low, Medium and High severity LiDAR Intensity Crack width (mm) Computer Vision Crack shaving Use machine learning to classify cracks into proper ASTM types Upload distresses and severity into PAVER

16 Crater Measurement GUI Cross-Section GUI Object Measurement GUI

17 Photogrammetric Technique for Sand Cone Photo-Density Procedure Step 1 Dig any size hole and weigh soil Performance Sand Cone compares well to Nuclear Gauge Assumed to be reference standard Photogrammetry compares similarly to both techniques all similar R 2 values near 90% Step 2 Photograph hole w/ 8 to 16 photos Step 3 Process photos and obtain volume

18 GUI To obtain the soil density, input the wieght of soil removed (in grams) and density is returned in g/cc. Multiply by 62.4 to get lbs/cf. Input moisture content if known and the software will calculate the dry density as well in g/cc.

19 QUESTIONS?