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How Rough Is Your Runway?

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How Rough Is Your Runway?

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Using Trimble TX9 terrestrial laser scanner my surveying team scanned an active runway in Western Australia to a tolerance spec of 3mm. The teams were working a live site providing aircraft right of way meant the teams had to setup and takedown scanner and targets to yield to any aircraft movements around the site and airspace. Surveyors took a ground based approach over drone UAS to maintain tighter vertical control than can be achieved using drone capture. We were tasked with looking for deviations, rutting and areas to derive Pavement Condition Index (PCI) criteria for their asset.

Once the data was captured surveying teams utilized TopoDot to assemble the raw scans into a consolidated model. They then attempted to use the pavement roughness algorithms in the software against the close to 3.4b points of classified data but had to split the datasets into halves and quads in order for the processing runs to complete. The Bentley product has an inbuilt “Road condition tool” which reports on pavement roughness characteristics but has preset expected pavement widths, roads not runway widths, set in the software. We explained to our surveyors that the algorithms might run faster in another product. It allowed us to explore FME as a point cloud processing workflow using feature tables functionality to quickly generate the statistics required for reporting deliverables using the entire dataset in one process.

Using Trimble TX9 terrestrial laser scanner my surveying team scanned an active runway in Western Australia to a tolerance spec of 3mm. The teams were working a live site providing aircraft right of way meant the teams had to setup and takedown scanner and targets to yield to any aircraft movements around the site and airspace. Surveyors took a ground based approach over drone UAS to maintain tighter vertical control than can be achieved using drone capture. We were tasked with looking for deviations, rutting and areas to derive Pavement Condition Index (PCI) criteria for their asset.

Once the data was captured surveying teams utilized TopoDot to assemble the raw scans into a consolidated model. They then attempted to use the pavement roughness algorithms in the software against the close to 3.4b points of classified data but had to split the datasets into halves and quads in order for the processing runs to complete. The Bentley product has an inbuilt “Road condition tool” which reports on pavement roughness characteristics but has preset expected pavement widths, roads not runway widths, set in the software. We explained to our surveyors that the algorithms might run faster in another product. It allowed us to explore FME as a point cloud processing workflow using feature tables functionality to quickly generate the statistics required for reporting deliverables using the entire dataset in one process.

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How Rough Is Your Runway?

  1. 1. How Rough is your Runway? Steven Cyphers
  2. 2. GHD ● FME Certified Professional ● AWS Certified Cloud Practitioner ● FME 10 years
  3. 3. Carnarvon Airport ● Regional Airport in WA ● Runway 1882m x 23m ● Assess structural capacity ● Identify extent / location of longitudinal depressions
  4. 4. Terestrial Laser Scanning ● Undertaken in two days ● Trimble TX8 ● Live runway – yield to aircraft ● All Targets & Scanner removed ● Tolerence spec 3mm
  5. 5. Rough workflow ● Assessing bumps 1m features at a time ●
  6. 6. first The problem The data is large, best to only use it as feature table & as little as possible. The solution Tile, Calculate PC stats, populate the tiles. Show the result Create CL and Xsection features, Drape and extract third Share some tips Set the audience up for success! last The Process Is EASY The processing requires some tricks.
  7. 7. Build PointCloud Statistics.
  8. 8. Build tiles to receive PC stats
  9. 9. Build Section lines from tiles
  10. 10. Drape surface into Section lines
  11. 11. Charts Station
  12. 12. Future Directions
  13. 13. Boeing Bump Index Acceptable Excessive (Bump length, m) (Bumpdepth,cm) Unacceptable
  14. 14. Feature table … want to do raster like functions. (Tiles) Pointcloud processing (pitfalls)
  15. 15. Feature Tables are very fast Make vector geometry Vector nodes Raster math (windowing TBA 2018.1) Pointcloud processing (wins)
  16. 16. THANK YOU! Steven Cyphers Steven.cyphers@ghd.com

Editor's Notes

  • 4500:300:20
    Design : GIS : FME
    https://www.ghd.com/en-us/about-us/about-us.aspx
    https://twitter.com/scyphers_
  • Radio https://goo.gl/images/vFyDTC
    Leica scanner https://goo.gl/images/fnX7W9
  • Tiled PC Stats that is
  • Tiled PC Stats that is
  • Using the upper and lower corners of the tile shapes to generate the section lines
  • Using the upper and lower corners of the tile shapes to generate the section lines
  • Using the upper and lower corners of the tile shapes to generate the section lines

    Long wave depressions, hillshade tools (thank you Dmitri)
  • Tiled PC Stats that is

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