Asset Characterization Using Automated Methods

Details

• Creators:
  Kim, Sungyop ; Baker, Donald ; Lee, Jejung ; Sprague, Aaron ; Mwaipopo, Charles
• Corporate Creators:
  University of Missouri--Kansas City ; Water Resources Solutions, LLC
• Corporate Contributors:
  Missouri. Department of Transportation. Construction and Materials Division ; United States. Department of Transportation. Federal Highway Administration
• Subject/TRT Terms:
  Asset Management Bridges Culverts Data Quality Laser Radar Machine Learning
• Publication/ Report Number:
  cmr 25-005
• Resource Type:
  Tech Report
• Geographical Coverage:
  Missouri;United States
• Edition:
  Final Report (June 2023-March 2025)
• Corporate Publisher:
  Missouri. Department of Transportation
• Abstract:
  Increasing and intensifying flood events pose serious challenges to highway agencies in maintaining water crossing assets and assessing potential flood hazards. This project aimed to develop an automated flood risk assessment program for small water crossing assets, focusing on culverts with less than a 20-foot span not listed in the 2023 National Bridge Inventory (NBI). Information on such small assets is often limited and not readily available. Using a Geographic Information System (GIS), the project identified potential non-NBI water crossing sites and conducted field surveys to collect the asset information on Missouri's highways. Based on the field survey data, the project developed a GIS-based flood risk assessment tool for small water crossing assets, called the Missouri Automated Culvert Analysis Tool (MoACAT) QGISPlugin, using publicly available high-resolution Light Detection and Ranging (LiDAR) data. The tool was built with Python scripts that process four sequential steps on the Quantum Geographic Information System (QGIS), the most popular non-proprietary open-source GIS software program. The project conducted a pilot study of culverts in Cass County, Missouri. The plugin hydraulic results were compared against HEC-RAS 2D modeling results. The results indicate that the tool has the potential to identify small water crossing assets and predict flood overtopping efficiently.
• Format:
  PDF
• Funding:
  TR202311
• Collection(s):
  US Transportation Collection
• Main Document Checksum:
  urn:sha-512:2fa542fade5d85fc30d6d512b4ec6773f578d56aafc6b04284506e0d0032b6d97311504c59ca167c0327769b711774a415a74b7064c4d5bd788d39a5b848e1a9
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/85304/dot_85304_DS1.pdf
• File Type:
  [PDF - 3.74 MB ]