Asset Characterization Using Automated Methods

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) QGIS Plugin, 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.  

VIEW FINAL REPORT

Report number: cmr 25-005
Published: April 2025

Project number: TR202311

Authors: Sungyop Kim, Donald Baker, Jejung Lee, Aaron Sprague, and Charles Mwaipopo

Performing organization: University of Missouri-Kansas City and Water Resources Solutions, Inc.