Project #578 - UTC Safety21: Improve Transportation Infrastructure (Geotechnical Asset) Safety by Reducing the Risks of Landslides - Phase 3

Details

• Alternative Title:
  UTC Safety21: Improve Transportation Infrastructure (Geotechnical Asset) Safety by Reducing the Risks of Landslides - Phase 3
• Creators:
  Sheng, Zhuping ; Owolabi, Oludare ; Liu, Yi ; Nyarko, Kofi
• Corporate Creators:
  Morgan State University. Department of Civil and Environmental Engineering
• Corporate Contributors:
  Carnegie Mellon University. Traffic21 Institute. Safety21 University Transportation Center (UTC) ; United States. Department of Transportation. University Transportation Centers (UTC) Program ; United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology ; United States. Department of Transportation. Federal Highway Administration
• Subject/TRT Terms:
  • Landslides
  • Slope stability
  • Laser radar
  • Machine learning
  • Rainfall
  • Geographic information systems
  • Benchmarks
  • Infrastructure
  • Geotechnical engineering
  • Assets
  More +
• Resource Type:
  Tech Report
• Geographical Coverage:
  United States ; Maryland
• Edition:
  Final Report July 1, 2025-June 30, 2026
• Corporate Publisher:
  Carnegie Mellon University. Traffic21 Institute. Safety21 University Transportation Center (UTC)
• Abstract:
  This report summarizes Phase 3 research findings of the project Improve Transportation Infrastructure (Geotechnical Asset) Safety by Reducing the Risks of Landslides, sponsored by the National UTC Safety21 program. The work advances an integrated framework for detecting and monitoring slope instability and assessing landslide risk along highways, railroads, and roadways. The studies include geometry-resolved InSAR monitoring and directional forecasting at Haystack Mountain; intervention-aware InSAR and multi-horizon machine learning for the Baltimore CSX Belt Line; a simulation-based CaseBank and machine-learning framework for rapid slope-stability assessment; soil-specific probabilistic rainfall thresholds for landslide initiation in Maryland; physics-informed multi-sensor machine-learning/deep-learning benchmarking and LiDAR monitoring for early warning; laboratory characterization of soils from Maryland landslide-inventory sites; and a national railroad landslide-exposure inventory developed through GIS-based proximity screening.
• Format:
  PDF
• Funding:
  69A3552344811 ; 69A3552348316
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/93514/dot_93514_DS1.pdf
• File Type:
  [PDF - 9.34 MB]
• Collection(s):
  University Transportation Centers
• Main Document Checksum:
  urn:sha-512:fe8d263ae3ea276576deb6da16760177d53ea0327bef0f3e99f195281950d52b19303936ed9740e4cc04b5745eeef0af89466a2a72ce2a4141d37e3b5cc9a6e4