Improve highway safety by reducing the risks of landslides (Phase 2).

Geologic hazards including slope failures, landslides, mudflows, debris flows, etc. and hydrological hazards related to floods and stormwater surge can be destructive to transportation infrastructure and threaten property and human life along the highway and roads. Landslides alone cause thousands of deaths and many billions of dollars in damage every year. Morgan State University team proposes a multi-phase (multi-year) project focusing on safety of transportation infrastructure systems by preventing geohazard, specifically slope failure and landslides and minimizing impacts of geohazard. This project will employ an integrated approach of geotechnical and Artificial Intelligence (AI)/Machine Learning methods for assessing conditions of geotechnical assets, such as cut slopes and embankment of the Maryland Department of Transportation (DOT SHA) and delineating landslides and high-risk areas. The objectives (tasks) of the proposal include: (1) with AI/Machine Learning approaches assess the risks of landslides based on soil/rock types, weather conditions, mechanical properties of slope materials, stream gage station flow data, pavement material and design, and the status of existing retaining structures along the selected highway sections, using Maryland as case studies, (2) identify and map the high-risk areas based on controlling factors such as geometry and mechanical properties of soil or rock, and triggering factors, including gravitational and hydraulic forces, using available survey data, remote sensing and LIDAR data and other factors like transportation modes, (3) design and test protocols for real time monitoring at selected sites in consultation with DOT SHA staff, and (4) recommend strategies for reducing the risks of landslides with real-time monitoring for the high-risk areas, and improving the safety of the transportation infrastructure. All the methods and strategies can be transferred to other states or regions with similar geological conditions and engineering configurations. Phase 1 of this project will primarily cover task 1 and part of task 2. Phase 2 will continue part of task 1 and task 2. This project will primarily complement the ongoing project sponsored by the Maryland DOT SHA (see more information in TRID) led by Zhuping Sheng in collaboration with Carnegie Mellon University (CMU) (Dr. Sean Qian). In this phase the research team will also expand their collaboration with CMU team (Dr. Christoph Mertz) by including technology transfer in photographical images processing to build conceptual models and identify slope failures. Dr. Zhuping Sheng has experience in geohazards assessment and mitigation, geotechnical and water resources engineering. As PI Dr. Sheng will coordinate the efforts in collaboration with MDOT/SHA and advise other faculty and postdoctoral research associates and graduate students to carry out the project. The team includes Co-PIs, Dr. Oludare Owolabi with experience in transportation engineering and resilient infrastructure and Dr. Yi Liu with experience in geohazards, land subsidence and landslides and geotechnical engineering. They are currently conducting research supported by MDOT SHA, which provides a strong foundation for future collaboration with the partner MDOT SHA and others for technical transfer. This program includes a summer internship program with two students and one graduate team for development of future workforce in transportation safety led by Dr. Owolabi in cooperation with MSU AI/ML program through National Center for Equitable Artificial Intelligence and Machine Learning Systems (CEAMLS) led by Dr. Kofi Nyarko. Students have participated in and will continue to participate in exchange programs and deployment partner symposium and other activities. Through this project the MSU team will continue to expand collaboration with CMU and other partner institutes via faculty meetings, seminars, national summit, and other venues, which provides great opportunity for professional development.

• Record URL:
  • https://ppms.cit.cmu.edu/projects/detail/492

Language

• English

Project

• Status: Active
• Funding: $600000
• Contract Numbers:

  69A3552344811

• Sponsor Organizations:

  Office of the Assistant Secretary for Research and Technology

  University Transportation Centers Program
  Department of Transportation
  Washington, DC  United States  20590
• Managing Organizations:

  Carnegie Mellon University

  Pittsburgh, PA  United States 

  Safety21 University Transportation Center

  Carnegie Mellon University
  Pittsburgh, PA  United States  15213
• Project Managers:

  Stearns, Amy

• Performing Organizations:

  Morgan State University

  1700 E. Cold Spring Lane
  Baltimore, MD 21251, Maryland  United States  21251
• Principal Investigators:

  Sheng, Zhuping

• Start Date: 20240701
• Expected Completion Date: 20250630
• Actual Completion Date: 0
• USDOT Program: University Transportation Centers

Subject/Index Terms

• TRT Terms: Artificial intelligence; Highway safety; Landslides; Machine learning; Mapping; Monitoring; Risk management; Slope failure
• Subject Areas: Geotechnology; Highways; Planning and Forecasting; Safety and Human Factors;

Filing Info

• Accession Number: 01933388
• Record Type: Research project
• Source Agency: Safety21 University Transportation Center
• Contract Numbers: 69A3552344811
• Files: UTC, RIP
• Created Date: Oct 12 2024 12:09PM