Abstract
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 (geotechnical asset) and threaten property and human life along the highway, railroad and road ways. 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 AI/Machine Learning methods for assessing conditions of geotechnical assets, such as cut slopes and embankment of the 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 and railroad, (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 and InSAR data and other factors like transportation modes, (3) design and test protocols for real time monitoring at selected sites in consultation with agencies and organizations (such as DOT SHA, Federal Railroad Administration), 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 covered task 1 and part of task 2. Phase 2 covers part of task 1 and task 2. Phase 3 will cover task 3 and will also include literature review and summary of landslides along the railroad. 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 CMU (Dr. Sean Qian). In this phase we will also expand our collaboration with CMU team (Dr. Christoph Mertz) by including technology transfer in photographical images processing to build conceptual models and identify slope failures.
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 under Tasks 1 and 2. Dr. Zhuping Sheng has experience in geohazards assessment and mitigation, geotechnical and water resources engineering. The team includes Co-PIs, Dr. Oludare Owolabi with experience in transportation engineering and resilient infrastructure and will mainly cover Task 1 and Dr. Yi Liu with experience in geohazards, land subsidence and landslides and geotechnical engineering and will cover Task 2. They have conducted and 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. Dr. Kofi Nyarko, Professor of the Electrical and Computer Engineering and Director of Morgan AI/ML program, has extensive experience in AI/ML research and applications, including automating complex systems through computer vision and machine learning as well as scientific/engineering simulation & visualization, predictive visual analytics and will mainly cover Task 3.
The Morgan AI/ML program will provide in-kind support (Dr. Nyarko’s time) and support for one student who will help with sensor tests, calibration and assembling as part of technical transfer at selected sites in consultation with Maryland DOT/SHA. We will also explore opportunity for summer intern program with both UTC and the AI/ML program's support, which is designed for development of future workforce in transportation safety. 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.
Description
Timeline
Strategic Description / RD&T
Section left blank until USDOT’s new priorities and RD&T strategic goals are available in Spring 2026.
Deployment Plan
Quarter 1 (July – September)
• Share progress report and research findings with MDOT SHA work and other partners supported by UTC Safety 21 for Phases 1 and 2.
• Share the work plan with MDOT and other partners, and request information related to landslides from the partner and arrange additional site visits and sample collection for Phase 3, especially landslides related to railroad.
• Continue technology transfer with CMU partners on photo imaging processing and interpretation, and monitoring.
Quarter 2 (October- December)
• Share UTC proto type work with MDOT SHA and other partners and seek inputs from the partners.
• Initiate plan for implementation for the monitoring system at one or two selected sites.
• Participate in Deployment Partners Symposium and explore opportunities for collaboration and partnership.
Quarter 3 (January - March)
• Continue to share UTC work with SHA and share findings from the monitoring system.
• Technology transfer and collaboration with CMU.
• Plan and prepare for a workshop.
Quarter 4 (April - June)
• Organize the workshop for DOT SHA staff and other professionals on MSU campus.
• Propose future internship activities in collaboration with Morgan AI/ML program.
Besides direct interaction with the partner, faculty and students will participate in DOT National Safety Summit and Safety 21 Deployment Partners Consortium Symposium, Safety Faculty Meetings, Safety Seminar, CUTC Summer Conference, and other related UTC activities. meetings. Dr. Sheng is involved in an NSF project: Broadening Adoption of Cyberinfrastructure and Research Workforce Development for Disaster Management. He will share findings and promote UTC programs through its webinar series and recommend UTC program students participate in trainings. MSU team members will also participate in webinars, conferences, and meetings with stakeholders to disseminate project findings and promote UTC products.
The research will sponsor two doctoral students (Ph. D. in Sustainable and Resilient Infrastructure Engineering) and two STEM undergraduate students at MSU. During the summer these students will acquire knowledge about how emerging technologies (AI/ML) will be used in adapting transportation infrastructure against geohazards. We shall provide a sequence of workshops in-order to create a community of resilient engineers that will be committed to adapting our highway infrastructure systems against geohazards.
Risk Mitigation Plan: The project may face potential risks/pitfalls, such as field site access delays and sensor malfunctions. Alternative site on campus will be used to test the prototype of monitoring system in case of lack of access to the field sites. To overcome malfunction of the sensors, all the sensors will be tested in the lab first to ensure its reliability. In case encountering malfunction of sensors during testing and operations, researchers will trouble shoot onsite and use pretested backup sensors to replace those failed sensors.
Expected Outcomes/Impacts
Besides advance in research and teaching related to transportation infrastructure safety and disaster management, the project is expected to bring broad positive impacts on transportation system in the following aspects:
Early detection of slope failure with warning and monitoring systems, especially for those road sections with recurring landslides, could help to design and implement measures to provide safe routing to avoid injury to vehicle occupants and minimize damage to the physical infrastructures. It is expected that early warning system could detect slope failures in advance one week or two weeks, reducing potential failure by 20 to 30%.
Reliable assessment of landslide risk with new technology, such as AI/ML approaches and image interpretation, could help with preventive maintenance to avoid slope failures, assuring safe and reliable roadways and saving costs by reducing the impacts of potential failures. Pilot systems can be deployed at 1 or 2 landslides-prone segments.
Research findings will help policy makers to improve the roadway design standards and secure resources to assure long-term safety of transportation systems under extreme weather conditions.
Research results, including assessment approaches and design protocols, are transferable to other states and regions with similar geological and hydrological conditions and roadway designs.
In addition, this project will support initiatives that enhance doctoral achievement in the STEM and non-STEM disciplines and provide educational opportunities for student assistants to gain knowledge and experience for civil and environmental engineering. Faculty and researchers will participate in different professional conferences, reaching out to students, professionals, entrepreneurs, and employers in science, technology, engineering, and math fields.
Expected Outputs
The following are expected outputs for the Phase 3 of the proposed project:
• 1 to 2 Journal articles on methodology and case studies
• 4 to 5 conference presentations at professional conferences and technical meetings
• Literature review summary of landslides on the railroads and preliminary findings of distribution patterns
• Enhanced GIS coverages (LiDAR and InSAR data) of detail delineation of selected landslides including those on railroads at two or more selected regions based on data availability from USGS by the end of project.
• Integrated models for landslides risk assessment with physical models and machine learning models (modified or new): Calibrated models will be completed at selected sites (one watershed or selected counties within Maryland based on the preliminary work last two years) by the end of the project.
• Initial prototype of the monitoring system for early warning and detection of landslides will be deployed at one or two sites in consultation with Maryland DOT/SHA by the end of the project.
• 1 training workshop for SHA staff and other professionals as part of workforce development in transportation safety and disaster management, and
• Presentations in webinar series related to Adoption of Cyberinfrastructure and Research Workforce Development for Disaster Management to promote UTC program.
Upon the completion of the whole project, the following deliverables are expected:
• Maps of landslides risks and make them accessible through DOT SHA or UTC;
• Curricular materials (methodology, case studies of landslides, monitoring protocols and more) for classroom teaching for slope stability analysis and risk assessment of landslides and improvement of transportation safety for different transportation modes.
• Protocols for monitoring of slope movement and failure as well as warning systems, and
• Guidelines for mapping risk areas and the real-time monitoring and alarm system for high-risk areas in cooperation with DOT SHA.
TRID
We have made the following searches on TRID: More search ….
1. Landslides Maryland (6 records including our UTC project)
2. Landslides on Railroad (98 records)
3. Landslides Warning System (38 records)
4. Landslides Risk Assessment (244 records)
5. Landslides (2872 records)
This project is unique by integrating geotechnical and machine learning approaches in assessing slope instability and risk of landslides. This project is built upon ongoing MDOT efforts in geotechnical asset management and the proposed work will complement the research project funded by MDOT SHA, focusing on effects of precipitation on slope failure as one of major triggering factors. MDOT SHA is supportive of our proposal and SHA funding has been used as non-federal matching funds.
Information related to the ongoing/recently completed projects supported by MDOT SHA can be found from the following web link:
1. Incorporating Precipitation Data into Geotechnical Asset Management. https://rip.trb.org/view/2118359 (ongoing)
MSU PI: Sheng; $150K (Collaboration with Dr. Sean Qian, CMU)
2. Develop a Mode Choice Model to Estimate Walk and Bike Trips in the Statewide Model (completed by 12/31/2024)
MSU PI: Liu; $300K (Collaboration with Dr. Sean Qian, CMU)
3. Effectiveness of Short Solid Barriers to Reduce Noise Generated by Different Types of Highway Vehicle (completed by 12/31/2024) PI: Owolabi; $150K.
Individuals Involved
| Email |
Name |
Affiliation |
Role |
Position |
| yi.liu@morgan.edu |
Liu, Yi |
Morgan State University |
Co-PI |
Faculty - Tenured |
| Kofi.nyarko@morgan.edu |
Nyarko, Kofi |
Morgan State University |
Other |
Faculty - Tenured |
| Oludare.Owolabi@morgan.edu |
Owolabi, Oludare |
Morgan Statee University |
Other |
Faculty - Tenured |
| zhuping.sheng@morgan.edu |
Sheng, Zhuping |
Morgan State University |
PI |
Faculty - Tenured |
Budget
Amount of UTC Funds Awarded
$292680.00
Total Project Budget (from all funding sources)
$585360.00
Documents
Match Sources
No match sources!
Partners
| Name |
Type |
| Maryland DOT/SHA |
Deployment Partner_ Deployment Partner_ |
