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 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 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, (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 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.
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.
Description
Timeline
Strategic Description / RD&T
The proposed project will address transportation safety, especially physical infrastructure systems and roadway design, covering the following US DOT goals:
• Update roadway design standards to protect vulnerable road users and vehicle occupants.
• Use regulatory and policy tools to advance roadway safety to reduce fatalities and injuries across modes.
• Support the adoption and maturation of safety management systems across modes.
• Use data and data analytics to take proactive actions to address emerging safety risks and support compliance.
The project will provide technical assistance to better identify, assess, and address critical physical vulnerabilities.
• Incorporate physical protection in the standards for design of emerging automated and connected systems and technologies, such as real time sensing and monitoring systems.
• Strengthen system response and recovery plans and protocols to minimize the effects of the system disruptions and hasten system recovery from natural disasters.
• Promote guidelines on vulnerability assessments with enhancement of AI/ML approaches.
The project will assess and mitigate the vulnerability of transportation infrastructure to climate change and natural disasters:
• Assess the vulnerability of assets and identify novel climate adaptation and mitigation strategies.
• Enhance resilience throughout transportation planning and project development processes by
updating guidance and regulations.
• Conduct case studies and pilot projects to develop and evaluate new and innovative adaptation and resiliency technologies, tools, and opportunities, such as image capturing systems, motion sensors and early warning systems.
This project will build research capacity in the critical area of designing resilient infrastructure for geohazards and changing climate conditions. It will also provide educational opportunities for graduate and undergraduate students to gain knowledge and experience in this important new area for sustainable and resilient engineering. Thus, the project will also build human capacity to address the challenge of geohazards adaptation related to transportation systems.
Deployment Plan
Quarter 1 (July – September)
• Share progress report with MDOT SHA based on ongoing SHA project(s) and work supported by UTC Safety 21 - Phase 1.
• 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 2.
• Initiate technology transfer with CMU partner on photo imaging processing and interpretation.
Quarter 2 (October- December)
• Share UTC work at the Quarterly meeting in October and seek inputs from the partner.
• Develop plan for integration of SHA project work and deployment of SHA products with the ongoing UTC planned work.
• Participate in Deployment Partners Symposium and explore opportunities for collaboration and partnership.
Quarter 3 (January - March)
• Extend SHA work as part of UTC work with SHA collaboration and continue to share UTC work with SHA.
• Technology transfer and collaboration with CMU.
• Plan and prepare for a workshop to be held next quarter.
Quarter 4 (April - June)
• Organize the workshop for MDOT SHA staff and other professionals on MSU campus.
• Organize internship activities in collaboration with MSU AI/ML program - National Center for Equitable Artificial Intelligence and Machine Learning Systems (CEAMLS)
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. An extensive public involvement program will be undertaken to make sure that all the stakeholders including students at lower levels of education are aware of this project.
Expected Outcomes/Impacts
Besides advance in research and teaching related to transportation 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.
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.
Research findings will help policy makers to improve the roadway design standards and secure resources to assure long-term safety of transportation systems under climate change.
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 for under-represented students of color and provide educational opportunities for student assistants to gain knowledge and experience for civil and environmental engineering. Faculty and researchers will participate in the Black Engineer of the Year Awards STEM Conference, 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 2 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
• Enhanced GIS coverages of detail delineation of selected landslides
• Enhanced database of geotechnical properties of soils
• 1 training workshop for SHA staff and other professionals in transportation safety and disaster management,
• Presentations in webinar series related to Adoption of Cyberinfrastructure and Research Workforce Development for Disaster Management to promote UTC program,
• AI/ML models for landslides risk assessment with physical models (modified or new)
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
For research projects- conduct a search on similar projects and based on the results describe how this project is unique, builds on existing research and/or how collaborative opportunities may exist with existing projects. (200 words & attach a copy to the search results) Note: TRiD is an integrated database that combines the records from TRB's Transportation Research Information Services (TRIS) database and the OECD's Joint Transport Research Centre's International Transport Research Documentation (ITRD) Database. TRID provides access to 1.4 million records of transportation research worldwide. https://trid.trb.org
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We have made the following searches on TRID: More search ….
1. Landslides Maryland (5 records including our UTC project)
2. Landslides Warning System (38 records)
3. Landslides Risk Assessment (220 records)
4. Landslides (2814 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 has confirmed that SHA funding can be used as non-federal matching funds.
Information related to the ongoing 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
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
MSU PI: Liu; $150K (Collaboration with Dr. Sean Qian, CMU)
3. Effectiveness of Short Solid Barriers to Reduce Noise Generated by Different Types of Highway Vehicle (PI: Owolabi; $150K)
Individuals Involved
| Email |
Name |
Affiliation |
Role |
Position |
| yi.liu@morgan.edu |
Liu, Yi |
Morgan State University |
Co-PI |
Faculty - Tenured |
| Oludare.Owolabi@morgan.edu |
Owolabi, Oludare |
Morgan Statee University |
Co-PI |
Faculty - Tenured |
| zhuping.sheng@morgan.edu |
Sheng, Zhuping |
Morgan State Unievrsity |
PI |
Faculty - Tenured |
Budget
Amount of UTC Funds Awarded
$
Total Project Budget (from all funding sources)
$600000.00
Documents
Match Sources
No match sources!
Partners
| Name |
Type |
| Maryland Department of Transportation |
Deployment & Equity Partner Deployment & Equity Partner |
| Carnegie Mellon University |
Deployment Partner Deployment Partner |
