<rss version="2.0" xmlns:atom="https://www.w3.org/2005/Atom">
  <channel>
    <title>Research in Progress (RIP)</title>
    <link>https://rip.trb.org/</link>
    <atom:link href="https://rip.trb.org/Record/RSS?s=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" rel="self" type="application/rss+xml" />
    <description></description>
    <language>en-us</language>
    <copyright>Copyright © 2026. National Academy of Sciences. All rights reserved.</copyright>
    <docs>http://blogs.law.harvard.edu/tech/rss</docs>
    <managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor>
    <webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster>
    <image>
      <title>Research in Progress (RIP)</title>
      <url>https://rip.trb.org/Images/PageHeader-wTitle-RIP.jpg</url>
      <link>https://rip.trb.org/</link>
    </image>
    <item>
      <title>UTC Safety21: Improve Transportation Infrastructure (Geotechnical Asset) Safety by Reducing the Risks of Landslides - Phase 3</title>
      <link>https://rip.trb.org/View/2778223</link>
      <description><![CDATA[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 Artificial Intelligence (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 (MDOT) 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.   

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. The team 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>
      <pubDate>Thu, 17 Sep 2026 14:52:56 GMT</pubDate>
      <guid>https://rip.trb.org/View/2778223</guid>
    </item>
    <item>
      <title>Risk and Resilience Geospatial Data Accessibility Tool</title>
      <link>https://rip.trb.org/View/2768414</link>
      <description><![CDATA[Researchers previously developed a data-driven geographic information system (GIS)-based framework that Kentucky Transportation Cabinet (KYTC) staff can use to quantify risk across the roadway network and prioritize resilience improvements. But it remains challenging for users to easily identify risk and resilience data that are relevant to proposed projects or corridors. The existing framework also needs to be updated using information on environmental stressors (e.g., flooding, landslides, rockfalls, seismic activity) derived from previous and ongoing research. One solution to this challenge is developing a geospatial tool that facilitates risk and resiliency evaluations.]]></description>
      <pubDate>Wed, 26 Aug 2026 17:04:23 GMT</pubDate>
      <guid>https://rip.trb.org/View/2768414</guid>
    </item>
    <item>
      <title>Linking Landslide Triggering and Runout Hazard with Surface Deformations for Optimized Infrastructure Systems Resiliency</title>
      <link>https://rip.trb.org/View/2726550</link>
      <description><![CDATA[Landslides are one of the most significant geohazards impacting North Carolina's transportation network, causing fatalities, property loss, and long-term economic disruption. These events are frequently triggered by extreme precipitation from hurricanes and tropical storms, which have historically produced hundreds to thousands of debris during a single event. For example, Hurricane Helene (2024) triggered more than 2,000 reported landslides across the Southern Appalachians, resulting in widespread road closures, bridge damage, and tens of billions of dollars in direct and indirect losses. As the frequency and intensity of extreme precipitation events increase, the risk of cascading infrastructure failures is expected to grow. Current North Carolina Department of Transportation (NCDOT) Geotechnical Asset Management (GAM) tools primarily operate reactively— tracking known unstable sites and coordinating post-disaster repairs. Therefore, there is a critical need for proactive capabilities to anticipate landslide hazards before they disrupt the network.

The objective of this project is to create a robust, scalable, and computationally efficient framework to predict landslide triggering and runout at a regional scale, supporting optimized maintenance, emergency response, and risk-informed investment decisions. This work will integrate the North Carolina Geological Survey (NCGS) Post-Helene Landslide Inventory, surface deformation mapping, and AI enhanced triggering predictions. The research will pursue four main objectives: (1) consolidate and curate a high-quality georeferenced dataset of landslide and debris flow events in North Carolina; (2) develop machine-learning models informed by physics to predict triggering susceptibility based on rainfall thresholds, slope geometry, and hydrologic conditions; (3) link surface deformation signals to slope stability through finite-element-based surrogate models; and (4) compute landslide runout using depth-averaged Material Point Method (DA-MPM) simulations that account for three-dimensional topographic effects and infrastructure exposure.

The approach follows a hierarchical and computationally efficient workflow. Regional-scale data-driven models will rapidly screen the entire state for slopes with high triggering potential. For these critical sites, limit equilibrium analysis (LEA) using existing NCGS models will identify likely failure surfaces and factors of safety. The outputs will serve as inputs to physics-based DA-MPM simulations that predict debris flow runout, impact zones, and potential consequences for NCDOT-managed assets. This strategy maximizes coverage while focusing on high-fidelity simulations where they are most needed, thereby balancing predictive power with computational cost.

The anticipated products include trained machine-learning models, enhanced infinite-slope analysis incorporating AI training, a verified and validated DA-MPM module, and geographic information system (GIS)-integrated hazard/risk maps. Integration into NCDOT's existing GAM system will enable decision-makers to: (i) develop watchlists of critical slopes, (ii) anticipate maintenance and debris removal needs, (iii) coordinate detour planning and emergency response, and (iv) communicate risk more transparently to stakeholders. Training workshops will be held with NCDOT and NCGS engineers and geologists to ensure usability and gather feedback for future system enhancements.

This project represents the first step toward a real-time, data- and physics-informed landslide early warning and infrastructure risk management system. By combining machine learning, geotechnical modeling, and large-deformation simulation, this work will strengthen North Carolina's landslide risk assessment and improve transportation resiliency, reduce lifecycle maintenance costs, and protect the safety and mobility of the traveling public.]]></description>
      <pubDate>Thu, 09 Jul 2026 09:02:15 GMT</pubDate>
      <guid>https://rip.trb.org/View/2726550</guid>
    </item>
    <item>
      <title>Phase 2: Leveraging Surface Monitoring to Guide Emergency Response and Long-Term Strategic Planning</title>
      <link>https://rip.trb.org/View/2725299</link>
      <description><![CDATA[Conventional monitoring, site investigation, and assessment of landslides for mitigation often requires drilling along with installation of piezometers and inclinometers which is not always feasible due to the significant expense of drilling, short lifespan of subsurface instruments, safety concerns about working on an active landslide, and difficult site access. A continued need exists to expand the capabilities of near-real-time surface monitoring of ground movements for (1) existing, monitored landslides to gather longer time-series of landslide response to variable wet seasons, and (2) newly-monitored landslides that reflect uncharacterized climatic and geologic conditions to extrapolate a spectrum of landslide impacts to Oregon Department of Transportation (ODOT) right-of-way, and (3) landslide events that occur and require quantitative information for decision-making. Such data is key for evaluating highway safety, repair and mitigation needs, and strategies for reopening after failure. The absence of this information places critical infrastructure and ultimately ODOT customers at risk.

The data from this expanded monitoring would inform important ODOT planning activities and research questions. For planning purposes, this data has already demonstrated benefits for emergency response to landslide events, planning around mitigation plans and Goal 18 discussions, maintenance considerations, and reduced ODOT time for inspection of landslides. Expanding these efforts would further support ODOT’s ability to monitor problematic slopes and make informed decisions regarding prioritization, planning, and sustaining mobility.]]></description>
      <pubDate>Wed, 08 Jul 2026 16:07:38 GMT</pubDate>
      <guid>https://rip.trb.org/View/2725299</guid>
    </item>
    <item>
      <title>Proactive Strategy For Climate Resilient Corridors: Landslide Hotspot Prioritization, Planning, And Management Tool</title>
      <link>https://rip.trb.org/View/2594026</link>
      <description><![CDATA[Landslides are frequent hazards that affect Oregon highway infrastructure, resulting in negative economic, environmental, and social impacts for Oregon communities. Goals from the Oregon Transportation Plan (OTP) include proactive preparation of lifeline routes to reduce possible hazards before events occur, starting with the strategy to map and assess multi-hazard threats to the transportation system. While Oregon Department of Transportation's (ODOT's) Climate Adaptation and Resilience Roadmap and Climate Hazard Risk Map identify resilience corridors to help prioritize investment, a higher resolution analysis to prioritize between sites along these corridors is needed for development of compelling business cases for investment and competitive funding opportunities. ODOT is already under financial strain reacting to landslide hazards as they happen. Given that these hazards are projected to increase in frequency/magnitude with climate change, reactive approaches for returning to site functionality will exacerbate ODOT’s fiscally constrained reality. High resolution landslide hazard site vulnerability analysis followed by site prioritization will provide planning and management teams practical science-based investment strategies aimed at improving safety, ensuring working emergency lifeline routes, preventing community isolation, and reducing rising maintenance costs for hazard removal.]]></description>
      <pubDate>Thu, 28 Aug 2025 15:56:56 GMT</pubDate>
      <guid>https://rip.trb.org/View/2594026</guid>
    </item>
    <item>
      <title>Real-time Surface Monitoring for Improved Safety, Response, And Repair</title>
      <link>https://rip.trb.org/View/2593995</link>
      <description><![CDATA[The recent, rapid, and overwhelming movement of the Hooskanaden landslide in 2019 and the Arizona Inn landslide in early 2023 greatly disrupted traffic along US 101 with several days of full road closure followed by prolonged reduced capacity and weeks of traffic control for repairs. Alarmingly, numerous precarious landslides exist throughout the state that can result in similar consequences, triggered by precipitation or erosion—both of which will be exacerbated with climate change. Real-time, on-site instrumentation is essential to characterize landslide kinematics as well as detect and predict movements that can disrupt the highway system. Real-time, on-site instrumentation can also help inform the timing of repair and estimation of material needs, improving both on-site safety and maintenance costs associated with repair. However, the standard methodology for landslide instrumentation requires costly drilling beneath the earth’s surface—which is frequently unsafe, costly, and infeasible on an active landslide. Further, drilled subsurface instrumentation is oftentimes destroyed with landslide movement, providing only short-term usefulness for obtaining active landslide data. This project will develop and deploy low-cost surface monitoring strategies to monitor landslide movements, leveraging Oregon Department of Transportation's (ODOT's) recent proof-of-concept success using real-time kinematic global navigation satellite systems (RTK-GNSS) to monitor the Arizona Inn landslide failure, which enabled real-time delivery of critical information to help inform closure actions and repairs. Further development and establishment of surface monitoring methods will also inform statewide characterization of active slides that impact ODOT infrastructure where drilling is cost prohibitive, unsafe, or impossible.]]></description>
      <pubDate>Thu, 28 Aug 2025 15:09:20 GMT</pubDate>
      <guid>https://rip.trb.org/View/2593995</guid>
    </item>
    <item>
      <title>Predicting Near Real-time Post-fire Landslide Debris Flows along ODOT Corridors</title>
      <link>https://rip.trb.org/View/2589109</link>
      <description><![CDATA[The main objective of this research is to develop tools that can be used to actively assess and address the potential for debris flows along Oregon Department of Transportation (ODOT) corridors in near-real time. This research will utilize landslide susceptibility modeling and ground surface monitoring research from SPR807, SPR808, as well as ODOT’s 2019 Surface Monitoring STIC grant project which successfully developed a field validated surface monitoring station prototype for active landslides.]]></description>
      <pubDate>Fri, 15 Aug 2025 15:23:33 GMT</pubDate>
      <guid>https://rip.trb.org/View/2589109</guid>
    </item>
    <item>
      <title>Cenozoic fracture systems in western North Carolina and their contribution to large, slow-moving, deep-seated landslides</title>
      <link>https://rip.trb.org/View/2563763</link>
      <description><![CDATA[Recent studies have shown that multiple fracture systems occur across western North Carolina that are associated with linear topographic lows, such as the Swannanoa Lineament that contains the I-40 corridor from Swannanoa through Asheville. Earthquakes have occurred on several of these lineaments suggesting they are seismically active. Multiple large, slow-moving landslides have been identified within these lineaments. This study will test the hypothesis that bedrock fractures within Cenozoic aged lineaments across western North Carolina are planes of weaknesses that contribute to these large, slow-moving landslides. This study will combine geologic mapping in the field and from high-resolution LiDAR data with kinematic slope stability analyses and topographic studies. This work will (1) constrain the bedrock structures that act as failure planes, (2) relate these structures to the lineament fracture systems, and (3) characterize the topographic evolution that resulted from these fracture systems that may influence the stability of slopes. Understanding the dynamics of these large, slow-moving landslides will enable the 
North Carolina Department of Transportation (NCDOT) to develop efficient practices to mitigate damage to infrastructure.]]></description>
      <pubDate>Fri, 13 Jun 2025 12:08:47 GMT</pubDate>
      <guid>https://rip.trb.org/View/2563763</guid>
    </item>
    <item>
      <title>Feasibility of InSAR for Continuous Monitoring of Ground Deformation and Performance Tracking of Geotechnical Assets</title>
      <link>https://rip.trb.org/View/2487308</link>
      <description><![CDATA[In Minnesota, ground movements (e.g., landslides, land subsidence, etc.) have led to significant damage and disruption to the state’s highway network. These geohazards can lead to lane closures, traffic delays, and emergency repairs. The objective of this research is to develop an automated warning system that can alert Minnesota Department o Transportation (MnDOT) staff of areas where abnormal ground deformation (e.g., landslides, subsidence, and sinkholes) is occurring along Minnesota interstate highways, allowing them to proactively intervene. While predicting these types of events beforehand is difficult, continuous, and accurate monitoring of ground deformation along roads is crucial to identify higher risk areas before they develop into major failures.]]></description>
      <pubDate>Mon, 19 May 2025 11:57:57 GMT</pubDate>
      <guid>https://rip.trb.org/View/2487308</guid>
    </item>
    <item>
      <title>Landslides: Tech transfer and commercialization advice </title>
      <link>https://rip.trb.org/View/2440032</link>
      <description><![CDATA[This sub-project is to support the main project "Improve highway safety by reducing the risks of landslides" with Zhuping Sheng from Morgan State University as the principal investigator (PI). The support will consist of: (1) Transfer the PI's knowledge of landslide detection and analysis in images to the main group. The PI has researched landslides in the past, especially by developing computer vision tools to analyze images of actual or potential landslides. Depending on the need of the main group, the PI will advice which tools are useful and teach individual members on how to use them. (2) Explore deployment and commercialization possibilities. 
The PI has experience in deploying research results, from running systems on robots or buses, open-sourcing code and data sets to starting a company (RoadBotics, recently acquired by Michelin). The PI will help the main group to explore deployment commercialization possibilities. One possibility to explore is to partner with Michelin (PI is part time employee of Michelin). (3) Explore follow-on research or development at 
Carnegie Mellon University (CMU) depending on results and needs. The findings of the main project might necessitate a larger effort to refine existing or develop new computer vision tools. The PI can organize and supervise student projects of various sizes, e.g. RISS summer students or MSCV capstone projects. 
]]></description>
      <pubDate>Sun, 13 Oct 2024 11:31:40 GMT</pubDate>
      <guid>https://rip.trb.org/View/2440032</guid>
    </item>
    <item>
      <title>Improve highway safety by reducing the risks of landslides (Phase 2).</title>
      <link>https://rip.trb.org/View/2440012</link>
      <description><![CDATA[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.
]]></description>
      <pubDate>Sat, 12 Oct 2024 12:09:51 GMT</pubDate>
      <guid>https://rip.trb.org/View/2440012</guid>
    </item>
    <item>
      <title>Landslide Hazard Identification and Monitoring</title>
      <link>https://rip.trb.org/View/2440046</link>
      <description><![CDATA[Debris flows, sometimes referred to as mudslides, mudflows, or debris avalanche, are fast-moving landslides that can carry large items such as boulders, trees, and cars and can destroy objects in their paths. Typically, debris flows occur during periods of intense rainfall or rapid snowmelt, and usually start on hillsides or mountains. Considerable roadway damage occurred in July 2023 VT rains causing from uphill landslide and debris flows spilling onto roadways. This was a new phenomenon at this magnitude for Vermont Agency of Transportation (VTrans) which required them to engage outside expertise to understand long- and short-term risk, develop standard details for rapid repairs, and in some cases, case specific recommendations.

There is safety and cost-saving value in identifying the future hazard potential and to develop strategies for preparing for, warning of, and responding to the debris flow hazard. Identifying higher risk areas and monitoring of these areas will facilitate asset management, improve roadway safety, reduce the need for emergency road closures, and reduce long- term costs associated with repeated emergency responses.
]]></description>
      <pubDate>Thu, 10 Oct 2024 14:13:29 GMT</pubDate>
      <guid>https://rip.trb.org/View/2440046</guid>
    </item>
    <item>
      <title>Improve highway safety by reducing the risks of landslides</title>
      <link>https://rip.trb.org/View/2292645</link>
      <description><![CDATA[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 (MSU) 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 (ML) methods for assessing conditions of geotechnical assets, such as cut slopes and embankment of the Maryland Department of Transportation State Highway Administration (MDOT 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, 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 and other factors like transportation modes, (3) design and test protocols for real time monitoring at selected sites in consultation with MDOT 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. This project will primarily complement ongoing projects sponsored by the MDOT SHA (see more information in TRID) led by Zhuping Sheng. Dr. Jiang Li has experience in both transportation research and environmental hazards. The former focuses on the mechanical behavior of road subgrades and the latter addresses the geological or hydrological hazards that may adversely affect the regional transportation infrastructure and traffic safety.  As principal investigator (PI) Dr. Li 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, Zhuping Sheng, Oludare Owolabi and Yi Liu who 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 in cooperation with MSU AI/ML program led by Dr. Owolabi. Students will also participate in exchange programs and deployment partners symposium and other activities. Through this project the MSU team is expected to expand collaboration with Carnegie Mellon University (CMU) and other partner institutes via faculty meetings, seminars, national summit, and other venues, which provides great opportunity for professional development.]]></description>
      <pubDate>Mon, 20 Nov 2023 20:00:16 GMT</pubDate>
      <guid>https://rip.trb.org/View/2292645</guid>
    </item>
    <item>
      <title>Green Landslide Repair Using Deep Rooted Vetiver Grass for MDOT</title>
      <link>https://rip.trb.org/View/2264430</link>
      <description><![CDATA[The proposed study will evaluation the performance of Vetiver stabilized slopes in different locations of Mississippi and Develop a Proactive Landslide Repair Protocol using Vetiver

The specific objective of the proposed study is to:
(1) Evaluate the overall performance of highway slopes in Mississippi stabilized with Vetiver Grass.
(2) Develop a simple Design and direct implementation technique for MDOT Districts.
(3) Develop Specification for Field Implementation of Vetiver. 
(4) Train MDOT Material Division (Geotechnical Section) and District Engineers on the Slope Stabilization Technique using Vetiver.]]></description>
      <pubDate>Mon, 09 Oct 2023 09:27:29 GMT</pubDate>
      <guid>https://rip.trb.org/View/2264430</guid>
    </item>
    <item>
      <title>Distributed strain sensing for pipeline safety against fault moving and landslide</title>
      <link>https://rip.trb.org/View/2085763</link>
      <description><![CDATA[The research objective is to develop a robust distributed fiber optic strain sensing system for long-term monitoring structural performance of pipeline subjected ground movements at fault crossing and landslide sites.]]></description>
      <pubDate>Fri, 16 Dec 2022 14:15:54 GMT</pubDate>
      <guid>https://rip.trb.org/View/2085763</guid>
    </item>
  </channel>
</rss>