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    <title>Research in Progress (RIP)</title>
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    <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>
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      <title>Research in Progress (RIP)</title>
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      <link>https://rip.trb.org/</link>
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    <item>
      <title>Reassessing the Subsurface Conditions and the Durability of Buried Foundation Elements at a Texas National Geotechnical Experimentation Site</title>
      <link>https://rip.trb.org/View/2780471</link>
      <description><![CDATA[A research site at Texas A&M University (TAMU) was one of six National Geotechnical Experimentation Sites (NGESs) established across the United States for geotechnical experiments. The NGESs were initially funded in 1992, through a partnership between the National Science Foundation and the Federal Highway Administration, to establish sites with extensive characterization data so research could focus on advancing state-of-the-art in situ testing, field instrumentation, foundation testing, and behavior predictions of soil and geotechnical elements. Many of the NGESs have since been decommissioned with TAMU being one of the last remaining. Before this site becomes decommissioned, an opportunity exists to reassess and complete geotechnical experiments.

OBJECTIVES: The objectives of this project are to quantify temporal changes in soil properties, assess advancements in geotechnical site characterization analysis, evaluate soil aggressiveness and the durability of buried concrete and steel, assess available prediction methods for corrosion and remaining strength given the soil in which the foundation elements are buried, and to produce a relational database of test results and measurements.]]></description>
      <pubDate>Wed, 23 Sep 2026 18:20:03 GMT</pubDate>
      <guid>https://rip.trb.org/View/2780471</guid>
    </item>
    <item>
      <title>Advancing Pavement Design Inputs Through Improved R-Value Predictions</title>
      <link>https://rip.trb.org/View/2780407</link>
      <description><![CDATA[The objective of this research is to develop improved R-value prediction equations that enhance accuracy while remaining practical for Arizona Department of Transportation (ADOT) pavement design use. To achieve this objective, the project will: 
(1) Collect and compile available ADOT soil and R-value datasets from different Arizona regions, including measured R-values, soil classification, index properties, gradation data, project location, material source, and other routinely available laboratory or field information. (2) Perform regression and statistical analyses to evaluate the accuracy of the current ADOT R-value correlation and identify soil parameters that can improve prediction performance for Arizona materials. (3) Develop and validate enhanced R-value prediction equations using practical input parameters such as PI, percent passing the No. 200 sieve, gradation indices, USCS classification, and regional soil information where appropriate.
(4) Provide implementation recommendations for ADOT, including recommended equations, input requirements, limitations, example calculations, and a simple calculation tool for use by Pavement Design and Geotechnical staff.]]></description>
      <pubDate>Fri, 18 Sep 2026 17:07:19 GMT</pubDate>
      <guid>https://rip.trb.org/View/2780407</guid>
    </item>
    <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>Advancing Slope Stability in Roadway Embankments: The Role of Innovative Geosynthetics and Numerical Modeling Under Dynamic Traffic Load and Extreme Rainfall Conditions</title>
      <link>https://rip.trb.org/View/2691527</link>
      <description><![CDATA[Slope stability in roadway embankments remains a critical concern in geotechnical engineering. The slopes are often mechanically stabilized by using conventional geosynthetics such as geotextiles, geogrids, and geomembranes for separation, reinforcement, filtration, drainage, and erosion control under static conditions. However, evolving infrastructure demands and increasing exposure to extreme weather and dynamic loading necessitate the adoption of advanced geosynthetics. New materials such as wicking geotextiles, which combine mechanical reinforcement with moisture regulation via capillary action, and geocomposites, which integrate multiple geosynthetic components for enhanced drainage and barrier performance, offer promising solutions. Numerical modeling techniques, including finite element and finite difference methods, are essential for evaluating the behavior of these materials under various environmental loading scenarios, capturing critical parameters such as rainfall infiltration, matric suction variations, and pore water pressure fluctuations. Furthermore, modeling under dynamic traffic loading enables the assessment of cyclic stress response, shear deformation, and stress redistribution in reinforced embankment systems. Despite these advances, research remains limited regarding the coupled effects of environmental and traffic-induced loading on advanced geosynthetic systems. This study proposes a comprehensive numerical modeling framework for advancing slope stability in roadway embankments by incorporating comprehensive laboratory test results, with a focus on evaluating the performance of advanced geosynthetics under combined environmental and traffic loading conditions. The outcomes are expected to support the development of resilient and sustainable transportation infrastructure systems. ]]></description>
      <pubDate>Thu, 10 Sep 2026 08:54:26 GMT</pubDate>
      <guid>https://rip.trb.org/View/2691527</guid>
    </item>
    <item>
      <title>Developing TxDOT Design Guidelines for Riprap Rock Size and Apron Limits at Culverts</title>
      <link>https://rip.trb.org/View/2768430</link>
      <description><![CDATA[Current design practices for bridge-class culverts often use bridge-centric or outlet-focused rip-rap rock size design equations, which do not adequately address inlet contraction scour, multiple culvert types, velocity amplification in wide structures, and varying soil erodibility. This project will develop defensible, culvert-specific rock sizing equations and apron design criteria tailored to varying culvert types (with-bottom and bottomless) and soil conditions. Researchers will: (1) perform literature review, survey, and site visits to synthesize design practice, identify research gaps, inspect Texas Department of Transportation's (TxDOT’s) data, and identify typical failure modes and vulnerable locations; (2) establish culvert-specific rock sizing equations for both with-bottom and bottomless culverts, with calibrated amplification factors for inlet and outlet culvert hydraulics and for both cohesive and non-cohesive soil conditions; (3) develop apron limits design criteria, including layout rules and quick-reference guidelines, that explicitly incorporate soil erodibility categories and address multiple vulnerable inlet and outlet transitions; and (4) produce ready-to-use design aids (charts, worksheets, and design examples), implementation recommendations, and quality assurance/quality control (QA/QC) checklists compatible with existing specifications and scour analysis procedures tailored for designers and maintenance personnel. These outcomes will provide TxDOT with scientifically robust design procedures compatible with the Specifications for the National Bridge Inventory (SNBI), enhancing infrastructure resilience and optimizing construction and maintenance expenditures.]]></description>
      <pubDate>Fri, 28 Aug 2026 10:02:11 GMT</pubDate>
      <guid>https://rip.trb.org/View/2768430</guid>
    </item>
    <item>
      <title>Bridge Approach Roadway Embankment Improvements</title>
      <link>https://rip.trb.org/View/2762034</link>
      <description><![CDATA[Bridge approach settlements, or bridge bumps, are a persistent challenge to Department of Transportations (DOTs), including Minnesota Department of Transportation (MnDOT), affecting ride quality, safety, and maintenance costs. These bumps result from differential settlement between the embankment and abutment, driven by soil settlement, erosion, inadequate drainage, poor compaction, freeze-thaw cycles, and traffic loads. 

MnDOT currently uses sandy backfills, and the cost of repairing settlements at the approaches are considerable. MnDOT is therefore exploring new ways of designing approach slabs. Possible mitigation methods include the use of Elasticized Expanded Polystyrene (EPS) foam as lightweight fill, Geosynthetic Stabilized Bridge Approaches (GSBA) developed by the Pennsylvania Department of Transportation (PennDOT), and cemented fills in embankments. The relative performance of these methods is unclear, and their suitability for Minnesota’s geotechnical and climatic conditions remains uncertain.


This research project will provide a comprehensive evaluation of these solutions for Minnesota bridges with the goal of reducing bridge approach bumps.]]></description>
      <pubDate>Tue, 18 Aug 2026 17:15:32 GMT</pubDate>
      <guid>https://rip.trb.org/View/2762034</guid>
    </item>
    <item>
      <title>Advancing Geotechnical Site Investigation with Automated 3D SPT-Seismic Testing</title>
      <link>https://rip.trb.org/View/2762014</link>
      <description><![CDATA[Effective geotechnical site characterization is essential for the planning, design, and long-term performance of transportation infrastructure. Unanticipated subsurface conditions (such as buried voids, weak soils, or highly variable stratigraphy) can lead to costly construction delays, change orders, and, in severe cases, structural distress or failure. For State DOTs responsible for delivering safe and resilient infrastructure, obtaining reliable information on subsurface conditions and their spatial variability is therefore critical during the early stages of project development.

Current practice relies heavily on invasive point-based methods such as Standard Penetration Test (SPT), Cone Penetration Test (CPT), and rock coring. While these methods provide high-quality measurements, they sample only a very small volume of material within individual boreholes. As a result, large portions of the subsurface remain uncharacterized, and critical features (such as voids, soft zones, or irregular bedrock surfaces) remain undetected between boreholes. Surface-based geophysical methods can provide broader spatial coverage; however, their resolution typically decreases rapidly with depth due to signal attenuation, limiting their effectiveness for deeper subsurface imaging.

Recent advancements in seismic testing have led to the development of the 3D SPT-seismic method, an innovative approach that integrates seismic wave analysis with conventional SPT operations to enable volumetric imaging around boreholes. The method records seismic waves generated by routine SPT hammer blows at depth using a 2D geophone array deployed at the ground surface. These waveforms are then analyzed using advanced 3D full-waveform inversion techniques to reconstruct a high-resolution three-dimensional shear-wave velocity (Vs) model surrounding the borehole. Field demonstrations at sites in Florida, Kansas, and Minnesota show that the method can produce subsurface images with approximately 2-ft spatial resolution across a large 3D domain extending up to 60 ft from the borehole at any tested depth (see additional document), with Vs profiles showing strong agreement with SPT-N values.

Despite its demonstrated potential, the current implementation still requires substantial manual data processing, limiting its routine use in practice. This project aims to advance the 3D SPT-seismic method by developing automated processing workflows and practical implementation guidelines that will enable state DOTs to efficiently generate high-resolution 3D subsurface images with routine SPT site investigations. The resulting capability will help reduce subsurface uncertainty, improve risk management, and support more informed design and construction decisions for transportation projects.

The primary objectives of this research are: (1) to automate the 3D SPT-seismic method and (2) to validate its applicability across a range of geological conditions commonly encountered by State DOTs to support widespread implementation. The project will focus on developing an integrated workflow that streamlines both data acquisition and analysis, enabling the method to be deployed efficiently alongside routine geotechnical investigations without disrupting standard SPT operations.

For automation, seismic signals generated by SPT hammer blows will be continuously recorded using a surface geophone array for all blows without interfering with the drilling crew. Advanced machine-learning algorithms will be implemented to automate key data-processing tasks, including signal conditioning, noise suppression, event detection, and removal of low-quality records. The processed data will be then analyzed via advanced 3D full-waveform inversion (FWI) to reconstruct a high-resolution 3D shear-wave velocity (Vs) image around the borehole, extending up to approximately 60 ft into the surrounding subsurface.

For validation, the methodology will be demonstrated through field testing at State DOT sites, with two test locations for each participating agency, representing a variety of soil and geological conditions. The results will be compared with SPT-N values to assess accuracy and practical benefits. By automating the analysis procedures and validating the method under real field conditions, this project will help transform the SPT-seismic approach into a practical and cost-effective tool for high-resolution geotechnical site characterization.]]></description>
      <pubDate>Tue, 18 Aug 2026 16:26:47 GMT</pubDate>
      <guid>https://rip.trb.org/View/2762014</guid>
    </item>
    <item>
      <title>Physics Informed Neural Network (PINN) enabled Predictive Resilience Framework for Maritime and Multimodal Levee Infrastructure</title>
      <link>https://rip.trb.org/View/2732360</link>
      <description><![CDATA[The performance and resilience of levee systems are governed by complex
hydro-mechanical interactions influenced by transient seepage, soil stratification, and environmental loading.
Conventional monitoring approaches, while effective in capturing field conditions, lack predictive capability and
often fail to integrate subsurface characterization with real-time system response. This study proposes a Physics-Informed Neural Network (PINN) enabled predictive resilience framework for maritime and multimodal levee
infrastructure, integrating multi-source sensing, geophysical imaging, and physics-based modeling. The
framework leverages Internet of Things (IoT) based sensor networks, including IMU derived tilt and displacement measurements, and
environmental variables such as rainfall, temperature, and soil moisture. To enhance subsurface characterization,
Electrical Resistivity Imaging (ERI) and Multichannel Analysis of Surface Waves (MASW) are incorporated to
capture spatial variability in moisture distribution, stiffness profiles, and potential seepage zones. These datasets
are fused with UAV based LiDAR point cloud models to develop high-resolution, temporal geospatial conditional
representations of levee geometry and deformation. The integrated dataset is utilized to calibrate finite element
method (FEM) based seepage and stability models, enabling accurate representation of coupled hydromechanical
behavior. The PINN architecture embeds governing equations of transient flow and unsaturated soil
mechanics into the learning process, allowing physically consistent prediction of pore pressure, volumetric
moisture content, and deformation fields. A hybrid physics-guided, data-driven digital twin will be developed to
continuously assimilate field and geophysical data, providing real-time predictions and identifying anomaly
thresholds indicative of instability. The proposed framework advances geotechnical asset management by
enabling predictive failure assessment, risk-informed decision-making, and proactive maintenance strategies,
thereby enhancing the resilience of critical maritime and multimodal infrastructure systems under extreme
environmental conditions.]]></description>
      <pubDate>Tue, 21 Jul 2026 16:41:56 GMT</pubDate>
      <guid>https://rip.trb.org/View/2732360</guid>
    </item>
    <item>
      <title>Improve MDOT's Understanding of the Acceptance and Performance of Riprap</title>
      <link>https://rip.trb.org/View/2731977</link>
      <description><![CDATA[The long term performance of riprap has been an issue because some local sources of riprap have known durability issues
and will degrade/dissolve over time. In addition, the acceptance of riprap size and gradation is currently done by performing a
Wolman count. Performing the Wolman count involves walking over large rocks, which can be a safety hazard and takes a
significant amount of time to do. There are challenges in assessing the performance and durability of riprap in riverine, lightly
acidic and other environments that need to be addressed. The potential exists that there may be technological and electronic
solutions that need to be utilized to enhance or replace existing processes.]]></description>
      <pubDate>Fri, 17 Jul 2026 15:21:06 GMT</pubDate>
      <guid>https://rip.trb.org/View/2731977</guid>
    </item>
    <item>
      <title>Enhanced Understanding of Geotechnical Processes</title>
      <link>https://rip.trb.org/View/2731975</link>
      <description><![CDATA[There is a need for a multidisciplinary hub for advancing geotechnical engineering practices in support of Michigan’s surface
transportation infrastructure. In alignment with the objectives outlined by the Michigan Department of Transportation (MDOT), the
Center will engage in a comprehensive suite of activities, including but not limited to education and workforce development, public
and stakeholder outreach, applied and theoretical research, implementation of innovative technologies, laboratory and field testing,
analytical modeling, and investigative services. These efforts will be guided by MDOT’s strategic priorities and may be further refined
through specific tasks detailed in this Scope of Services. The overarching goal of this center is to function as a responsive and
collaborative resource for MDOT by facilitating in the development, evaluation, and deployment of novel geotechnical solutions that
enhance the safety, durability, and sustainability of Michigan’s transportation systems. This can be done by fostering innovation and
continuous improvement in geotechnical engineering. The Center aims to bridge the gap between research and practice, ensuring
that emerging technologies and methodologies are effectively translated into real-world applications that benefit the traveling public
and support the long-term stewardship of the state’s infrastructure assets.]]></description>
      <pubDate>Fri, 17 Jul 2026 15:11:11 GMT</pubDate>
      <guid>https://rip.trb.org/View/2731975</guid>
    </item>
    <item>
      <title>Designing and Constructing Permanent Stable Foundation Layers in Areas of Sulfate Rich Soils</title>
      <link>https://rip.trb.org/View/2727387</link>
      <description><![CDATA[The presence of a suitable foundation layer plays a significant role in the constructability and long-term performance of pavements. During construction, these layers must provide sufficient support for placement and compaction of subsequent pavement layers, and during service life, these foundation layers play a critical role in the pavement structure by supporting the upper pavement layers and spreading loads to provide long-term pavement performance. When designed correctly, lime stabilized layers have a long history of providing permanent support in areas of plastic soils. However, lime has been removed from recent projects because of concerns over soluble sulfates. The use of select fill and geogrids has not provided projects with the support needed to successfully complete construction, and in some cases even handle construction traffic. These failures cost millions of dollars to fix and result in significant project delays. Adequate and permanent foundation layers are critical to performance of both flexible and rigid pavement structures. The research team will document the effectiveness of current practices for identifying sulfates on construction projects and determine if new or improved technologies exist to more effectively and reliably detect sulfates. The research team will deploy these tools on actual construction projects and document their effectiveness. Using advanced lab testing, the research team will determine treatment alternatives for soils containing sulfates. Based on the findings, the research team will recommend soil treatment or pavement structural design alternatives to provide permanent and stable foundation layers. The findings from this project shall be used to recommend updates to project selection, treatment guidelines, test procedures, specifications, and the Pavement Manual.]]></description>
      <pubDate>Fri, 10 Jul 2026 16:37:15 GMT</pubDate>
      <guid>https://rip.trb.org/View/2727387</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>Empirical Modeling for Improved Ground Failure Analysis</title>
      <link>https://rip.trb.org/View/2726232</link>
      <description><![CDATA[Problem Statement: Numerous bridge approaches and substructures, highway and railway embankments, and particularly roads in low-lying areas adjacent to rivers and their corresponding traffic sign and signal poles are underlain by the silt soils of the Willamette and Columbia River Valleys and below Oregon's coastal communities. These soils are susceptible to liquefaction or cyclic softening during earthquakes and will produce varying degrees of severity in the consequences such as lateral spreading displacement, global instability, and settlement. Settlement of soils will produce drag loads to bridge and traffic sign and signal pole foundations. Such damage has the potential to severely impact our critical surface transportation lifelines and reduce the efficacy of emergency responders and reduce the rate of economic recovery. The risk of seismic ground failure is exacerbated by groundwater table rise, which occurs during short-term, acute events (flooding) and the long-term effects of potential rising sea levels. Application of ground failure models to silty soils that were developed based on the responses of sandy soils can result in over-conservative estimates of the effects seismic ground failure and lead to inefficient use of limited resources as Oregon strives to maintain and improve its current resilience.
This work aims to develop the types of empirical relationships that the geotechnical community are well-familiar with but geared towards transitional silty soils, which can exhibit differing behaviors from the soils which are presently represented in available models. The objectives of this research are to produce specific design guidance, models, and spreadsheet-based tools to: (1) account for the effects of sloping ground on the calculation of the factor of safety against liquefaction/cyclic softening during earthquakes, (2) compute lateral displacements of sloping ground, and (3) calculate vertical settlements of level and sloping ground and any foundations buried within, to (4) culminate in a decision matrix for Oregon Department of Transportation (ODOT) engineers and their consultants to guide the selection of a particular model when assessing the seismic vulnerabilities of existing surface transportation infrastructure. The decision matrix and specific guidelines for conducting cyclic failure analyses and simplified displacement estimates will guide cost-effective measures to assess and improve existing surface transportation infrastructure and improve community and infrastructure resilience to increasingly combined natural hazards.
]]></description>
      <pubDate>Wed, 08 Jul 2026 17:38:31 GMT</pubDate>
      <guid>https://rip.trb.org/View/2726232</guid>
    </item>
    <item>
      <title>Subsurface Analysis Planning Tool for Cost Reduction, Rapid Emergency Evaluation, And Data Support for Rural Service Areas</title>
      <link>https://rip.trb.org/View/2726188</link>
      <description><![CDATA[Transportation professionals often make important decisions about system dependability, project design, and emergency response with limited time and data. This is particularly true as it relates to the underlying soil, rock, and groundwater conditions that directly affect the design and repair of critical infrastructure. Typically, designers and engineers rely on drilling boreholes and performing in-situ tests to characterize subsurface conditions and associated problems for applications ranging from bridges to roadways to stream crossings to landslides. Unfortunately, these exploration techniques are expensive, time-consuming, inherently risky, and often accompanied by significant lead times. Further, the complex nature of this data can be very difficult to interpret and the uncertainty difficult to quantify.

This research project will leverage investments from CLiP, Oregon Department of Transportation (ODOT) SPR786, and SPR808 to improve GOSEP algorithms for predicting subsurface information for planning and emergency response, with a focused aim for extrapolation improvement in rural, data sparse regions. This project also aims to expand the GOSEP database and thereby its capabilities by adding more geotechnical data and parameter datasets as well as by improving the OCR borehole log scanner for archived handwritten borehole logs.]]></description>
      <pubDate>Wed, 08 Jul 2026 17:34:58 GMT</pubDate>
      <guid>https://rip.trb.org/View/2726188</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>
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