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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>Geotechnical Design Manual</title>
      <link>https://rip.trb.org/View/2789637</link>
      <description><![CDATA[The objective of this research project is to create a Geotechnical Design Manual that documents the current processes utilized by the Louisiana Department of Transportation and Development (LADOTD) Section 67, Geotechnical Design Group with explanation, hyperlinks and references to the supporting policy, design methodology, test procedures, and project development, coordination, and management.  The manual will include at least the following topics:  Table of Contents; Project Coordination Process; Consultant Services and Review; Subsurface Investigation Guidelines; Field and Laboratory Testing Procedures; Material Description-Classification-Logging; Geo Mechanics; Geotechnical load factor and resistance design (LFRD) Design; Geotechnical Resistance Factors; Geotechnical Performance Limits; LA Geology Seismicity; Shallow Foundations; Deep Foundations; Embankments; Earth Retaining Structures; Ground Improvement; Geosynthetic Design; Geotechnical Reports; Plan Preparation; Specifications and Special Provisions; Construction quality assurance/quality control (QA/QC); Construction Monitoring and Instrumentation; Geotechnical Software; Geotechnical Design Section Forms; mechanically stabilized earth (MSE) Walls; Reinforced Soil Slopes; Geotechnical Template Plans; and Project Specifications.
]]></description>
      <pubDate>Mon, 12 Oct 2026 15:38:38 GMT</pubDate>
      <guid>https://rip.trb.org/View/2789637</guid>
    </item>
    <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>An Integrated AI-Powered Approach for Road Infrastructure Health
Monitoring Using UAV and ERI Technologies</title>
      <link>https://rip.trb.org/View/2696933</link>
      <description><![CDATA[The stability of transportation infrastructure, particularly slopes adjacent to roadways, is critical for ensuring safety and longevity. However, traditional monitoring methods (e.g., inclinometers, extensometers, piezometers, sensors, etc.) often fail to provide comprehensive subsurface insights for early slope failure detection. While significant advancements have been achieved by unmanned aerial vehicle (UAV)-based imaging techniques, such as red, green, blue and light detection and ranging, for surface monitoring, several challenges and research gaps remain, such as deeper subsurface data acquisition and automation in surface and subsurface data fusion. This research proposes a data fusion framework integrating UAV-based sensing of slope surface and Electrical Resistivity Imaging (ERI)-based sub-surface characterization to enhance slope stability assessments. Integrating advanced data fusion methodologies using Machine Learning (ML) and Artificial Intelligence (AI), this research aims to develop an infrastructure health monitoring (IHM) framework. This research directly contributes to transportation infrastructure's durability and extended service life by providing a holistic view, advancing early‐warning capabilities, and optimizing maintenance strategies. The integrated monitoring system developed from this study will enable preventative interventions and deliver the precise information required for maintenance. In addition, advanced data fusion using ML and AI enhances infrastructure management technology. By integrating UAV and Electrical Resistivity Imaging (ERI) technologies, this study addresses the limitations of conventional IHM and offers a novel, scalable solution for transportation agencies. The developed framework will serve as a model for data-driven, technology-enhanced geohazard assessments, ensuring safer and sustainable transportation networks.]]></description>
      <pubDate>Thu, 10 Sep 2026 08:32:09 GMT</pubDate>
      <guid>https://rip.trb.org/View/2696933</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>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>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>Liquefaction Induced Lateral Spreading Loads On Bridge Pile Foundations</title>
      <link>https://rip.trb.org/View/2724825</link>
      <description><![CDATA[Earthquake induced soil liquefaction can result in significant displacements in sloping ground. This type of displacement is referred to as lateral spreading and is considered a substantial hazard to Oregon bridges. One current challenge facing bridge foundation design is the knowledge gap regarding appropriate selection of load factors for combining lateral spreading loads (kinematic) and superstructure inertial loads (inertia). Unfortunately, there is no consensus in design codes for how to combine inertial and kinematic loads. Failure to address this knowledge gap presents challenges for Oregon Department of Transportation (ODOT) engineers and designers. If lateral spreading and superstructure inertial loads interact during an earthquake, neglecting their combined effects could lead to inadequate and unsafe designs. Conversely, overconservatively combining these loads may result in costly, non-constructible foundations, particularly for piles passing through stiff, non-liquefiable crusts overlying deep liquefiable soils on sloped grounds. 

The primary objective of this research is to solidify ODOT’s design guidelines for combining superstructure inertial and lateral spreading loads in a pseudo-static analysis. The inertial and kinematic load interaction factors will be characterized by accounting for differences in seismicity in Eastern and Western Oregon, foundation types, and the complexity levels of design methods utilized in various ODOT projects. The proposed methodology for combining superstructure inertial and lateral spreading loads in a pseudo-static analysis will be detailed in a practice-ready recommended amendment to the ODOT Geotechnical Design Manual (GDM) and ODOT Bridge Design Manual (BDM).]]></description>
      <pubDate>Wed, 08 Jul 2026 14:50:18 GMT</pubDate>
      <guid>https://rip.trb.org/View/2724825</guid>
    </item>
    <item>
      <title>Enhancing Site Response Analysis for Bridge Infrastructure in the Southern Plains Region: Issues, Pitfalls, and Pathways to Improvement </title>
      <link>https://rip.trb.org/View/2646962</link>
      <description><![CDATA[Bridges in the Southern Plains Region are essential to the continuity and resilience of the regional transportation network. Although traditionally considered a region of moderate seismic hazard, past seismic events and updated hazard characterizations along with escalating bridge costs have highlighted the need for the additional use of site response analysis in seismic bridge design. This project aims to investigate and resolve key technical limitations in current site response analysis practices as applied to bridge infrastructure within the Southern Plains Region. These include discrepancies in design acceleration estimation between American Association of State Highway and Transportation Officials (AASHTO) general procedure and site-specific site response analyses, the influence of bedrock shear wave velocity on seismic hazard and amplification, the sensitivity of site response outcomes to input motion depth, and the comparative accuracy of equivalent-linear and nonlinear modeling approaches under varying site conditions. The project will utilize detailed geotechnical and seismic data from existing Southern Plains Transportation Center (SPTC), Arkansas Department of Transportation (ARDOT), and U.S. Geological Survey (USGS) efforts to assess representative bridge sites, ultimately producing evidence-based recommendations and tools for more reliable seismic design and evaluation of bridge systems throughout the Southern Plains Region.  

There are four objectives of this project. The first is to quantify discrepancies in fundamental period and design spectrum estimates between the AASHTO general procedure and site-specific site response analysis across varying geologic conditions. The second is to evaluate the sensitivity of site response predictions and Probabilistic Seismic Hazard Analysis results to different bedrock shear wave velocity assumptions, including stiff soil and rock layers. The third is to assess the influence of varying ground motion input depths—from shallow to deep—on spectral amplification and response characteristics for deep basin sites. The last is to compare equivalent-linear and fully nonlinear site response analyses to establish practical modeling guidelines based on site condition, shaking intensity, and data availability. Project tasks include (1) literature review, (2) site selection and data complication, (3) baseline site response analysis, (4) comparative and sensitivity analysis, (5) guideline development, (6) reporting, and (7) dissemination. ]]></description>
      <pubDate>Tue, 06 Jan 2026 17:14:23 GMT</pubDate>
      <guid>https://rip.trb.org/View/2646962</guid>
    </item>
    <item>
      <title>Rockfall Hazard Assessment and Monitoring Program for New Mexico's Transportation Infrastructure</title>
      <link>https://rip.trb.org/View/2607967</link>
      <description><![CDATA[The New Mexico Department of Transportation (NMDOT) Geotechnical Team proposes to develop and implement a geotechnical asset management program that integrates remotely sensed drone data (including photogrammetry, LiDAR, and thermal imagery) with Geographic Information Systems (GIS) to enhance the state’s rockfall monitoring program. Historically, rockfall events have been documented using paper records and Excel spreadsheets. This initiative will modernize data collection and management practices, improving the accessibility, consistency, and utility of rockfall monitoring across New Mexico. Recent advancements in remote sensing and geospatial analytics will equip NMDOT to conduct proactive maintenance, inform planning, and implement long-term risk mitigation strategies for transportation corridors susceptible to rockfall and other slope failure hazards - particularly those exacerbated by changing climate conditions. ]]></description>
      <pubDate>Thu, 09 Oct 2025 13:11:47 GMT</pubDate>
      <guid>https://rip.trb.org/View/2607967</guid>
    </item>
    <item>
      <title>Clay and Shale LRFD Design Criteria for Drilled Shaft Foundations</title>
      <link>https://rip.trb.org/View/2604567</link>
      <description><![CDATA[The Texas Department of Transportation's (TxDOT) geotechnical design guidance has transitioned from using Texas Cone Penetrometer (TCP) boring logs and capacity correlations to design drilled shaft foundation elements with American Association of State Highway and Transportation Officials' Load and Resistance Factor Design (AASHTO LRFD) investigations and resistance-based design methods. The research team will optimize TxDOT's drilled shaft design methods and resistance factors for clay and shale in accordance with TxDOT's specific soil and construction conditions. The research team will develop a likelihood map of shale for a district selected by TxDOT.]]></description>
      <pubDate>Mon, 29 Sep 2025 16:35:38 GMT</pubDate>
      <guid>https://rip.trb.org/View/2604567</guid>
    </item>
    <item>
      <title>Idaho-Specific Correlation of Dynamic Cone Penetrometer (DCP) to Resilient Modulus (Mr)</title>
      <link>https://rip.trb.org/View/2601427</link>
      <description><![CDATA[This project builds on previous Idaho Transportation Department (ITD) research to develop Idaho-specific equations that more accurately correlate Dynamic Cone Penetrometer (DCP) penetration rates with Resilient Modulus (Mr) values for different soil types across the state. The DCP provides a simple, cost-effective method for collecting in-situ subgrade and base resilient modulus data to support pavement design. Reliable correlations will allow districts with limited geotechnical resources to design simpler roadway projects and validate laboratory testing for larger projects. Improved accuracy will help prevent over- or under-designed pavements, reducing costs and extending roadway service life.]]></description>
      <pubDate>Wed, 17 Sep 2025 16:28:03 GMT</pubDate>
      <guid>https://rip.trb.org/View/2601427</guid>
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