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    <title>Research in Progress (RIP)</title>
    <link>https://rip.trb.org/</link>
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    <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>
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    <item>
      <title>Division of Operations Researcher on Call (ROC)  FY2027-2029</title>
      <link>https://rip.trb.org/View/2786775</link>
      <description><![CDATA[The Ohio Department of Transportation (ODOT) is charged with the management and maintenance of Ohio's vast transportation system.  ODOT strives to execute this charge in the most effective and efficient manner possible.  At times, ODOT encounters situations where low-cost, short-term, focused research tasks are needed to address an urgent issue.  While important and potentially impactful, these research tasks do not warrant the level of a full-scale research project.  Due to the time-sensitive nature of these tasks, it is possible that some of these tasks go unmet because the standard contracting process requires more time than available.  To address this issue, ODOT developed the Research-On-Call (ROC) program.  The ROC is designed to provide direct, quick access to researchers in specific areas of expertise to conduct short-term, focused, urgent research tasks.  This ROC will focus on tasks from the Division of Operations.     ]]></description>
      <pubDate>Wed, 07 Oct 2026 10:37:53 GMT</pubDate>
      <guid>https://rip.trb.org/View/2786775</guid>
    </item>
    <item>
      <title>Clear Roads Winter Highway Operations Phase IV Pooled Fund</title>
      <link>https://rip.trb.org/View/2784243</link>
      <description><![CDATA[Phase IV will maintain Clear Road’s long-term goal mission to advance winter highway operations nationally by undertaking practical, practice-ready research related to materials, equipment, and methods. This supports state DOTs’ pursuit of new technologies, practices, tools, and programs that improve winter highway operations and safety while maintaining fiscal responsibility.

Emerging priority areas of interest in FFYs 2027-2031 are likely to include advanced imagery, machine learning and artificial intelligence technologies, improved decision-making tools, operations coordination among states, updates to content and format of Clear Roads training materials, as well as others that cannot yet be anticipated over the next half decade.

In phase IV, Clear Roads will: (1) Address both operational and management research needs, investigating the most effective tools and practices for clearing snow and ice and for managing program resources, budgets, and performance measures. (2) Continue to promote an even stronger emphasis on the use of research results in the field. (3)         Provide expanded support for implementation and technology transfer through the development of user manuals, training modules, peer exchanges, and quick turnaround syntheses of the most effective state practices from around the country. (4) Give additional attention to new member states from the southeastern and southwestern portions of the country and their unique needs and challenges. (5) Address emerging winter challenges in traditional winter-weather states, such as operational effectiveness in extreme cold conditions and changes in salt supply, availability and cost. (6) Continue to collaborate with other organizations focused on winter maintenance (such as the Aurora Pooled Fund, AASHTO, FHWA, TRB, APWA, and NPRA) to further leverage the research funding investment.

In achieving these objectives, state and local winter maintenance organizations will be able to make informed operational and purchasing decisions based on objective evaluations of promising materials and equipment.

By pooling resources, agencies can conduct more extensive studies across a greater range of conditions than could be done by a single agency with only its own funds. Moreover, by collaborating, sharing information, and conducting impromptu surveys, agencies benefit from each other's experiences and avoid the duplication of research efforts. The outcomes of these projects will help agencies get the maximum financial benefit out of their investments in materials, equipment, and technologies. ]]></description>
      <pubDate>Mon, 05 Oct 2026 04:59:49 GMT</pubDate>
      <guid>https://rip.trb.org/View/2784243</guid>
    </item>
    <item>
      <title>Advancing the Extended T-FAST Test for Assessing ASR Risk in Hardened Concrete</title>
      <link>https://rip.trb.org/View/2784610</link>
      <description><![CDATA[Alkali-Silica Reaction (ASR) remains a significant durability concern for concrete infrastructure and continues to present challenges for transportation agencies. Existing ASR test methods often involve the tradeoff between speed and reliability. Accelerated methods can produce false positives and false negatives, while more reliable expansion-based methods may require several months or years to complete. Research project RP#2024-11 evaluated an extended version of the T-FAST test (Turner-Fairbank Alkali Silica Reaction (ASR) Aggregate Susceptibility Test) by AASHTO TP144, for quantifying ASR risk in hardened concrete. Significant progress has been made through the evaluation of aggregates with known reactivity, development of and experimental database, and assessment of variables affecting the Reactivity Index (RI), including curing conditions, immersion solutions, and use of fly ash and silica fume for ASR mitigation. As the research progressed, several new variables were identified that were not included in the original scope but are important for continued validation of the method. This Technical Transfer project will further advance the Extended T-FAST method by: (i) incorporating additional aggregates with documented reactivity; (ii) evaluating interactions between fine and coarse aggregates; (iii) refining the RI classification framework using classification algorithms; and (iv) supporting the statistical analysis needed to develop a standardized test procedure.​]]></description>
      <pubDate>Wed, 30 Sep 2026 10:32:25 GMT</pubDate>
      <guid>https://rip.trb.org/View/2784610</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>Abrasion Resistance Comparison Wearing Surfaces</title>
      <link>https://rip.trb.org/View/2780489</link>
      <description><![CDATA[This project will evaluate and compare the abrasion resistance of Ultra High Performance Concrete (UHPC), Polyester Polymer Concrete (PPC), and standard concrete mixtures used for bridge wearing surfaces. Laboratory testing will be conducted using ASTM C944 and other applicable methods to compare material performance and assess current testing procedures. The results will be used to develop recommendations for abrasion resistance requirements and testing procedures to support ITD specifications and ensure adequate durability of concrete materials.
]]></description>
      <pubDate>Mon, 21 Sep 2026 15:58:59 GMT</pubDate>
      <guid>https://rip.trb.org/View/2780489</guid>
    </item>
    <item>
      <title>Residual System-Level Performance of Prestressed Girder Bridges with Cast-in-Place Decks after OHTI Repairs: Phase 1</title>
      <link>https://rip.trb.org/View/2780483</link>
      <description><![CDATA[This project will evaluate the performance of prestressed girder bridges with cast in place decks after over height vehicle impacts and repairs. The study will examine girder damage, repair methods, load distribution, diaphragm performance, and preloading to better understand how these factors affect long term bridge performance. The project will include a literature review, analytical modeling, and experimental testing. The results will be used to develop practical guidance for bridge inspections, load ratings, repair decisions, and future bridge design.]]></description>
      <pubDate>Mon, 21 Sep 2026 15:14:15 GMT</pubDate>
      <guid>https://rip.trb.org/View/2780483</guid>
    </item>
    <item>
      <title>Integrating Remote Sensing and Ecological Monitoring for Wildlife-Resilient Roadside Maintenance</title>
      <link>https://rip.trb.org/View/2780408</link>
      <description><![CDATA[The primary objective of this project is to develop an automated, multi-scale decision-support framework that transforms satellite and vehicle-acquired remote sensing data into actionable roadside vegetation management workflows. Specifically, the project will achieve the following technical milestones: (1) Develop a Multi-Scale Sensing Architecture: Combine wide-area daily satellite imagery for macro-scale screening with meso-scale mobile sensor rigs mounted to daily Arizona Department of Transportation (ADOT) fleet vehicles (trucks, sweepers, mowers) for high-resolution edge diagnostics. (2) Engineer Multi-Modal Phenological Classification Models: Utilize Visible & Near-Infrared (VNIR) spectral bands (400–900 nm) to track rapid, post-monsoon green-up windows unique to invasive grasses and invasive forbs across all phenological growth stages, alongside Short-Wave Infrared (SWIR) imaging (900–1700 nm) and Long-Wave Infrared (LWIR) thermal sensing to calculate accurate Live Fuel Moisture Content (LFMC). (3) Construct a Predictive Prioritization Matrix: Build an automated roadside inventory system that fuses structural vegetation canopy data (via oblique terrestrial LiDAR/RGB), fuel moisture maps, wildfire risk indices, and traffic data to automate maintenance work orders and preserve clear zones, seamlessly integrated with an Interactive Fire Risk Dashboard.]]></description>
      <pubDate>Fri, 18 Sep 2026 17:11:58 GMT</pubDate>
      <guid>https://rip.trb.org/View/2780408</guid>
    </item>
    <item>
      <title>Quantifying the Impact of Pavement Smoothness</title>
      <link>https://rip.trb.org/View/2780399</link>
      <description><![CDATA[Pavement smoothness affects the lifespan of Arizona’s state highway network, the frequency of roadway maintenance and preservation projects needed to maintain it, and the cost to individuals of operating vehicles on it. Quantifying the impact of pavement smoothness specifications on pavement service life, vehicle operating costs, and emissions will help Arizona Department of Transportation (ADOT) determine appropriate smoothness standards for roadway construction and rehabilitation. For instance, pavements with a lower score on the International Roughness Index (IRI) typically require less maintenance and have a longer service life. Pavements with high IRI values can lead to increased overall vehicle maintenance costs for motorists, as rough roads cause tires and suspension systems to deteriorate at a faster rate and vehicles to consume more fuel. 
This study will estimate how changes to ADOT’s pavement surface smoothness specifications might extend pavement service life, delay the emergence of maintenance and rehabilitation (M&R) treatment triggers, and reduce vehicle operating costs for individual users. ADOT may use the findings of this study to modify smoothness specifications, producing statewide cost savings due to the increases in service life of pavements and reduced or deferred M&R activities. The results of this study may also be used to update project cost estimates and estimate user-cost savings.  
]]></description>
      <pubDate>Fri, 18 Sep 2026 16:35:41 GMT</pubDate>
      <guid>https://rip.trb.org/View/2780399</guid>
    </item>
    <item>
      <title>Develop Enhanced Quality Assurance Methods for Full-Depth Reclamation Construction</title>
      <link>https://rip.trb.org/View/2778041</link>
      <description><![CDATA[Full depth reclamation (FDR) with emulsified or foamed asphalt can rapidly renew a pavement with significant time and cost savings compared to major reconstruction while providing a durable, high modulus, moisture-resistant base. While most FDR asphalt projects are performing well, improvements are needed in construction startup operations, moisture control, and better relations of acceptance testing to field performance. The research team will assess existing FDR quality assurance frameworks, identify technological advancements, and develop new test methods and protocols to ensure FDR layers achieve their intended performance. The research team will develop more comprehensive and reliable testing coverage for moisture content control to reduce the risk of premature distress under early traffic. The research team will identify, develop, and pilot acceptance test methods that better correlate to long-term pavement performance. The research team will identify improvements to guidance resources to promote more successful FDR project startups. The research team will use the results from this project to develop new test procedures, update construction specifications, and update FDR construction guidelines.]]></description>
      <pubDate>Mon, 14 Sep 2026 16:22:41 GMT</pubDate>
      <guid>https://rip.trb.org/View/2778041</guid>
    </item>
    <item>
      <title>Developing a Pavement Performance Model for Effective Pavement Management of Wyoming County Roads, Phase I</title>
      <link>https://rip.trb.org/View/2777829</link>
      <description><![CDATA[As pavements progressively age and deteriorate over time, it becomes necessary for agencies to allocate substantial financial resources to sustain their pavement network in a condition deemed acceptable. A significant number of county roads were constructed decades ago and have received on-demand maintenance, resulting in suboptimal overall road conditions for Wyoming counties. Therefore, since 2014, the Wyoming Technology Transfer Center, in association with Wyoming Department of Transportation (WYDOT), has been collecting county road pavement performance data in Wyoming to make informed management decisions in the future. The pavement data collected includes rut depth, cracking, roughness, and other associated data such as route, direction, mile post, road width, length, district, county, and speed. Local governmental entities are responsible for the management of county roads, relying on their engineering expertise and judgment, without the implementation of a county-specific Pavement Management System (PMS). Also, different vendors have collected this data over time. There exists a need to check the consistency, quality, and continuity of this data from vendors. This study addresses this gap and plans to develop a pavement performance model from the collected data for county roads. Overall, this study is divided into two phases. Phase I of the research will review the data collected from different vendors for consistency, check quality, and continuity. Thereafter, the study will examine the existing pavement performance deterioration model for county roads. Finally, Phase II of this research plans to develop a new county pavement deterioration model and performance index, along with decision trees for pavement management. This research will help aid decision-makers in making better choices by allocating the funding appropriately with preventive treatment, minor and major rehabilitation to ensure good quality county roads. Finally, the outcome of this research can be used by policymakers to implement the best practices of PMS in Wyoming counties.]]></description>
      <pubDate>Mon, 14 Sep 2026 14:14:50 GMT</pubDate>
      <guid>https://rip.trb.org/View/2777829</guid>
    </item>
    <item>
      <title>Agentic AI for ASTM-Aligned Pavement Assessment Framework</title>
      <link>https://rip.trb.org/View/2777810</link>
      <description><![CDATA[This project proposes an agentic artificial intelligence (AI) framework for pavement assessment that brings together multimodal deep learning, depth-aware evidence, and standards-aligned reasoning within a single workflow. Although recent automated methods have improved distress detection, segmentation, and severity labeling, they still function largely as isolated tasks and therefore do not support the broader engineering reasoning required for maintenance decision-making. As a result, an important gap remains between model prediction and practical pavement assessment. Building on prior advances in pavement condition index estimation, dense captioning, and annotation-efficient segmentation, the proposed framework will detect and classify pavement distress, estimate severity and depth-related condition indicators, identify the appropriate ASTM-aligned assessment pathway, and generate clear engineering-oriented reports. In this way, the project connects perception, interpretation, pathway selection, and reporting in a coordinated system. Ultimately, it aims to move pavement AI beyond passive prediction toward a more interpretable, scalable, and decision-ready tool for transportation practice.]]></description>
      <pubDate>Mon, 14 Sep 2026 14:03:26 GMT</pubDate>
      <guid>https://rip.trb.org/View/2777810</guid>
    </item>
    <item>
      <title>Completion of Hydrologic Design and Platform Data (TR-792 Phase II)</title>
      <link>https://rip.trb.org/View/2775961</link>
      <description><![CDATA[Flooding is Iowa's most persistent and costly natural hazard, responsible for approximately 80 percent of all federally declared disasters in the state since 1989. The Iowa Highway Research Board (IHRB) Project TR-792 demonstrated that On-Road Structures (ORS) are a practical tool for reducing peak flows and protecting downstream communities and infrastructure. TR-792 identified approximately 250,000 potential ORS sites statewide, developed an automated hydrologic design methodology, and deployed results through the Iowa DOT On-Road Structures (IDOT-ORS) web platform. Watershed-scale modeling in six representative HUC12 watersheds showed that strategically deployed ORS systems can reduce 50-year storm peak flows by approximately 18 percent at watershed outlets.

This Phase II proposal will build upon and expand the work completed by TR-792 by working on three interlinked objectives. Objective 1 extends the automated planning design methodology to all feasible ORS sites across Iowa, including scenario analyses that evaluate the effect of alternative roadway elevation assumptions (e.g., raising the road by one or three feet). Objective 2 develops a surrogate machine-learning model, trained on process-based hydrologic simulations, to enable rapid, large-scale estimation of watershed-scale flood reduction benefits. Objective 3 modernizes the IDOT-ORS platform, integrates all new datasets and scenario modeling capabilities, and deploys a targeted communication strategy, including educational videos, training materials, and digital brochures to translate technical outputs into actionable guidance for county engineers, watershed managers, and emergency management professionals. Similar to the approach followed for TR-792, the proposed research will be performed by a team of researchers from Iowa State University and The University of Iowa.]]></description>
      <pubDate>Thu, 10 Sep 2026 11:49:49 GMT</pubDate>
      <guid>https://rip.trb.org/View/2775961</guid>
    </item>
    <item>
      <title>County Pavement Preservation Data Collection</title>
      <link>https://rip.trb.org/View/2775960</link>
      <description><![CDATA[Pavement preservation is a cost-effective, proactive approach for maintaining roadway
infrastructure in a state of good repair. However, pavement preservation practices vary substantially across Iowa’s counties, cities, and agencies like the Iowa Department of Transportation due to differences in available resources, technical expertise, staffing levels, and operational constraints. Counties in particular often face significant challenges in identifying the most appropriate preservation treatments for specific pavement conditions. While generally open to adopting new materials, technologies, and preservation strategies, the performance outcomes of county-implemented treatments have not been systematically documented, standardized, or shared statewide. As a result, Iowa counties often lack access to historical performance data and lessons learned that could inform more confident, cost-effective decision-making. To address this gap, researchers at Iowa State University, in partnership with the Iowa County Engineers Association Service Bureau, have developed and are updating the Iowa Pavement Analysis Techniques (IPAT) tool with support from the Iowa Highway Research Board. Although the IPAT tool provides valuable decision-support capabilities, its effectiveness is fundamentally dependent on the availability, consistency, and quality of input data. The primary objective of this proposed study is to collect, standardize, and process pavement preservation and related data from all 99 Iowa counties to support and enhance preservation recommendations. The study will focus on a statewide collection of historic roadway physical and structural data, along with pavement condition, traffic, and pavement preservation treatment data for Iowa’s secondary paved road network. A web-based County Pavement Preservation Database Platform will be developed to store, manage, and visualize these datasets. The resulting statewide core dataset will enable the IPAT tool to provide county engineers with preservation options informed by both current conditions and historical county-level data—an integrated approach that has not previously been available in Iowa counties.]]></description>
      <pubDate>Thu, 10 Sep 2026 11:43:45 GMT</pubDate>
      <guid>https://rip.trb.org/View/2775960</guid>
    </item>
    <item>
      <title>Ongoing Evaluation of Full-Scale Unpaved Roads Research Test Sites</title>
      <link>https://rip.trb.org/View/2775959</link>
      <description><![CDATA[The goal of the proposed project is to evaluate past Iowa Highway Research Board (IHRB) unpaved granular-surfaced (gravel) road test sites to identify ones that could provide useful long-term performance data from additional field tests, and to develop a list of proposed sites, test methods, and analysis methods for a Phase II experimental project. Deliverables include a Data Collection Template, a Site Condition Summary, and a final report that serves as a scope recommendation and roadmap for a Phase II project.]]></description>
      <pubDate>Thu, 10 Sep 2026 11:19:45 GMT</pubDate>
      <guid>https://rip.trb.org/View/2775959</guid>
    </item>
    <item>
      <title>Human-AI Collaborative Bridge Inspection for Advanced Damage Detection
and Defect Quantification</title>
      <link>https://rip.trb.org/View/2689757</link>
      <description><![CDATA[The aging concrete bridge infrastructure across the U.S. poses increasing risks to safety and reliability, amplifying the urgency to modernize inspection processes. Traditional manual inspection methods are labor-intensive, subjective, and inconsistent, creating barriers to effective infrastructure management. This research proposes an innovative human-AI collaborative framework designed to enhance the durability, resiliency, and technological capabilities of bridge inspections. The project integrates advanced artificial intelligence (AI) and computer vision technologies to automate damage detection and defect quantification. To ensure reliability and trustworthiness, the AI system incorporates rigorous uncertainty quantification methods, increasing transparency and inspector confidence. An active learning component strategically facilitates human-AI collaboration, enabling inspectors to focus on the most critical inspection cases identified by the AI, significantly reducing manual workloads. Additionally, engineering domain expertise, particularly in structural reliability and risk assessment, will inform and prioritize inspection processes within the AI framework. The outcomes of this project will substantially improve the accuracy, efficiency, and reliability of bridge inspections, promoting safer, longer-lasting, and more resilient transportation infrastructure. Anticipated benefits include reduced inspection costs, enhanced public safety, and a proactive approach to infrastructure management that significantly extends service life and supports sustainability. ]]></description>
      <pubDate>Thu, 10 Sep 2026 09:04:28 GMT</pubDate>
      <guid>https://rip.trb.org/View/2689757</guid>
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