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    <title>Research in Progress (RIP)</title>
    <link>https://rip.trb.org/</link>
    <atom:link href="https://rip.trb.org/Record/RSS?s=PHNlYXJjaD48cGFyYW1zPjxwYXJhbSBuYW1lPSJzdWJqZWN0aWQiIHZhbHVlPSIxNzg4IiAvPjxwYXJhbSBuYW1lPSJkYXRlaW4iIHZhbHVlPSI3MzAiIC8+PHBhcmFtIG5hbWU9InN1YmplY3Rsb2dpYyIgdmFsdWU9Im9yIiAvPjxwYXJhbSBuYW1lPSJ0ZXJtc2xvZ2ljIiB2YWx1ZT0ib3IiIC8+PHBhcmFtIG5hbWU9ImxvY2F0aW9uIiB2YWx1ZT0iMTYiIC8+PC9wYXJhbXM+PGZpbHRlcnMgLz48cmFuZ2VzIC8+PHNvcnRzPjxzb3J0IGZpZWxkPSJwdWJsaXNoZWQiIG9yZGVyPSJkZXNjIiAvPjwvc29ydHM+PHBlcnNpc3RzPjxwZXJzaXN0IG5hbWU9InJhbmdldHlwZSIgdmFsdWU9InB1Ymxpc2hlZGRhdGUiIC8+PC9wZXJzaXN0cz48L3NlYXJjaD4=" rel="self" type="application/rss+xml" />
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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> 
Developing a Web Interface for Pipe Deterioration Models
</title>
      <link>https://rip.trb.org/View/2762052</link>
      <description><![CDATA[Pipeline infrastructure is critical to energy transportation and urban utility systems, yet aging pipelines are increasingly vulnerable to corrosion, material degradation, and environmental damage. Traditional integrity assessment relies on periodic inspection and offline analysis, providing only discrete snapshots of system condition and failing to capture the continuous, uncertain evolution of deterioration, leaving maintenance and risk-mitigation decisions reactive rather than predictive.
This project develops a dynamic probabilistic deterioration model and an interactive web-based decision-support interface. The predictive engine is a Dynamic Bayesian Network (DBN) constructed in the GeNIe modeling environment, mapping causal dependencies among pipeline condition factors (coating degradation, soil corrosivity, operating parameters) and defining time-slice transitions to capture corrosion evolution. Conditional probability tables are parameterized from historical inspection records, empirical corrosion models, and structured expert elicitation. The DBN is exported and embedded in a backend inference module that applies Bayesian updating to user inputs and computes posterior risk probabilities in real time. An interactive frontend translates the probabilistic forecasts into intuitive time-series risk curves and visual dashboards supporting 'what-if' scenario analyses.
The result is a fully functional web-based framework bridging probabilistic engineering modeling and practical pipeline integrity management. Operators and field engineers can integrate inspection data, simulate risk trajectories, forecast deterioration trends, optimize maintenance schedules, and mitigate corrosion risk before structural failures occur — shifting asset management from reactive to proactive, reducing costly emergency repairs, extending infrastructure lifespan, and mitigating risks to public safety and ecological health. Deliverables include the calibrated DBN model, open-source web interface code, implementation guidelines, a technical report, and peer-reviewed publication.
]]></description>
      <pubDate>Wed, 19 Aug 2026 16:19:45 GMT</pubDate>
      <guid>https://rip.trb.org/View/2762052</guid>
    </item>
    <item>
      <title>Application of Machine Learning to Investigate Grinding Parameters of a Novel Railway Grinding Machine</title>
      <link>https://rip.trb.org/View/2762049</link>
      <description><![CDATA[Rail surface defects develop through repeated wheel–rail interactions and require timely grinding intervention to maintain rail integrity, enhance safety, and avoid costly rail replacement. Although prior studies have optimized individual grinding parameters such as rotational speed, stone granularity, and feed rate, the combined influence of multiple interacting parameters on metal removal rate and surface quality remains insufficiently understood, hindering development of optimized grinding patterns.
This project develops an uncertainty-aware machine learning framework tailored to the limited size of laboratory-generated railway grinding datasets. A Gaussian Process Regression (GPR) model will predict key performance indicators — Metal Removal Rate and Surface Roughness- from inputs such as rotational speed and grinding wheel orientation while quantifying predictive uncertainty. Leave-one-out cross-validation maximizes data utilization, GridSearch optimization identifies hyperparameters, and a fixed random state ensures reproducibility; performance is evaluated with the coefficient of determination and Root Mean Squared Error. A complementary Support Vector Classifier is developed to classify grinding burn conditions, with data-imbalance mitigation via class weighting and Random Minority Oversampling, evaluated by accuracy and F1-score.
Interpretability is central to the effort: SHAP analyses will rank feature importance so railway management can identify the most influential grinding parameters, and Partial Dependence Plots will clarify individual parameter effects. Expected outcomes include unified grinding patterns that achieve target rail profiles while maximizing material removal efficiency and surface quality, supporting the industry shift from corrective to preventive grinding, extending rail service life, reducing replacement costs, and strengthening railway operational safety.
]]></description>
      <pubDate>Wed, 19 Aug 2026 16:05:23 GMT</pubDate>
      <guid>https://rip.trb.org/View/2762049</guid>
    </item>
    <item>
      <title>Tools for Improving Visibility for Snow Plowing</title>
      <link>https://rip.trb.org/View/2762132</link>
      <description><![CDATA[The ability for snowplow operators to see ahead and around their equipment is crucial to the safe and efficient clearing and treatment of roadways during storm events. Under poor visibility, a plow operator may lose situational awareness of their surroundings, which includes other vehicles, the edge or center of the roadway, ditches, curbs, and so forth. Aside from the direct safety impacts on the travelling public in the form of a potential crash, the loss of visibility by plow operators means a need to pay even closer attention to their environment, leading to increased fatigue, stress, etc. Low visibility also increases the likelihood of striking minor objects, such as concrete curbing, leading to the potential for operator injuries to occur, not to mention infrastructure damage.
There is a need for research to investigate the available technologies that can be employed in-vehicle to improve the visibility of the roadway environment for plow operators. As autonomous vehicles become more sophisticated, the technologies they employ to view the roadway are likely transferable to activities like snow plowing. The application of these technologies (i.e., a Global Positioning System [GPS], different types of cameras, other sensors) to snowplows would provide operators with improved visibility of the road ahead. However, until an investigation is made into what technologies are available, their capabilities and costs, what agencies within Minnesota and nationally may be already using them, among other questions, the potential for widespread application of such devices remains largely untapped. The primary benefit of this research will be an understanding of the technologies and products that are available to assist plow operators in seeing the road ahead and the associated costs of those technologies. ]]></description>
      <pubDate>Wed, 19 Aug 2026 15:18:12 GMT</pubDate>
      <guid>https://rip.trb.org/View/2762132</guid>
    </item>
    <item>
      <title>Assessing the Chloride Impacts on the Pavement Structure</title>
      <link>https://rip.trb.org/View/2762035</link>
      <description><![CDATA[Winter maintenance in Minnesota relies heavily on chloride-based deicing agents to maintain roadway safety during snow and ice events. These salts infiltrate pavement structures, increase moisture retention in aggregate layers, alter freeze–thaw behavior, and accelerate surface and subsurface deterioration. Over time, chloride-induced damage reduces granular equivalent (GE) strength, shortens pavement service life, and increases maintenance costs.

This research will characterize chloride transport and retention in pavement layers; quantify impacts on moisture conditions, stiffness, and GE strength; and assess the performance of asphalt, concrete, and granular layers under chloride exposure. The work will include a targeted agency survey, representative site investigations, field sampling, and laboratory testing.]]></description>
      <pubDate>Wed, 19 Aug 2026 10:08:23 GMT</pubDate>
      <guid>https://rip.trb.org/View/2762035</guid>
    </item>
    <item>
      <title>Determination of the Surface Oxidation of Pavement Preservation Treatments</title>
      <link>https://rip.trb.org/View/2761616</link>
      <description><![CDATA[Oxidation, a process in which asphalt binder reacts with oxygen, occurs in asphalt pavement, reducing its longevity, durability and performance. The role of oxidation in pavement preservation treatments, however, is little understood. This project seeks to determine the impact of environmental aging on pavement preservation treatments. Researchers will quantify the extent of aging on asphalt binder used in chip seal and microsurfacing treatments and, if aging is a significant factor, propose ways to mitigate it. Obtaining data on the role of aging in pavement preservation will allow the Illinois Department of Transportation to better predict the lifespan of pavement and help to lessen aging, improving the longevity of pavements and minimizing disruption to drivers for maintenance, rehabilitation or reconstruction activities.]]></description>
      <pubDate>Mon, 17 Aug 2026 11:05:30 GMT</pubDate>
      <guid>https://rip.trb.org/View/2761616</guid>
    </item>
    <item>
      <title>Field Implementation of Quarry By-Product based Otta Seal for Low-Volume Roads</title>
      <link>https://rip.trb.org/View/2761615</link>
      <description><![CDATA[An Otta seal is a cost-effective surface treatment that is formed by adding graded aggregate to a thick application of soft binder. It is worked into the aggregate as traffic rolls over it. This effort expands on Illinois Center for Transportation and Illinois Department of Transportation project R27-268, which successfully designed and tested Otta seal mixes in the lab using underutilized quarry materials, a leftover material from crushed rock extraction. Researchers will test the lab-developed Otta seal design on a real-world roadway section and assess its performance over time. Successful use of Otta seal treatments made with underutilized quarry materials will reduce operational costs and maintenance needs on local roads as well as provide environmental benefits from utilizing existing materials.]]></description>
      <pubDate>Mon, 17 Aug 2026 11:01:41 GMT</pubDate>
      <guid>https://rip.trb.org/View/2761615</guid>
    </item>
    <item>
      <title>Development of a Pavement Friction Management Program for Illinois - Phase II</title>
      <link>https://rip.trb.org/View/2761613</link>
      <description><![CDATA[Friction between pavement and vehicles is a key part to highway safety, allowing drivers to change directions safely as well as reducing skidding or hydroplaning. This effort builds off Illinois Center for Transportation and Illinois Department of Transportation (IDOT) project R27-264, which developed a statewide management program for pavement friction by using statistical modeling to correlate pavement friction and safety analysis. In this project, researchers will use pavement friction data for Illinois’ road network to further enhance the developed program, continue revisions to the friction aggregate mixture policy and conduct pilot implementation in a few IDOT districts. The enhanced program will allow IDOT to identify areas with elevated safety risks and determine improvements to friction to reduce crashes, increasing safety for roadway users.]]></description>
      <pubDate>Mon, 17 Aug 2026 10:55:10 GMT</pubDate>
      <guid>https://rip.trb.org/View/2761613</guid>
    </item>
    <item>
      <title>Condition Rating Survey Performance Models for Pavement Treatments and Surface Types</title>
      <link>https://rip.trb.org/View/2761608</link>
      <description><![CDATA[This project will update models for Illinois Department of Transportation’s (IDOT's) Condition Rating Survey (CRS), which is used to manage, plan and prioritize transportation projects, to reflect new pavement technologies and preservation treatments developed over the past 10 years. The researchers will also develop Illinois-specific performance models for nationwide Federal Highway Administration pavement indices, including International Roughness Index, rutting, faulting, and percent cracking, for all IDOT pavement types. They will also create software that will allow IDOT to model pavement deterioration and update the models as needed. The updated CRS models will improve the accuracy of IDOT’s CRS calculations that forecast future pavement condition as well as projections of FHWA’s performance metrics for Illinois, which will help to identify gaps in performance and potential funding needed to close those gaps.]]></description>
      <pubDate>Mon, 17 Aug 2026 10:32:05 GMT</pubDate>
      <guid>https://rip.trb.org/View/2761608</guid>
    </item>
    <item>
      <title>Unified Distress Severity Framework and Enhanced CRS Calculation Models for Illinois Pavements</title>
      <link>https://rip.trb.org/View/2761607</link>
      <description><![CDATA[The Illinois Department of Transportation (IDOT) uses the Condition Rating Survey (CRS) to assess the condition of pavement statewide and predict future performance. The rating system helps the agency to plan and manage transportation projects. IDOT’s CRS currently has differing definitions for pavement distress and varying severity levels. This project aims to better define distress levels used in IDOT’s CRS as well as create a data-driven, standardized framework for the severity and extent of pavement distresses. The research team will redevelop IDOT’s existing calculation and prediction models for CRS and update the agency’s CRS tool. The updated CRS models will improve the accuracy of condition ratings, which will help IDOT to better select pavement projects and treatments as well as plan the life cycle of pavement.]]></description>
      <pubDate>Mon, 17 Aug 2026 10:29:36 GMT</pubDate>
      <guid>https://rip.trb.org/View/2761607</guid>
    </item>
    <item>
      <title>Accelerated Pavement Testing Working Group</title>
      <link>https://rip.trb.org/View/2761146</link>
      <description><![CDATA[The goal of this pooled fund is to provide a forum for the exchange of technical information on accelerated pavement testing (APT) facilities. The main objective is to focus discussions, studies and knowledge exchange on the entire operation process, including experimental design, sensor selection and installation, data collection and analysis, monitoring processes, and database documentation and data storage with best data management practices, and disseminate research results and the road map for 
APT research. This will be done through formalized online meetings, face-face operational meetings, and one online international conference once during this pooled fund. A consultant will be hired to help document the desired outcomes of this pooled fund. APT operators and agencies will also be held responsible for full participation to make this effort successful. Currently there is no “common” way for all facilities to coordinate with each other than individual partnerships between facilities. Members of this pooled fund could include test tracks like (MnROAD/MnDOT, NCAT/Auburn, Florida, Virginia Smart Road, …) and linear vehicle simulators (LVS) like (FHWA Turner Fairbanks, University California Davis, University of Illinois, CRREL, ERDC- Waterway Experiment, Purdue University, Texas, Florida, …) along with international partnerships from Sweden, Japan, China, France, Spain, …).]]></description>
      <pubDate>Fri, 14 Aug 2026 22:01:28 GMT</pubDate>
      <guid>https://rip.trb.org/View/2761146</guid>
    </item>
    <item>
      <title>Ohio's Research Initiative for Locals (ORIL) Research on Call (ROC) FY2027-2029</title>
      <link>https://rip.trb.org/View/2761155</link>
      <description><![CDATA[Ohio's Research Initiative for Locals (ORIL) is a program designed to provide practice-ready solutions to real-world issues facing Ohio's local transportation system through research. It's a multi-organizational collaborative effort to improve the transportation network of Ohio's counties, townships, cities and villages. Additional information on the ORIL program is available on their website: http://oril.transportation.ohio.gov.  

Created in 2011, ORIL develops, funds and oversees transportation research projects to meet the needs of local agencies for the safety and economic well-being of the traveling public and Ohio. As of March 2023, ORIL has funded a total of 32 projects addressing issues specific to county, township and city roads. At times, situations arise where low-cost, short-term, focused research tasks are needed to address an urgent issue, identify best practices or synthesis existing research. 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, the ORIL Board has determined participation in Ohio Department of Transportation's (ODOT's) Research-On-Call (ROC) program is warranted. The ROC is designed to provide direct, quick access to researchers in specific areas of expertise to conduct short-term, focused, urgent research tasks.
                                                            ]]></description>
      <pubDate>Fri, 14 Aug 2026 15:17:19 GMT</pubDate>
      <guid>https://rip.trb.org/View/2761155</guid>
    </item>
    <item>
      <title>Thermally Strain Resilient Concrete for Surface Transportation Infrastructure</title>
      <link>https://rip.trb.org/View/2752280</link>
      <description><![CDATA[Concrete pavements routinely exhibit large thermal gradients during solar exposure, leading to premature distress and cracking due to thermal expansion. This project aims to develop a new class of thermally strain-resilient cementitious and concrete-based materials designed to transfer heat from the high-temperature surface to lower-temperature subsurface layers (thermal effusivity) while minimizing solar heat absorption. The objective is to include thermal vascular networks into light-colored supplementary cementitious material (SCM)-cementitious matrices generating preferential thermal pathways that promote uniform heat distribution throughout the concrete volume. This technological advancement is expected to yield concrete pavements with to 2x higher thermal effusivity while maintaining high solar reflectivity, keeping surface temperatures near or below ambient air levels under extreme temperature conditions.]]></description>
      <pubDate>Thu, 13 Aug 2026 15:32:09 GMT</pubDate>
      <guid>https://rip.trb.org/View/2752280</guid>
    </item>
    <item>
      <title>Over-height truck impact with prestressed bridge girders  </title>
      <link>https://rip.trb.org/View/2752285</link>
      <description><![CDATA[The devastating collapse of the Dali Francis Scott Key Bridge in Baltimore, Maryland, due to a vessel collision, serves as a sobering reminder of the vulnerabilities in the nation’s infrastructure. The total structural failure led to the loss of six lives, while the $1.9 billion replacement cost underscores the urgent need for improved bridge resilience against extreme impact loads. Bridge collisions are a persistent and widespread hazard, ranking as the second leading cause of bridge failures nationwide. Between 2013 and 2018, an estimated 3,000 vehicle collisions with bridges occurred annually across the United States, with over-height vehicle impacts being among the most frequent and destructive. Prestressed concrete bridge girders, the backbone of many highway overpasses, are particularly vulnerable to such impacts, often resulting in costly repairs, reduced load capacity, or complete structural failure. This project aims to enhance the resilience of prestressed bridge girders against over-height truck impacts by investigating innovative repair and protection strategies. Through a combination of analytical modeling and experimental testing, this research will provide critical insights into the dynamic response of impacted girders and develop effective, practical repair solutions to extend bridge service life and improve safety.
]]></description>
      <pubDate>Thu, 13 Aug 2026 15:31:40 GMT</pubDate>
      <guid>https://rip.trb.org/View/2752285</guid>
    </item>
    <item>
      <title>AI-assisted Condition Assessment of Roads</title>
      <link>https://rip.trb.org/View/2752288</link>
      <description><![CDATA[The objective of this project is to develop an AI-assisted road monitoring system that enables low-cost, autonomous, and frequent condition-based assessments using a network of mobile sensing units. The system will use computer vision and machine learning to detect and quantify pavement defects, replacing traditional schedule-based inspections with continuous, data-driven monitoring. The proposed system provides transportation agencies with an affordable, scalable, and intelligent tool for real-time pavement monitoring. By using low-cost sensors on existing vehicles and automated data interpretation, it delivers accurate condition insights, reduces inspection costs, and supports timely maintenance decisions.]]></description>
      <pubDate>Thu, 13 Aug 2026 15:31:12 GMT</pubDate>
      <guid>https://rip.trb.org/View/2752288</guid>
    </item>
    <item>
      <title>A Data-Driven Probabilistic Framework Using Computational Fluid Dynamics, Artificial Intelligence, and Underwater Robotics for Predicting Bridge Scour</title>
      <link>https://rip.trb.org/View/2745258</link>
      <description><![CDATA[Scour is the leading cause of bridge failure in the U.S. Traditional methods of
scour prediction rely on empirical formulas that require flow information at
bridge location, which is scarce and hard to obtain, and scour inspections often
rely on human divers which is costly, involve safety risks, and often lack the
precision and adaptability needed for the complex coastal and estuarine
environments. This project will develop a novel approach for scour prediction
and mapping that addresses these shortcomings by integrating computational
fluid dynamics (CFD) and machine learning (ML) for real-time prediction of flow
velocity and scour, and underwater robots powered by first-principles and
machine learning-driven perception to provide high fidelity maps of the scour
beyond capabilities of human divers. Project tasks include: (1) identify bridges
vulnerable to scour and characterize their environmental conditions and
structural features, (2) development of a CFD model for scour of a vulnerable
bridge, and deployment of a current meter on the channel bed close to the
bridge to measure currents that drive scour, and use of its data to validate the
CFD model, (3) run the CFD model for a variety water level conditions, spanning
regular tides to intense storms to generate training data for a ML model that will
calculate scour in real time given real-time current measurements at operational
gauges, (4) Develop a probabilistic framework for scour prediction using the
trained ML model, (5) deployment of low-cost underwater autonomous vehicles
to map a scour patch pre- and post-storm, and using the data to validate the
scour models. The framework in this proof-of-concept project can be scaled up
to numerous bridges across any region in future studies. By combining novel simulation and in-situ data acquisition techniques, this project aims to enable risk-informed decision making for management of bridge infrastructure.
]]></description>
      <pubDate>Fri, 07 Aug 2026 08:37:48 GMT</pubDate>
      <guid>https://rip.trb.org/View/2745258</guid>
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