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    <title>Research in Progress (RIP)</title>
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    <atom:link href="https://rip.trb.org/Record/RSS?s=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" 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>Road Network Restoration after Major Disruptions</title>
      <link>https://rip.trb.org/View/2447123</link>
      <description><![CDATA[This project develops practical optimization methods for selecting, sequencing, and scheduling restoration actions for disrupted road networks based on incomplete and gradually improving information. Road networks may be severely damaged by events such as hurricanes and earthquakes, and prompt restoration is often necessary for the resumption of emergency services, other essential services, and normal activities.

The proposed methods employ artificial intelligence heuristics such as genetic algorithms and particle swarm algorithms to optimize the schedules of restoration tasks. A hybrid optimization approach combines fast traffic assignment with microscopic simulation to refine solutions. The methods are designed to start with incomplete, uncertain information and adapt dynamically as additional data becomes available from weather forecasts, work crews, and the public. The project also develops methods for pre-planning purposes, including preparing effective restoration plans based on estimated probabilities of disruptions and their consequences.

The research team will collaborate with the Maryland State Highway Administration and other agencies to ensure the practical applicability of the methods. Technology transfer activities include journal papers, conference presentations, software with a user manual, a final technical report, and workshops for interested transportation organizations.]]></description>
      <pubDate>Wed, 11 Mar 2026 13:21:54 GMT</pubDate>
      <guid>https://rip.trb.org/View/2447123</guid>
    </item>
    <item>
      <title>National Road Research Alliance (Phase-3)</title>
      <link>https://rip.trb.org/View/2678150</link>
      <description><![CDATA[This solicitation is for the continuation of the National Road Research Alliance (NRRA) for another 5 years and to continue to support Veda development to increase efficiency and effectiveness of both efforts. The NRRA exists to strategically implement cooperative pavement research. State agencies, industry, academia, consultants and associations work together to identify problems, complete research projects and implement results. The goal is to help agencies nationwide achieve consistent benefits from real world road research. It also seeks to provide members a forum to discuss issues and an outdoor, real-world laboratory (MnROAD) for evaluating cutting-edge pavement technologies.  The NRRA consists of five project teams: Flexible, Rigid, Geotechnical, Intelligent Construction Technologies, and Preventive Maintenance and is governed by an Executive Committee made up of two representatives from each government agency participating in the study.   Each team activities include prioritization of short and long-term research, development of long-term research test sections at MnROAD and providing input for technology transfer.  


]]></description>
      <pubDate>Fri, 06 Mar 2026 13:10:59 GMT</pubDate>
      <guid>https://rip.trb.org/View/2678150</guid>
    </item>
    <item>
      <title>Modernization and Web-Based Implementation of the Illinois Pavement Feedback System</title>
      <link>https://rip.trb.org/View/2677555</link>
      <description><![CDATA[This project will modernize the Illinois Department of Transportation’s (IDOT's) Illinois Pavement Feedback System, a pavement management system that contains detailed construction history, performance data and traffic data of the Illinois interstate system. Researchers will transition the database from a mainframe-based system into a secure, web-based pavement data management and analysis platform. Transitioning to a web-based platform will provide IDOT with an easy way to access the data, monitor interstate sections, and make informed maintenance and rehabilitation decisions. The system will also have a detailed dataset on Illinois’ interstate system available to researchers.]]></description>
      <pubDate>Wed, 04 Mar 2026 09:22:25 GMT</pubDate>
      <guid>https://rip.trb.org/View/2677555</guid>
    </item>
    <item>
      <title>Inventing an AI-Informed Risk Index to Prioritize Transportation Infrastructure Preservation</title>
      <link>https://rip.trb.org/View/2672766</link>
      <description><![CDATA[The proposed project seeks to transform national transportation asset datasets into actionable intelligence for preservation planning. Recognizing the fragmentation between roadway and bridge performance data within the Highway Performance Monitoring System (HPMS) and National Bridge Inventory (NBI), the project introduces an artificial intelligence (AI)-driven framework to systematically connect these datasets and develop a unified risk index. The study will first conduct comprehensive literature and data reviews to identify gaps in cross-asset analysis and assess data quality through spatial joins and validation of key attributes such as average daily traffic (ADT). Using descriptive, prescriptive, and predictive analytics, the research will examine relationships among international roughness index (IRI), bridge condition ratings, and traffic loading to uncover deterioration trends and key predictive features. The research will apply advanced machine learning models to forecast performance and support prioritization under budget constraints. The resulting risk index will provide transportation agencies with an objective method to rank preservation needs. This result will enhance Transportation Asset Management Plans (TAMPs) and ensure data-driven resource allocation. Expected outcomes include a validated analytic framework, cross-asset integration methods, predictive deterioration models, and interactive visualization dashboards to aid decision-making. The project directly supports USDOT's strategic goals of economic strength and global competitiveness by improving asset reliability, minimizing disruptions to freight and passenger mobility, and extending infrastructure service life. Educationally, it will train at least one doctoral student in advanced analytics and risk-based asset management. The research will also integrate the methods and results into graduate coursework and research. Technology transfer activities will disseminate results through academic publications, conference presentations, outreach products, and online tools. Stakeholder engagement will ensure practical adoption. Overall, this project aims to deliver a replicable, scalable decision-support tool to strengthen national transportation resilience and investment efficiency.]]></description>
      <pubDate>Sun, 22 Feb 2026 10:44:16 GMT</pubDate>
      <guid>https://rip.trb.org/View/2672766</guid>
    </item>
    <item>
      <title>TRC2104 Inspection and Maintenance Guidelines for Mechanically Stabilized Earth
Walls</title>
      <link>https://rip.trb.org/View/2669637</link>
      <description><![CDATA[This report outlines the results of a comprehensive study focused on enhancing maintenance and inspection practices for mechanically stabilized earth (MSE) walls. The project was sponsored by the Arkansas Department of Transportation (ARDOT),
and its central objectives were to formulate the best management practices for MSE wall maintenance and develop a comprehensive inspector’s guidebook. To address a gap in the existing literature, the research focused on creating a manual that provides inspection procedures, maintenance guidance, and repair strategies to mitigate distress signs such as panel movement, cracking, and backfill material loss. The methodology encompassed a thorough review of documented MSE wall issues, a nationwide survey yielding insights from 44 state Departments of Transportation, consultations with MSE wall manufacturers, and hands-on site inspections in Arkansas. These efforts were instrumental in creating a geographic information system database cataloging ARDOT's MSE walls, informed by both the survey findings and expert consultation. The resultant maintenance inspector’s guidebook presents a step-by-step inspection framework, grading for distress severity, and tailored repair recommendations. This guidebook was piloted with assistance from ARDOT and was updated as needed to ensure smooth implementation into current maintenance practices within the agency. The project’s multi-faceted approach ultimately seeks to enhance the longevity and reliability of the MSE wall infrastructure and serve as a pivotal resource for maintenance personnel.]]></description>
      <pubDate>Fri, 13 Feb 2026 10:51:12 GMT</pubDate>
      <guid>https://rip.trb.org/View/2669637</guid>
    </item>
    <item>
      <title>Coastal pavement maintenance and rehabilitation decision making based on both surface and subsurface conditions</title>
      <link>https://rip.trb.org/View/2662938</link>
      <description><![CDATA[Texas has approximately 3,359 miles of coastline spanning five geographically distinct districts. Pavements in these regions are exposed to highly variable subgrade soils, diverse traffic loading levels, and unique climatic challenges, including hurricanes, storm surges, and recurrent flooding. Effective decision-making for pavement Maintenance and Rehabilitation (M&R) is therefore critical to ensuring resilient infrastructure, optimizing project selection, and allocating limited resources efficiently. Current M&R selection practices primarily rely on surface-level indicators—such as distress manifestations (cracking, rutting, etc.) and ride quality. While these measures are useful, they fail to provide a comprehensive understanding of the pavement’s structural health. To address this limitation, this study will propose an integrated framework that combines both surface and subsurface information for M&R decision-making. In particular, subsurface conditions derived from non-destructive testing will be emphasized as a means to bridge the existing knowledge gap, enabling a more holistic and data-driven approach to pavement management.]]></description>
      <pubDate>Thu, 29 Jan 2026 15:57:56 GMT</pubDate>
      <guid>https://rip.trb.org/View/2662938</guid>
    </item>
    <item>
      <title>Development of Data Driven Digital Twin for Enhancing Pavement Performance Prediction in South-Central United States</title>
      <link>https://rip.trb.org/View/2658057</link>
      <description><![CDATA[A comprehensive survey conducted by National Cooperative Highway Research Program (NCHRP) Synthesis 501 revealed that many state departments of transportation (DOTs) update their pavement performance models only every 2 to 5 years, with some agencies updating even less frequently. Such lengthy update cycles mean that the models often fail to reflect recent trends in traffic loading and material performance, leading to outdated forecasts that diminish the accuracy and usefulness of maintenance and rehabilitation planning. The primary objective of this project is to develop a data-driven Digital Twin (DT) framework based on pavement management system data that will regularly update Artificial Intelligence (AI)-based performance models for pavements in Louisiana. This framework aims to help state agencies make smarter, more accurate maintenance decisions while reducing costs over time. The proposed Digital Twin platform will focus on the interstate network in Louisiana, given its importance to the state and its wide implications on mobility and freight movement. The work will be divided into five tasks: (1) collect and preprocess pavement management system data for the interstate network, (2) development of digital twin framework, (3) forecast future pavement conditions in digital twin platform, (4) suggest potential maintenance strategies in the digital twin platform, and (5) prepare final report. The project will address the growing need for innovative approaches that can dynamically integrate diverse datasets, learn from both historical and emerging patterns, and provide transportation agencies with actionable, real-time insights. Digital twin technology offers this dynamic capability by enabling a shift from reactive maintenance toward predictive and proactive strategies.  ]]></description>
      <pubDate>Fri, 23 Jan 2026 13:50:08 GMT</pubDate>
      <guid>https://rip.trb.org/View/2658057</guid>
    </item>
    <item>
      <title>An AI-Based Reasoning Framework for Proactive Infrastructure Monitoring and Preservation Using Connected Autonomous Vehicles</title>
      <link>https://rip.trb.org/View/2655750</link>
      <description><![CDATA[This research proposes the development of a Connected Autonomous Vehicles (CAV)-based Proactive Infrastructure Preserving (CAV-PIP) system to enhance the safety, resilience, and operational efficiency of transportation infrastructure. The system leverages the sensing and communication capabilities of CAVs to enable continuous, real-time detection and reporting of roadway anomalies, such as pavement distress and damaged traffic signage. By fusing multi-modal sensor data and incorporating a retrieval-augmented generation (RAG) framework with large language models (LLMs), the system constructs a dynamic prior knowledge base to reason about infrastructure conditions and recommend context-aware maintenance actions. The project aims to transform current reactive maintenance practices into a data-driven, proactive framework that improves decision-making for transportation agencies. The system will be validated through simulation in the CARLA (Car Learning to Act) environment and supported by curated real-world datasets. Expected outcomes include an integrated detection and reasoning framework, structured maintenance reporting tools, and publicly shareable datasets and software packages. The project's broader impact lies in advancing intelligent infrastructure monitoring technologies, reducing long-term maintenance costs, and contributing to safer and more sustainable transportation systems.]]></description>
      <pubDate>Mon, 19 Jan 2026 17:01:54 GMT</pubDate>
      <guid>https://rip.trb.org/View/2655750</guid>
    </item>
    <item>
      <title>Digital Twin and Automation of Pipeline Data for Predictive Maintenance and Risk Analysis incorporating Bayesian network</title>
      <link>https://rip.trb.org/View/2655704</link>
      <description><![CDATA[Pipeline infrastructure is critical for global energy transportation, yet aging systems face increasing degradation risks from corrosion, fatigue, and environmental factors. Recent catastrophic failures have demonstrated severe safety, environmental, and economic consequences of inadequate integrity management, with annual losses reaching billions of dollars. Despite significant advances in inspection technologies, including intelligent inspection tools and sensor networks, three fundamental challenges remain unresolved: lack of interpretability in machine learning approaches, difficulty quantifying uncertainties in defect growth predictions, and the challenge of optimizing maintenance decisions with incomplete information.

This research develops a Valuation Bayesian Network (VBN) integrated with Digital Twin technology as an automated decision-support tool for pipeline integrity management. The VBN approach provides a principled foundation for uncertainty quantification, data fusion, state estimation, prediction, and maintenance planning while maintaining interpretability. The framework incorporates probabilistic degradation and failure models, logic and relational models, and surrogate models to address key performance indicators, failure risks, and remaining life estimation.

The proposed Digital Twin architecture comprises four interconnected layers. The physical layer encompasses pipeline infrastructure with distributed sensors and expert knowledge. The simulation layer employs VBN-based probabilistic models capturing time-dependent degradation processes, where state variables, including defect depth and material properties, are modeled through Markov transition models with parameters learned from historical inspection records, pipeline failure databases, and expert elicitation. The data fusion layer performs Bayesian updating by integrating inspection data, operational history, and expert knowledge, enabling adaptive parameter calibration to capture site-specific degradation patterns. The decision layer implements maintenance optimization using value of information analysis and supports counterfactual reasoning for intervention scenarios.

This framework achieves full uncertainty propagation from measurement noise through remaining life predictions, ultimately enhancing operational safety by predicting high-risk defect development and enabling timely maintenance interventions.]]></description>
      <pubDate>Mon, 19 Jan 2026 16:27:23 GMT</pubDate>
      <guid>https://rip.trb.org/View/2655704</guid>
    </item>
    <item>
      <title>Pavement-Maintenance-GPT: Optimizing Pavement Maintenance Decisions Using Generative AI </title>
      <link>https://rip.trb.org/View/2652179</link>
      <description><![CDATA[Pavement maintenance has significant impacts on transportation safety, mobility, and asset management. However, current decision-making for prioritizing pavement repairs often relies on subjective assessments, manual reviews, or disparate datasets, causing inefficiencies, increased vehicle emissions, and occupational health risks. This project proposes “Pavement-Maintenance-GPT,” a large language model designed to optimize repair prioritization decisions using high-fidelity Ground Penetrating Radar (GPR) video log data. 
 
Pavement-Maintenance-GPT leverages advanced generative AI to simulate expert decision-making, synthesizing pavement condition data into actionable maintenance strategies. The LLM will be trained on historical data, expert judgments, and GPR metrics to replicate and enhance human diagnostic capabilities. By providing precise and efficient repair recommendations, the model significantly improves mobility efficiency by reducing unnecessary lane closures while decreasing vehicle idling and associated fuel consumption. 
 
Aligned with CHEM’s focus areas of “Occupational Health and Efficient Mobility,” this research addresses occupational hazards by minimizing workers’ exposure to construction-related risks through optimized maintenance schedules. Additionally, it promotes efficient mobility by lessening disruptions, thereby improving overall roadway mobility. 
 
The anticipated outcomes include improved resource allocation, reduced vehicle emissions, enhanced safety for workers and road users, lower lifecycle costs, and greater transportation system performance. Pavement Maintenance GPT represents a transformative advancement, providing transportation agencies with a robust, scalable, and sustainable solution for optimized infrastructure maintenance, directly contributing to safer, healthier, and more efficient mobility. ]]></description>
      <pubDate>Tue, 13 Jan 2026 15:10:33 GMT</pubDate>
      <guid>https://rip.trb.org/View/2652179</guid>
    </item>
    <item>
      <title>Leveraging AI for Risk‑Informed Culvert Infrastructure Decision‑Making</title>
      <link>https://rip.trb.org/View/2643021</link>
      <description><![CDATA[Culverts are essential components of roadway and drainage systems, yet many are aging, undersized, and vulnerable to failure during heavy rainfall and flooding. Limited inspection resources and outdated condition data make it difficult for transportation agencies to identify which culverts pose the greatest risk to safety and network performance. This project addresses these challenges by applying artificial intelligence and network analysis to improve culvert condition assessment and maintenance prioritization.

The research focuses on the Deerfield County Watershed, where publicly available culvert condition data are incomplete or outdated. Machine learning models will be developed to predict current culvert condition ratings using historical records and environmental data, with an emphasis on interpretable and physics-informed approaches. These predictions will be integrated with geospatial network simulations to identify culverts whose failure would result in significant connectivity loss, flooding exposure, or service disruption. Targeted field inspections will be conducted to validate model predictions and improve accuracy. The results will support proactive, data-driven culvert management and provide a scalable framework for applying AI to infrastructure risk assessment.]]></description>
      <pubDate>Thu, 18 Dec 2025 14:26:56 GMT</pubDate>
      <guid>https://rip.trb.org/View/2643021</guid>
    </item>
    <item>
      <title>Hydrologic and Hydraulic Software Enhancements 2 (SMS, WMS, Hydraulic Toolbox, and HY-8)</title>
      <link>https://rip.trb.org/View/2640674</link>
      <description><![CDATA[This Transportation Pooled Fund (TPF) project will: 1) Enhance the capabilities of the four Federal Highway Administration (FHWA) sponsored software programs and ensure they remain consistent with the latest FHWA technical reference documents; 2) Update the software user manual documentation; 3) Make new software versions publicly available; 4) Develop and deploy technology transfer materials and workshops to test and demonstrate new software content and features; 5) Inform users of the availability of new software versions and features through website postings, email notifications, newsletter articles, conference presentations, and other avenues.]]></description>
      <pubDate>Wed, 17 Dec 2025 15:42:55 GMT</pubDate>
      <guid>https://rip.trb.org/View/2640674</guid>
    </item>
    <item>
      <title>Sustainable Anti-Icing Solutions Reducing Concrete Damage with Organic-based Agents </title>
      <link>https://rip.trb.org/View/2640693</link>
      <description><![CDATA[The goal of the research is to identify sustainable alternatives to traditional chloride-based de-icing solutions by evaluating organic-based anti-icing agents, such as beet, corn juice, etc. These agents are intended to reduce the detrimental impact on concrete infrastructure, minimize corrosion, and maintain effective performance at lower temperatures. This project will provide the Missouri Department of Transportation (MoDOT) with an innovative, sustainable approach to winter road maintenance, enhancing road safety while protecting infrastructure longevity. Reduced salt usage will lower maintenance costs over time and improve environmental outcomes, benefiting Missouri's residents and ecosystems. Organic-based anti-icing solutions can be more effective at lower temperatures than traditional salt solutions and may be less corrosive to concrete. The goal for this research project is to find an organic alternative by exploring renewable resources from organic by-products, which are both cost-effective and environmentally friendly.]]></description>
      <pubDate>Tue, 16 Dec 2025 09:28:34 GMT</pubDate>
      <guid>https://rip.trb.org/View/2640693</guid>
    </item>
    <item>
      <title>Evaluating Bat Use of NDOT-managed Transportation Structures with Occupancy Modeling</title>
      <link>https://rip.trb.org/View/2636171</link>
      <description><![CDATA[Bridges, culverts, and other transportation structures sometimes provide roosting habitat for bats. Widespread use of these transportation structures across Nevada may potentially provide bat roosting habitat in areas naturally devoid of karst systems, and it is currently poorly documented how the presence of transportation structures in Nevada may increase the range of cavernicolous roosting bat species across the state. These structures may also supplement natural cave roosting habitat in the region that is sometimes disturbed or negatively impacted. Bridges, culverts, and other transportation structures vary in both design and materials, and, therefore, in their potential attractiveness to bats as roosting structures. Currently, Nevada Department of Transportation (NDOT) manages over 2,000 bridges and 52,000 culverts across Nevada. During a pilot study NDOT conducted in 2024, a subset of bridges and culverts were documented as supporting roosting use by bats. Thoroughly understanding the types of transportation structures that bats are using across Nevada, as well as the species, colony type, and degree of use, will provide NDOT an opportunity to efficiently plan maintenance activities on these structures so that future unexpected bridge and culvert maintenance work delays are minimized.

The objectives of this research are to: (1) Determine which transportation structures across Nevada are most likely to provide roosting habitat for bats through the development of a predictive model to improve NDOT bridge survey planning efficiency. (2) Complete 500 to 650 visits to transportation structures assuming approximately four culvert visits for every bridge visit (because bridge surveys are generally more time intensive) to determine which surveyed structures show no signs of use, show signs of use, and are actively occupied at the time of survey. (3) Determine the type of colony and seasonality of use for occupied structures. (4) Develop best management recommendations into an estimated 20-page bat management plan plus references and appendices based on survey results and a finalized model. ]]></description>
      <pubDate>Tue, 09 Dec 2025 14:09:43 GMT</pubDate>
      <guid>https://rip.trb.org/View/2636171</guid>
    </item>
    <item>
      <title>Synthesis of Information Related to Highway Practices. Topic 57-03. Practice on Monitoring Scour Plan of Action for Bridges During and After Floods</title>
      <link>https://rip.trb.org/View/2630487</link>
      <description><![CDATA[Scour is the most common cause of bridge failures. Per 23 CFR 650.313, state departments of transportation (DOTs) must have programs to manage the risks of scour vulnerability in their bridge inventories. Specifically, this regulation requires state DOTs to maintain a documented plan of action (POA) for every scour-critical bridge and every bridge with unknown foundations. A POA typically includes a plan for monitoring the bridge during or after flooding to ensure it is safe for traffic or closed if found to be unsafe.

The 23 CFR 650.313 does not prescribe specific monitoring methods, so state DOTs use a variety of approaches and software in their POAs. These approaches can vary depending on factors such as data availability, funding, and resources for scour monitoring. Therefore, a synthesis study documenting state DOT practices for meeting POA monitoring requirements for both on- and off-system bridges will help state DOTs implement monitoring scour POAs within their unique organizational contexts.

The objective of this synthesis is to document state DOT practices and policies for POA implementation, including monitoring methods, software, instrumentation, and other tools used in these efforts.
]]></description>
      <pubDate>Wed, 26 Nov 2025 16:33:27 GMT</pubDate>
      <guid>https://rip.trb.org/View/2630487</guid>
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