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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>
    </image>
    <item>
      <title>SPR-5031: Developing INDOT Road Crack Image Datasets for Advanced Analytics Research</title>
      <link>https://rip.trb.org/View/2691526</link>
      <description><![CDATA[The Indiana Department of Transportation (INDOT) requires a comprehensive, annotated crack image dataset from falling weight deflectometer (FWD) testing to enable advanced analytics for pavement management. This project delivers systematically labeled crack images to correlate with structural deflection data, and standardized annotation protocol. Dataset enables automated crack detection, enhanced structural assessment capabilities, and data-driven pavement management decisions while leveraging existing image archives cost-effectively through annotators.]]></description>
      <pubDate>Wed, 06 May 2026 14:55:27 GMT</pubDate>
      <guid>https://rip.trb.org/View/2691526</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>Incorporating Pavement Structural Capacity into TxDOT Pavement Management Information System</title>
      <link>https://rip.trb.org/View/2666837</link>
      <description><![CDATA[The research team will provide the Texas Department of Transportation (TxDOT) a means to use Traffic Speed Deflectometer (TSD) data to assess the structural condition of their roadways at the network-level by (a) leveraging TSD measurements and pavement data from existing databases in the US to complement the information collected in Texas for proposing and validating indices derived from velocity-based TSD measurements. To do this, the research team will develop a novel, velocity-based methodology for analyzing TSD data, as existing approaches rely on deflection-based methods not suited for the TSD, consider appropriate velocity indices and thresholds for classifying pavement structural condition, assess load transfer efficiency of jointed pavements, and ensure seamless integration of these data into PMIS.]]></description>
      <pubDate>Tue, 10 Feb 2026 14:45:51 GMT</pubDate>
      <guid>https://rip.trb.org/View/2666837</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>Correlating Pavement Conditions and Traffic Accidents through AI-based Data Mining
</title>
      <link>https://rip.trb.org/View/2627710</link>
      <description><![CDATA[Pavement surface conditions have a strong positive effect on accident risks. Pavement surface distresses directly affect ride comfort and indirectly cause distraction to the driver resulting in loss of control of the vehicle, which may lead to injuries or deaths. The reason for the lack of research on contribution of bad pavement condition to traffic crashes maybe lies in the fact that previously the data of pavement condition are not readily available to researchers in traffic safety, or sometimes it is comparatively hard for researchers to get the systematic data of pavement condition to conduct analyses. The proposed research will take opportunity of current well-known databases such as the long-term pavement performance (LTPP) database and pavement management system (PMS) at state agencies, to conduct deep and systematic data mining on the existing pavement performance and traffic safety data using data-driven intelligence technologies, and develop predictive models in terms of pavement performance, material properties, traffic effects, and pavement maintenance plans. The research outcome will help guide highway agencies to better design, maintain, and manage pavement infrastructures with enhanced roadway safety. 
]]></description>
      <pubDate>Fri, 21 Nov 2025 14:09:04 GMT</pubDate>
      <guid>https://rip.trb.org/View/2627710</guid>
    </item>
    <item>
      <title>AI-Driven Infrastructure Prioritization: Vision-Language Model Framework for Capital Planning</title>
      <link>https://rip.trb.org/View/2620732</link>
      <description><![CDATA[This project develops a scalable, artificial intelligence (AI)-powered framework for automated condition assessment and capital investment prioritization of road and bridge infrastructure across Colorado. Current manual inspection methods are costly, slow, and limited in coverage, often missing early signs of degradation. Leveraging recent advances in Vision-Language Models (VLMs), the project proposes a novel approach that extracts Pavement Condition Index (PCI) and bridge deck ratings from satellite and street-level imagery using VLMs guided by prompt engineering and in-context learning, requiring no retraining, and will be validated against Colorado Department of Transportation (CDOT) inspection records using machine learning model accuracy metrics.

The framework integrates three components: (1) Infrastructure condition (2) network criticality, computed via graph-theoretic metrics and traffic data, and (3) hazard exposure, based on geospatial data for landslides, wildfires, and floods. These layers will be synthesized into a weighted prioritization model to rank road segments for capital upgrades, refined through CDOT expert review.

A web-based geographic information system (GIS) tool will visualize prioritization results, supporting interactive exploration and decision-making. The tool will be pilot-tested with CDOT districts, and outcomes will be disseminated through a final report, peer-reviewed publication, Transportation Learning Network (TLN) webinar, and Colorado LTAP training.

By integrating AI, geospatial analytics, and network modeling, this project addresses data-driven infrastructure planning needs—enhancing efficiency, reliability, and resilience—while offering a replicable model nationwide.]]></description>
      <pubDate>Mon, 10 Nov 2025 16:24:50 GMT</pubDate>
      <guid>https://rip.trb.org/View/2620732</guid>
    </item>
    <item>
      <title>Develop Performance Models for Different Preventive Maintenance Treatments</title>
      <link>https://rip.trb.org/View/2604521</link>
      <description><![CDATA[The Texas Department of Transportation's (TxDOT) Pavement Management Information System (PMIS), recently implemented as Pavement Analyst (PA), stores and analyses network information and pavement performance data to select projects for maintenance and rehabilitation. After the network is analysed, an optimization algorithm produces one of the following recommendations: do nothing, preventive maintenance (PM), light, medium or heavy rehabilitation. PM includes several treatment options associated with very different cost and performance, e.g., seal coat, thin overlay, microsurfacing, etc. Since the performance and cost of these options are different, research is needed to quantify the difference and to incorporate this information into PMIS. Previous TxDOT-sponsored projects have developed performance models and decision trees that were last updated in 2021 as part of Project 0-6988, "Quantification of the Performance of Preventive Maintenance and Rehabilitation Strategies." This update was based on data available at that time, which was a combination of visual distress surveys and automated data. Now, more accurate automated data are available. Therefore, the research team will develop pavement performance models for different PM treatments, update the current decision trees and performance models, validate the models with new data, and develop an implementation plan to incorporate the findings into PMIS.]]></description>
      <pubDate>Mon, 29 Sep 2025 16:08:57 GMT</pubDate>
      <guid>https://rip.trb.org/View/2604521</guid>
    </item>
    <item>
      <title>Update and Improve Pavement Management Information System (PMIS) Treatment Decisions and Effectiveness</title>
      <link>https://rip.trb.org/View/2593187</link>
      <description><![CDATA[The Texas Department of Transportation (TxDOT) modern Pavement Management Information System (PMIS), Pavement Analyst has been successfully implemented since 2017. Districts rely on Pavement Analyst for condition evaluation, project selection and decision-making such as in the 4-Year Pavement Management Plan (4Y PMP). To better reflect the geographical variation in engineering practice, operation, climate conditions, material resources, and project cost among different regions or Districts, the research team will: (1) update the existing statewide decision trees and develop decision trees for various regions or Districts in a hierarchical manner, (2) update different levels of treatment definitions and descriptions, (3) develop a standard procedure for treatment unit cost update and update the treatment unit costs, and (4) update the treatment quality effectiveness. The goal of this project is to develop the necessary tools to make the project selection decision-making more accurate, efficient, and realistic in accounting for statewide and local conditions.]]></description>
      <pubDate>Tue, 26 Aug 2025 12:35:04 GMT</pubDate>
      <guid>https://rip.trb.org/View/2593187</guid>
    </item>
    <item>
      <title>Chip Seal for Uniform Usages</title>
      <link>https://rip.trb.org/View/2582918</link>
      <description><![CDATA[Chip seals are one of the most popular pavement preservation treatments for asphalt pavements due to their ability to seal the existing road surface from moisture damage and oxidation, improve skid resistance, seal minor cracks, and delay deterioration. They consist of a uniform spray application of an asphalt binder followed by a uniform application of aggregate coat cover which is then rolled with pneumatic tire rollers to achieve the desired embedment. This simple process can be conducted using local maintenance personnel with minimal equipment requirements, making it a cost-effective option.

The New Mexico Department of Transportation (NMDOT) has not adopted a specific chip seal design procedure, and rather each district has independent methods to determine the material application rates, generally based on experience. The materials used vary throughout the state based on local availability. Recently, the use of reclaimed asphalt pavement (RAP) as chip seal aggregate has been adopted as an alternative to virgin aggregate due to the sustainability benefits associated with it.

OBJECTIVES: The objectives of this research are to:

Evaluate current NMDOT chip seal practices across the state of New Mexico, specifically materials and application rates. Assess the impact of these practices and/or factors on the performance based on the available pavement management system (PMS) database;
Develop a chip seal design procedure, addressing both local materials and geographic conditions across the state of New Mexico;
Develop statewide chip seal construction specifications;
Develop project selection criteria for NMDOT to obtain optimum performance and treatment life from chip seal applications, such as skid resistance and minimum chip loss.]]></description>
      <pubDate>Tue, 05 Aug 2025 12:00:31 GMT</pubDate>
      <guid>https://rip.trb.org/View/2582918</guid>
    </item>
    <item>
      <title>Pavement Distress Evaluation and Cracking Indices Generation using Deep Learning</title>
      <link>https://rip.trb.org/View/2563770</link>
      <description><![CDATA[The North Carolina Department of Transportation (NCDOT) manages the nation's second-largest roadway network. To ensure safety and efficiency of this network, it is crucial to implement timely and effective maintenance strategies. This research project aims to address these needs. 

In this research, to help optimize maintenance strategies, non-crack distresses will be classified, segmented, and quantified using cutting-edge deep learning techniques. Since an on-going research project has already completed similar tasks for varying types of cracks, upon completion of this proposed study, all types (crack and non-crack) of distresses across the 14 Divisions monitored by NCDOT can be classified and quantified using deep learning models. With this approach, it is estimated that a comprehensive state-wide pavement performance assessment can be completed in one week. Consequently, the outcomes of this proposed study, combined with those from the on-going research, will enable timely updates of distress indices and Pavement Condition Rating (PCR) values. This enhanced responsiveness of NCDOT’s PMS will significantly benefit North Carolina’s roadway network in terms of durability and sustainability. In addition, specific crack metric and index, namely the Pavement Surface Cracking Metric (PSCM) and the Pavement Surface Cracking Index (PSCI), will be calculated using the ASTM E3303-21 standard. The calculated results will be highly accurate, as the length of every crack is quantified at a pixel level. Moreover, this task will standardize and enhance the reliability of crack assessments, contributing to a more effective PMS managed by NCDOT.

One potential challenge that the UNC Charlotte researchers face is identifying certain types of uncommon non-crack distresses from the raw images provided by NCDOT. The lack of training data for these distresses can directly impact the performance of the corresponding deep learning models. To address this issue, the researchers plan to work closely with NCDOT engineers to pinpoint the locations of these distresses and gather sufficient distress data for model training purposes. Another potential challenge is the time-consuming nature of the image annotation process, a common obstacle in studies utilizing deep learning techniques for image processing. Building on the experience gained from the on-going study, the researchers plan to evaluate both AI-based and self-supervised learning approaches to expedite the annotation process effectively. 

It should be noted that transferred learning from deep learning models developed in the on-going NCDOT research project will be used to develop new models in this study. This approach allows resources spent on one task to be transferred, reused, and adapted for other related tasks, significantly reducing the computational resources and time, and more importantly, leading to improved performance of newly developed models.

In summary, this research project is proposed to improve maintenance efficiency, reduce repair costs, and support NCDOT’s sustainability goals. Various approaches will be utilized to ensure the success of this project. The methods and tools developed in this project can be applied to address other challenges in the future.
]]></description>
      <pubDate>Fri, 13 Jun 2025 12:48:03 GMT</pubDate>
      <guid>https://rip.trb.org/View/2563770</guid>
    </item>
    <item>
      <title>Handbook for Pavement Design, Construction, and Management</title>
      <link>https://rip.trb.org/View/2555869</link>
      <description><![CDATA[The design, construction, and management of highway pavements is a complex task that involves the consideration of many topics.  Among these topics are structural design and analysis, drainage provisions, traffic load spectra, material utilization/conservation, surface characteristics, construction practices, pavement evaluation, pavement type selection, life-cycle cost analysis, and preservation.  Some of these topics have been addressed in American Association of State Highway and Transportation Officials (AASHTO) documents (e.g., Pavement Management Guide; Guide for Pavement Friction; and Mechanistic-Empirical Pavement Design Guide, Interim Edition: A Manual of Practice) and a great deal of information on the other topics is available in the literature.  However, this information has not yet been synthesized or assembled in a format that will facilitate accessibility and use by highway agency professionals.  Research is needed to identify current practices; review relevant information; and develop a handbook that discusses the topics pertaining to the design, construction, and management of pavements and incorporates relevant AASHTO documents.  Preparing such a handbook in an interactive-electronic, easy-to-edit format with a printer-friendly option will further facilitate its use and update.  Such a handbook will provide highway agency professionals with ready-to-use information to help them effectively perform the task of pavement design, construction, and management.

 OBJECTIVE: The objective of this research is to develop a handbook that addresses design, construction, and management aspects of pavements.  The Handbook shall be prepared in an interactive-electronic, easily editable format with a printer-friendly option, suitable for consideration and adoption by AASHTO.]]></description>
      <pubDate>Tue, 20 May 2025 14:27:19 GMT</pubDate>
      <guid>https://rip.trb.org/View/2555869</guid>
    </item>
    <item>
      <title>SPR-5016: Integration of SCRIM and Emerging Data Sources into Network Friction Testing Program for Immediate and Long-Term Needs</title>
      <link>https://rip.trb.org/View/2553991</link>
      <description><![CDATA[The Sideway-force Coefficient Routine Investigation Machine (SCRIM) offers an immediate and robust alternative by enabling continuous friction and texture measurements simultaneously, along with key road geometrics and visual image data. Advances in sensor technologies present new opportunities to gather real-time, comprehensive pavement friction data. The proposed project will primarily focus on integrating SCRIM test into INDOT’s network friction testing program while also assessing potential use of emerging data sources to address immediate and long-term needs and enhance the agency’s ability to monitor pavement friction in a comprehensive and cost-effective manner.]]></description>
      <pubDate>Thu, 15 May 2025 15:55:24 GMT</pubDate>
      <guid>https://rip.trb.org/View/2553991</guid>
    </item>
    <item>
      <title>Data-Supported Quantification of Bridge Deck Degradation Using GDOT’s Road Maintenance Data and Other Data Available </title>
      <link>https://rip.trb.org/View/2508900</link>
      <description><![CDATA[The primary aim of this project is to leverage the Georgia Department of Transporation (GDOT)'s extensive data resources to accurately quantify damage or degradation in bridge deck slabs, with the overarching goal of improving safety and mobility. This objective encompasses three main goals: (1) Implementing a geospatial data visualization approach to monitor road surface maintenance activities specifically on bridge decks, (2) Developing methods to quantify bridge deck degradation effectively, and (3) Investigating the impact of changes in traffic patterns on bridge maintenance and condition data.]]></description>
      <pubDate>Tue, 11 Feb 2025 14:59:40 GMT</pubDate>
      <guid>https://rip.trb.org/View/2508900</guid>
    </item>
    <item>
      <title>Quantify the effect of re-carbonation during the use-phase and end-of-life of concrete pavements</title>
      <link>https://rip.trb.org/View/2495001</link>
      <description><![CDATA[This project seeks to validate and improve quantification methods and simulation models to better understand CO₂ uptake in concrete pavements during their service life and recycled concrete aggregate at the end-of-life phase. Hydrated cement in concrete has the potential to sequester CO₂ during the use and end-of-life phases through carbonation, a mineralization process where atmospheric CO₂ reacts with alkali products like portlandite to form stable carbonates. Pavement systems have significant potential for carbonation due to their constant exposure to the environment, the use of preservation methods like diamond grinding that repeatedly expose fresh hydrated cement, and the stockpiling of crushed concrete at the end of its life, where the increased surface area can enhance carbonation. However, systematic methods for quantifying and addressing this uptake in transportation systems is lacking. In this work, the research team will use laboratory characterization of carbonation depth, analysis of factors influencing RCA carbonation in stockpiles, and validation of diffusion-based models to better inform consideration of carbon sequestration in concrete. By considering regional climate variations and assessing the impacts of preservation practices, this work aims to inform sustainable pavement management practices.]]></description>
      <pubDate>Fri, 31 Jan 2025 16:36:28 GMT</pubDate>
      <guid>https://rip.trb.org/View/2495001</guid>
    </item>
    <item>
      <title>Analysis of 2018-2024 Network Level Pavement Structural Testing with the TSD</title>
      <link>https://rip.trb.org/View/2495008</link>
      <description><![CDATA[The Traffic Speed Deflectometer (TSD) is a device used to measure the structural response of pavements while traveling up to the prevailing traffic speed. Virginia Department of Transportation (VDOT) has previously collected data on more than 8,000 lane miles of its roadway network using the TSD. This study seeks to combine thickness data from ground penetrating radar (GPR) and VDOT traffic data to calculate the remaining structural life of the network tested between 2018 and 2024 and to upload the data to VDOTs Pavement Management System.  ]]></description>
      <pubDate>Sat, 25 Jan 2025 10:49:20 GMT</pubDate>
      <guid>https://rip.trb.org/View/2495008</guid>
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