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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>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>Artificial Intelligence for Pavement Condition Assessment from 2D/3D Surface Images</title>
      <link>https://rip.trb.org/View/2727389</link>
      <description><![CDATA[In phase I, the research team selected/annotated a library of two-dimensional/three-dimensional (2D/3D) pavement surface images in AASHTO standard and developed Artificial Intelligence (AI) Machine Learning (ML) models, using the established dataset. Phase I achieved a Technology Readiness Level (TRL) of 6, significantly below TRL 8 required for implementation. This gap was compounded by the lack of sufficient data for several pavement distress types and a new functional requirement requested by TxDOT to include distress segmentation to the scope of work. In phase II, the research team will prepare more pavement image data provided by TxDOT to achieve the needed diversity on all pavement distress types in the 2D/3D image data library. The research team will revisit the tasks of literature review and AI/ML model selection, revise and optimize the trained models to make improvements, and develop new models to more accurately detect/segment and quantify pavement distresses for TxDOT.]]></description>
      <pubDate>Fri, 10 Jul 2026 17:50:55 GMT</pubDate>
      <guid>https://rip.trb.org/View/2727389</guid>
    </item>
    <item>
      <title>Condition Assessment Technology for Bridge Decks with Waterproofing Membranes</title>
      <link>https://rip.trb.org/View/2717329</link>
      <description><![CDATA[The condition assessment of reinforced concrete (R/C) bridge decks with waterproofing membranes and asphalt overlays is a significant challenge that has limited the use of this corrosion-protection strategy. State departments of transportation (DOTs) with existing inventories of bridge decks with waterproofing are unable to make effective repair and rehabilitation decisions because there is no way to assess the condition of the deck concealed by the waterproofing system. Some highway agencies do not use waterproofing membranes or asphalt overlays for the same reason.

 

Bare R/C decks can be assessed easily by traditional methods such as sounding or chain drag, but bridge decks with a waterproofing membrane and asphalt comprise several layers of different materials that preclude the use of these traditional methods. Defects can occur within the R/C decks (delamination) or between the layers (debonding), and chloride-contaminated moisture can be entrapped between the layers. The presence of these subsurface defects affects heat flow through the multi-layer medium, producing subtle variations in the asphalt surface temperature, which are too small to be detected using conventional infrared thermography. More advanced nondestructive methods have also not proven successful. 

For NCHRP 20-30/IDEA 262, the research team will develop a new nondestructive technology based on time-lapse thermography to assess the condition of R/C bridge decks with a waterproofing membrane and asphalt overlay. The proposed research will explore if subtle surface temperature variations can be reliably detected by using a time-lapse thermography approach, which collects thermal data over time to assess time-varying thermal behavior rather than simply measuring surface temperatures. This helps increase sensitivity, potentially enabling more effective imaging of debonding, delamination in the R/C deck, and areas of entrapped moisture. The proposed technology serves a high-priority need of state DOTs that own in-service decks with waterproofing. 

Working with DOT partners (New Hampshire and Nebraska DOTs), deck materials will be evaluated to obtain more detailed information on the thermal properties of membranes in relation to the proposed measurement method. Measurement algorithms will be developed using a modeling and simulation approach that is currently being used for subsurface damage detection. Following laboratory characterizations to assess the measurement procedures and to obtain needed measurement parameters, field validation of the developed procedure will be performed.]]></description>
      <pubDate>Tue, 23 Jun 2026 13:40:38 GMT</pubDate>
      <guid>https://rip.trb.org/View/2717329</guid>
    </item>
    <item>
      <title>Statewide Pavement Marking Condition Assessment Program</title>
      <link>https://rip.trb.org/View/2485381</link>
      <description><![CDATA[New driver-assist vehicle technologies—such as lane departure warnings, lane-centering systems, and lane-keep-assist systems—rely on pavement striping to function safely and effectively. Therefore, if these systems cannot detect pavement markings due to rain, snow, dust, or roadway damage, they may not operate correctly. Using retroreflective raised pavement markers (RRPM) is one solution to help maintain the visibility of pavement striping under these conditions, but there are tradeoffs. For instance, vehicle technology companies report that RRPMs are effective in helping their systems work, yet deployment is inconsistent throughout many states due to the high expense of installation and maintenance. 
This study would help Arizona Department of Transportation's (ADOT’s) Traffic Engineering Group determine appropriate design and installation guidelines for roadway striping to facilitate the use of new driver-assist vehicle technologies and develop a long-term striping maintenance plan that supports both legacy users and new technology, fine-tune striping assessment programs to optimize where and when new roadway striping should be implemented, and determine how best to inventory the striping’s current condition in order to prioritize striping maintenance activities. This research would then help provide the basis for a statewide system to make sure all Arizona roadways have pavement markings that support and perform at the levels needed for both human drivers and driver-assist vehicle technologies.]]></description>
      <pubDate>Fri, 03 Jan 2025 16:27:13 GMT</pubDate>
      <guid>https://rip.trb.org/View/2485381</guid>
    </item>
    <item>
      <title>Leveraging Vehicle Sensors for Pavement Condition Evaluation and Tracking</title>
      <link>https://rip.trb.org/View/2472693</link>
      <description><![CDATA[Pavement distresses like potholes and rutting pose significant safety risks and maintenance challenges for road networks. Traditional pavement monitoring methods rely on costly, intermittent surveys that fail to capture dynamic changes in conditions. This research explores the use of advanced sensors in modern and autonomous vehicles, including LiDAR, cameras, and inertial sensors, to gather real-time, high-resolution data for detecting and mapping pavement conditions. The project integrates data into Pavement Management Systems (PMS) through novel algorithms employing computer vision, machine learning, and statistical methods. Outputs include open-source algorithms and toolkits, enabling rapid identification and remediation of pavement issues, thereby improving road safety and advancing transportation technology.
]]></description>
      <pubDate>Mon, 09 Dec 2024 09:53:44 GMT</pubDate>
      <guid>https://rip.trb.org/View/2472693</guid>
    </item>
    <item>
      <title>Leverage AI for Asset Inventories &amp; Management</title>
      <link>https://rip.trb.org/View/2447052</link>
      <description><![CDATA[Texas Department of Transportation (TxDOT) owns and maintains a large number of safety and traffic-related devices including signs, delineators, guardrails, roadway lights, and traffic signals. While TxDOT maintains inventories for large infrastructure assets such as bridges, there is a need to develop more robust, dynamic inventory databases. This tool would enable TxDOT to make strides toward a cutting-edge proactive and holistic asset management practice, prioritize assets and corridors for maintenance, and maintain an updated understanding of asset location and condition. The main objective of this research is to develop a framework and prototype that uses data collected from CAVs to augment TxDOT's asset inventory and management. Integrating cutting-edge data sources and training AI to (1) identify assets and (2) detect changes to asset condition can improve TxDOT's awareness of the location and condition of assets. These data sources would augment, not replace, the traditional remote sensing and mobile mapping applications used by TxDOT teams. Components of the research work would included: [1] Compile asset inventory and condition summaries using one or multiple third party datasets. [2] Develop a condition rating system for each infrastructure to understand and monitor the status. [3] Collect data during nighttime operations in order to evaluate reflectivity. [4] Develop and end-to-end framework to integrate third-party data into TxDOT's asset inventory toolkit.]]></description>
      <pubDate>Wed, 30 Oct 2024 15:11:50 GMT</pubDate>
      <guid>https://rip.trb.org/View/2447052</guid>
    </item>
    <item>
      <title>Developing Context-Aware Computer Vision Models for Robust Data-Informed Condition Assessment of Bridges</title>
      <link>https://rip.trb.org/View/2437397</link>
      <description><![CDATA[Visual inspection at regular intervals has traditionally been the primary method for assessing the condition of transportation assets to ensure they meet performance objectives. However, this method is labor-intensive, costly, poses safety risks to inspectors, and may suffer from quality inconsistencies. These challenges have driven the adoption of new inspection technologies such as drone imagery and LiDAR. However, the abundance of data generated from these technologies motivates the development of automatable and reliable methodologies for data processing to understand asset conditions and performance. Computer vision (CV) techniques offer an efficient means to process visual data and extract a high-level understanding of images and videos. However, the current CV-based techniques ignore the "context" of collected data, limiting their applicability and generalizability. This study aims to develop robust, context-aware CV models with low inference times that provide actionable insights on asset conditions. The proposed models will be applied to steel bridges, and the impact of various spatial and temporal contexts on CV model performance will be examined. The project outcome will advance the state of the art of using CV models for bridge inspection and provide opportunities for integrating these technologies into integrated asset management systems.]]></description>
      <pubDate>Wed, 02 Oct 2024 16:03:54 GMT</pubDate>
      <guid>https://rip.trb.org/View/2437397</guid>
    </item>
    <item>
      <title>A Review of Florida's FC-5 Raveling Condition Assessment and Measurement Methods</title>
      <link>https://rip.trb.org/View/2425104</link>
      <description><![CDATA[The objective of this project is to determine an appropriate method to account for raveling in Florida's pavement condition survey and subsequent pavement performance forecasting. The research should consider survey approaches as well as the rating system.]]></description>
      <pubDate>Tue, 03 Sep 2024 13:18:50 GMT</pubDate>
      <guid>https://rip.trb.org/View/2425104</guid>
    </item>
    <item>
      <title>Transforming Infrastructure Inspection by Integrating a UAS with a Continuum Robotic Arm for Potential Contact-Based Damage Assessment</title>
      <link>https://rip.trb.org/View/2412947</link>
      <description><![CDATA[Uncrewed Aerial Systems (UAS) hold promise for revolutionizing the inspection of transportation infrastructure by enabling rapid and safe assessments. However, the application of UAS is predominantly limited to detecting surface-level defects, such as visible cracks, due to the reliance on vision sensors. This approach inherently misses subsurface damage, which, to date, requires direct contact-based methods (e.g., ultrasonic, magnetic, and radiographic techniques) that are currently carried out by manual inspection. This project aims to preliminarily investigate a transformative approach to infrastructure inspection by developing an integrated UAS platform equipped with a continuum robotic arm for contact-based inspection. The project will also conduct a preliminary evaluation of sensors suitable for contact-based infrastructure inspection, providing a basis for future sensor integration efforts. This proposed system aims to establish a foundational approach for future developments in multimodal and autonomous infrastructure inspection, advancing the field by overcoming current limitations in damage assessment capabilities.]]></description>
      <pubDate>Mon, 05 Aug 2024 19:03:12 GMT</pubDate>
      <guid>https://rip.trb.org/View/2412947</guid>
    </item>
    <item>
      <title>Leveraging Vehicle Camera Data for Road Condition Monitoring: A Crowdsourcing and Machine Learning Approach</title>
      <link>https://rip.trb.org/View/2408289</link>
      <description><![CDATA[One key part of pavement management is to assess the road condition and identify pavement distresses such as cracks and potholes. These road distresses, if not identified and repaired timely, could compromise road safety, cause expensive damage claims, and also lead to more expensive later repairs. To assess pavement condition, pavement condition data need to be collected first. However, traditional pavement data collection still relies on manual or specialized vehicles equipped with expensive sensors and requires personnel driving along each road in the road networks. Therefore, traditional road inspection methods are often costly, labor-intensive, and sporadic with limited coverage, leading to delayed maintenance and compromised safety. Recent advancements in machine learning (ML) and the proliferation of  vehicles equipped with various cameras (built-in or dashacams) and sensors offer a promising avenue for revolutionizing road condition assessment practices. This project will establish a framework for collecting and processing crowdsourcing vehicle camera data, and develop machine learning algorithms that uses such data to automatically assess road conditions and identify road damages such as cracks and potholes. The project has the potential to offer a more efficient, cost-effective, and real-time approach to road condition monitoring over large road networks and provide critical information for timely maintenance.]]></description>
      <pubDate>Fri, 26 Jul 2024 21:32:52 GMT</pubDate>
      <guid>https://rip.trb.org/View/2408289</guid>
    </item>
    <item>
      <title>Quant CR for Transformative Bridge Asset Management </title>
      <link>https://rip.trb.org/View/2404269</link>
      <description><![CDATA[The research team proposes developing an artificial intelligence (AI)-powered quantitative condition rating (QUANT CR) model which operates on a low-cost geographic information system (GIS) platform, aiding local and state bridge owners in maintenance, repair, and replacement (MRR) decisions while preserving the established inspection and condition rating practices. 
The next generation asset management system leverages the knowledge gained from 50+ years of bridge inspection practices but is predictive, forward-looking, and transformative. QUANT CR embodies insights gained from the understanding of human behavior to better assist bridge owners in decision-making. Thus, the team envisions QUANT CR will be operated in parallel with the existing bridge condition ratings and provide simple decision aids for bridge owners. 
The team believes bridge condition ratings can be better predicted by modern machine learning methods, leveraging the historic data, evolving element condition ratings, and detailed defect items. Additionally, deep learning widely used for text recognition enables an analysis of inspectors’ narratives describing bridge conditions. Lastly, computer vision and deep generative learning help bridge owners visualize the outcomes of their decisions - MRR actions/inactions, empowering bridge owners. ]]></description>
      <pubDate>Sun, 21 Jul 2024 15:03:54 GMT</pubDate>
      <guid>https://rip.trb.org/View/2404269</guid>
    </item>
    <item>
      <title>SPR-4907:  Systemwide Asset Condition Assessment using Connected Vehicle Data</title>
      <link>https://rip.trb.org/View/2389623</link>
      <description><![CDATA[This research project will investigate the feasibility of connected vehicle data to monitor the pavement conditions and ride quality on Indiana roads, in near real-time. The routine monitoring of pavement condition from this crowd-sourced data will be beneficial for agile prioritization of maintenance activities and longer term capital projects. It is envisioned the findings of this project could also be useful to local agencies. ]]></description>
      <pubDate>Wed, 12 Jun 2024 16:19:22 GMT</pubDate>
      <guid>https://rip.trb.org/View/2389623</guid>
    </item>
    <item>
      <title>Develop a Methodology for Pavement Drainage System Rating</title>
      <link>https://rip.trb.org/View/2379652</link>
      <description><![CDATA[The objective of this research is to explore the use of existing pavement and LiDAR data to develop a pavement drainage system rating index as part of pavement condition assessment in Louisiana, potentially by creating a drainage rating index as part of pavement condition assessment.]]></description>
      <pubDate>Tue, 14 May 2024 10:42:55 GMT</pubDate>
      <guid>https://rip.trb.org/View/2379652</guid>
    </item>
    <item>
      <title>New and Updated Statewide Historic Bridge Survey</title>
      <link>https://rip.trb.org/View/2342175</link>
      <description><![CDATA[Completed in 1995, the collaborative effort completed by the Iowa Department of Transportation (Iowa DOT), Federal Highway Administration (FHWA), and all Cities and Counties was the first statewide historic bridge study that inventoried all bridges in the state (with very few exceptions) that were built prior to 1942 (FraserDesign 1995).  Prior to this effort LPAs and the Iowa DOT had to evaluate all bridges over 50 years old individually as part of the project development process under the National Historic Preservation Act.  Under the premise that historical significance changes through time, historic preservation stakeholders such as the Iowa State Historic Preservation Office (Iowa SHPO) usually consider evaluations five years old or less valid.  A substantial amount of grace was extended to the 1995 statewide bridge study.  However, over the last several years it has become apparent that an update to the statewide bridge study is becoming increasingly necessary.  Either funded with support from FHWA or permitted through the USACE, more and more LPAs are being asked to evaluate their historic bridges one by one (recent examples can be provided).  We are likely entering a time were it will be much more effective (for both planning purposes and for costs) to complete an update to the statewide bridge survey whereby allowing LPAs to know if they are dealing with a historic bridge before they begin the project development process.  Advancing the bridge inventory cut-off date to 1975 would maximize the long-term efficiencies that an update could provide.  It’s recommended that LPAs and the Iowa DOT work to find a partnership that benefits all infrastructure owning agencies. Its important to note that the Iowa DOT did update its inventory of historic bridges in 2011, however, that effort only applied to primary system bridges.  This effort will allow for submission to any federal agency requiring a historical review including Corps, FHWA, FEMA.  All LPA's and Iowa DOT will benefit from this new updated study.  ]]></description>
      <pubDate>Tue, 20 Feb 2024 19:03:57 GMT</pubDate>
      <guid>https://rip.trb.org/View/2342175</guid>
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