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    <title>Research in Progress (RIP)</title>
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    <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>
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      <title>Research in Progress (RIP)</title>
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      <link>https://rip.trb.org/</link>
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    <item>
      <title>Artificial Intelligence (AI) Technologies for Data-Driven Bridge Management</title>
      <link>https://rip.trb.org/View/2703687</link>
      <description><![CDATA[Artificial intelligence (AI) is showing promise for providing quick analysis, summary and documentation of field conditions for bridges. This project will provide the Illinois Department of Transportation (IDOT) with an overview of AI products available for bridge inspection and management. Researchers will review other state agencies’ practices and policies for use of AI in this field as well as develop recommendations for IDOT. Aid in formulation of AI policy for bridge inspection within IDOT may be considered if the department deems the technology essential. Effective use of AI in bridge inspection and management systems will provide cost and time savings to the state, allowing for quicker bridge inspections, diagnosis of issues and documentation.]]></description>
      <pubDate>Fri, 15 May 2026 09:24:49 GMT</pubDate>
      <guid>https://rip.trb.org/View/2703687</guid>
    </item>
    <item>
      <title>SPR-5012: Investigating FAST Act Legislation Requirements for Bridge Load Rating Considering Emergency Vehicles</title>
      <link>https://rip.trb.org/View/2700540</link>
      <description><![CDATA[The research aim is to understand the impact of the Fixing America’s Surface Transportation (FAST) Act combined with exemptions in Indiana code on load rating of bridges and to develop recommendations that comply with legislation while reducing staff burden. The FAST Act has resulted in emergency vehicle loads that must be considered for load rating of bridges. Due to exemptions in Indiana code, this requirement applies to interstate and noninterstate bridges, leading to the posting of 1,649 bridges, mostly maintained by local agencies. The focus will be on understanding the impact on locally maintained bridges and providing guidance on compliance.]]></description>
      <pubDate>Thu, 07 May 2026 09:23:55 GMT</pubDate>
      <guid>https://rip.trb.org/View/2700540</guid>
    </item>
    <item>
      <title>Development of a Real-Time Decision Support Framework for Resilient Bridge Infrastructure During Evolving Hazard Conditions
</title>
      <link>https://rip.trb.org/View/2696159</link>
      <description><![CDATA[Bridge infrastructure serves as a critical lifeline for transportation, emergency response, and economic continuity. In hazard-prone regions such as Florida, bridges face escalating risks from floods, hurricanes, and wildfires that can rapidly disrupt traffic flow and delay emergency operations. Existing bridge management systems primarily focus on long-term planning and condition assessment, offering limited capability for real-time decision-making during evolving hazard events. This project aims to develop a real-time decision support framework that enables dynamic management of bridge infrastructure under active hazard conditions. The proposed framework will integrate real-time hazard forecasts, sensor-based condition monitoring, and infrastructure performance data to guide rapid, data driven decisions. Using advanced analytics and scenario modeling, the system will support time-sensitive operational actions such as rerouting, temporary reinforcement, and emergency closures. A visual decision-support interface will convey hazard progression, bridge condition, and recommended response strategies to transportation agencies and emergency managers in an intuitive, spatially enabled format. Building upon prior work at Florida A&M University on the IntelliViz prioritization platform, this research extends the concept from long term resilience planning to operational support. A regional case study in Florida will demonstrate the practical implementation of the framework and its benefits for improving coordination, minimizing downtime, and enhancing public safety during flood and hurricane events. By integrating real-time data streams with predictive modeling and visualization tools, the project will bridge the gap between static risk assessment and dynamic hazard response, providing a scalable and implementable framework for strengthening transportation resilience and supporting informed, timely decisions during extreme events.]]></description>
      <pubDate>Mon, 27 Apr 2026 20:01:55 GMT</pubDate>
      <guid>https://rip.trb.org/View/2696159</guid>
    </item>
    <item>
      <title>Hyperspectral Imaging for Corrosion Detection in Bridge Structures</title>
      <link>https://rip.trb.org/View/2696155</link>
      <description><![CDATA[The proposed research will investigate the use of hyperspectral imaging for
identifying corrosion of reinforced concrete and steel bridge components.
Research outputs will comprise (1) data characterizing the efficacy of hyperspectral
imaging for identification of corrosion prior to corrosion products being visible to
the human eye, (2) data characterizing the link between corrosion products that
are visible via hyperspectral imaging and the extent of steel mass loss for
reinforced concrete and steel bridge components, and (3) recommendations for
using hyperspectral imaging as part of a comprehensive bridge inspection and
maintenance program.]]></description>
      <pubDate>Mon, 27 Apr 2026 19:49:29 GMT</pubDate>
      <guid>https://rip.trb.org/View/2696155</guid>
    </item>
    <item>
      <title>Intellibridge:AI-Powered Precision In Bridge Maintenance Optimization</title>
      <link>https://rip.trb.org/View/2633325</link>
      <description><![CDATA[Bridges perform the most important roles as part of transportation networks, growing opportunities for interconnection and economic development. Yet, due to limited budgets and the difficulties associated with determining how structures will decay over time, keeping these essential structures in tip-top shape poses significant challenges. These conventional maintenance strategies involve periodic inspection and query-based scheduling, which results in a lack of precision that can lead to delays, poor resource allocation, and wasted time. To overcome these issues, this project will develop an intelligent system called IntelliBridge to transform the way to plan bridge maintenance. The proposed system makes use of advanced machine learning (ML) algorithms that predict the future state of bridge elements, perform cost analysis and give the best maintenance interventions that are under budget, and identify any inefficiencies in the existing strategies. Utilizing historical data from the National Bridge Inventory (NBI) and National Bridge Elements (NBE), IntelliBridge will enable actionable insights that support data-driven decision-making to deliver concerted maintenance interventions that are cost-effective, timely, and performance-driven. Implementing this AI-based solution will provide transport agencies with an effective and strategic mechanism to increase bridges' life, safety, and efficiency.]]></description>
      <pubDate>Tue, 02 Dec 2025 16:42:07 GMT</pubDate>
      <guid>https://rip.trb.org/View/2633325</guid>
    </item>
    <item>
      <title>Intelli-Viz – Comprehensive, Human-centered, Risk-based Online Platform for Evaluation, Visualization and Prioritization of Bridge Projects</title>
      <link>https://rip.trb.org/View/2633324</link>
      <description><![CDATA[Many of the nation’s bridges are aging and require urgent attention for rehabilitation or replacement. Traditional bridge prioritization approaches do not account for the broader impacts of bridge failures on access to critical infrastructure or community resilience. This project aims to address the shortcomings of the existing systems by developing a decision-support platform that considers diverse performance metrics, including mobility, and disaster resilience. Various national agencies have identified the need for computational platforms for varied purposes, such as the Natural Hazards Engineering Research Infrastructure (NHERI) Design Safe, SimCenter, funded by the National Science Foundation (NSF), and Interdependent Networked Community Resilience Modeling Environment (IN-CORE), funded by the National Institute of Standards and Technology (NIST). The purpose of these platforms has been to improve disaster resilience by providing archives of data, state-of-the-art algorithms, and access to high-performance computational resources. However, these efforts have typically limited the use case to researchers instead of practicing professionals. While the proposed project is smaller than these cited efforts, the research focus and targeted user groups differ. The PI and Co-PIs will learn from these projects to implement best practices for data management, algorithmic integration, and long-term survivability. Implementing advanced prioritization methodologies requires data from multiple sources, optimization and statistical prediction techniques, and visualization tools to support decision-making. The platform will assess risk factors on bridges from routine wear to extreme disasters, enabling resilient strategies that strengthen evacuation routes and connectivity to essential services. Designed with a human-centered approach, the platform will prioritize bridge projects that improve safety, and quality of life. An online platform will reduce the time and effort of practical applications, enhance communication, and amplify the impact of other research projects.]]></description>
      <pubDate>Tue, 02 Dec 2025 16:40:27 GMT</pubDate>
      <guid>https://rip.trb.org/View/2633324</guid>
    </item>
    <item>
      <title>Optimizing Strategies in Bridge Asset Management Through Generating Interactive Reinforcement Learning (GI-RL) Methods</title>
      <link>https://rip.trb.org/View/2633318</link>
      <description><![CDATA[The proposed study aims to create a Generating Interactive Reinforcement Learning (GI-RL) framework to optimize bridge maintenance strategies. Bridge asset management is a critical aspect of infrastructure maintenance, particularly for bridge owners responsible for ensuring the safety and functionality of numerous bridges. Traditional methods often involve reactive maintenance strategies, which can lead to suboptimal outcomes. This study proposes exploring the application of reinforcement learning (RL) to optimize bridge management strategies, focusing on strategic decision-making under imperfect information. RL is a subfield of artificial intelligence (AI) that focuses on training agents or stakeholders to make sequences of decisions. It rewards bridge owners for making beneficial choices, such as performing preventive maintenance and/or reducing user/driver time, thereby promoting Accelerated Bridge Construction (ABC).]]></description>
      <pubDate>Tue, 02 Dec 2025 16:33:39 GMT</pubDate>
      <guid>https://rip.trb.org/View/2633318</guid>
    </item>
    <item>
      <title>Identifying the Underlying Reasons Driving the Shifts in Bridge Performance Rating Trends in Georgia with a Particular Focus on the Notable Inversion Observed since 2019
</title>
      <link>https://rip.trb.org/View/2589066</link>
      <description><![CDATA[
The objectives of this research are to (1) identify the underlying factors driving the shifts in bridge performance rating trends in Georgia since 2016, emphasizing the notable inversion observed from 2019, and (2) investigate opportunities for improving maintenance practices/policies, leading to enhanced bridge management strategies. ]]></description>
      <pubDate>Thu, 14 Aug 2025 14:29:51 GMT</pubDate>
      <guid>https://rip.trb.org/View/2589066</guid>
    </item>
    <item>
      <title>Instrumentation And Monitoring For G-Beam/Stillwater Avenue Bridge Replacement</title>
      <link>https://rip.trb.org/View/2582413</link>
      <description><![CDATA[In the proposed project, the research team plans to deploy an extensive instrumentation and communication system that will be embedded in the G-Beam girders proposed for the Stillwater Avenue bridge in Orono/Old Town.  Some of the details of the specific monitoring plan will need to be deferred to coincide with girder design.
The study will include the following. First, an array of fiber optic cabling will be installed along the longitudinal beam axis at different locations relative to the neutral axis.  Each cable will include discrete sensors at different locations along the beam axis to capture strain at those points.  Second, an array of accelerometers will be located it key locations in order to capture frequencies and modes of vibration during service.  Both the accelerometers and the fiber optic system will be connected to a communications network that both collects data from the sensor array and broadcasts the data over a wireless network to a server at University of Maine (UMaine).  Depending on collection rates, the data will either be transmitted over a conventional 5G cellular network, or more likely via a closed network that sends the data through a series of discrete repeaters in between the bridge site and the server.  Third, the team proposes a system of digital cameras that will be used both to trigger the acquisition and transmission system, but also through machine vision, be able to identify the vehicle type (e.g. number of axles.)  Once triggered, the array of strain gages and accelerometers, will preprocess data and send to the UMaine server.  In this way, resulting strain and vibration data can be tied to load types.  Fourth, a weather station will monitor current temperature, sunlight, and relative humidity data to complement the acquired structural data.  Depending on design issues, additional on-site sensors can monitor water level, ice status, and other environmental conditions that may be relevant. Finally, we will conduct diagnostic live load tests on the completed structure immediately before it is opened to traffic and approximately one year after its completion]]></description>
      <pubDate>Thu, 31 Jul 2025 14:23:33 GMT</pubDate>
      <guid>https://rip.trb.org/View/2582413</guid>
    </item>
    <item>
      <title>Agentic Artificial Intelligence Framework for Enabling Automation in Bridge Inventory Database Using Large Language Models</title>
      <link>https://rip.trb.org/View/2570732</link>
      <description><![CDATA[An ideal bridge inventory database is a structured, accessible repository of comprehensive information about bridges, such as their condition, inspection history, load capacities, design types, age, and other relevant attributes. Such database is essential to support data-informed and cost-effective bridge asset management and preservation. However, current practices for retrieving information/insights from and updating the databases lack automation, are slow and extremely expert-demanding. The increasing amount and the heterogeneous (multi-modal) nature of the data make it increasingly challenging to manually synthesize and distill useful insights from and/or updating the databases, calling for smart analytics technologies to automate the management, extraction, and interpretation of bridge inventory data. While large language models (LLMs) have shown the capability of comprehending multi-modal data, they remain significantly underutilized in bridge management. This project will investigate the viability of using LLMs to build artificial intelligence (AI) agents that can extract, memorize bridge condition from inspection records/reports, and enable standardized interpretation and organization of insights to support bridge preservation. The AI agents will convert raw and semi-structured bridge inventory data (e.g., inspection narratives, images, sensor signals) into structured database entries, summaries, and actionable recommendations. Users can interact intuitively with the AI agents via natural language queries, enabling efficient retrieval and interpretation of critical insights for bridge management. The agentic AI framework can achieve specified goals with minimal human/expert intervention.]]></description>
      <pubDate>Wed, 02 Jul 2025 12:02:53 GMT</pubDate>
      <guid>https://rip.trb.org/View/2570732</guid>
    </item>
    <item>
      <title>Synthesis of VDOT Historic Bridge Survey, Review, and Management Information</title>
      <link>https://rip.trb.org/View/2561782</link>
      <description><![CDATA[Since the 1970s, the Virginia Department of Transportation (VDOT), through the Virginia Transportation Research Council (VTRC) has conducted studies to manage its historically significant bridges as well as developing (and updating periodically) a statewide historic bridge management plan and conducting studies on rehabilitating and moving historic truss bridges, truss bridge eye bar deterioration, and feasibility of alternative uses.  Further, specific projects relating to individual bridges are covered by separate guidance documents, cultural resource reviews, Memoranda of Agreement (MOA), or by Programmatic Agreement (PA) documents.  These surveys, projects, and agreements are documented by various VTRC survey files, reports, and memos, as well as documents filed in VDOT district Environmental offices and VDOT Environmental Division files in VDOT's Central Office.  However, there is no one document containing this information.

In the late 2010s, VDOT’s Central Office cultural resource staff identified a need for a synthesis document that would consolidate information regarding historic bridges.  The lack of such a synthesis resulted in gaps in the cultural resource records regarding these bridges, which is problematic for newer VDOT personnel who periodically must put together information on the previous projects that have involved these bridges.  Phase I of this synthesis, collecting information from the initial and early VDOT/VTRC historic bridge projects (covering 1972-1993), was completed in 2022.  This Phase 2 will continue the collection of this information from 1993 to the present, such that this report will be a final synthesis report covering 1972 to the present.  This report will contain data on the cultural resource surveys, reviews, studies, management plans, published reports and agreements, including MOAs and PAs.  Because a VDOT-specific synthesis like this has not been published before, this report could be a model for future updates based on additional historic bridge surveys, reviews, and management projects. 
]]></description>
      <pubDate>Thu, 05 Jun 2025 10:34:33 GMT</pubDate>
      <guid>https://rip.trb.org/View/2561782</guid>
    </item>
    <item>
      <title>Evaluation of ITD’s Bridge Deck Preservation Strategies</title>
      <link>https://rip.trb.org/View/2560881</link>
      <description><![CDATA[This project will evaluate the Idaho Transportation Department’s (ITD) current bridge deck preservation strategies
and determine whether the ITD Bridge Section is using the most effective approaches for extending the lifespan of
bridge decks in Idaho. ITD currently relies on a range of preservation practices designed to extend bridge deck
service life and optimize the use of resources, but evolving construction methods and materials create
opportunities to improve durability, efficiency, and cost-effectiveness. The research will assess ITD’s existing
preservation practices and investigate alternative materials for new bridge deck overlays, particularly for use in
Accelerated Bridge Construction (ABC) applications. Potential alternatives will be assessed for their ability to
enhance preservation efforts for both new and existing decks, reduce time and costs, and match or exceed the
strength and performance of Polyester Polymer Concrete (PPC), which is commonly used today. Findings from this
project will guide updates to ITD’s preservation guidelines to create strategies that are cost-effective and
appropriate for Idaho’s climate, traffic, and infrastructure.]]></description>
      <pubDate>Tue, 03 Jun 2025 13:12:59 GMT</pubDate>
      <guid>https://rip.trb.org/View/2560881</guid>
    </item>
    <item>
      <title>RL-Empowered Optimizer for Bridge Fortification: A Novel Decision-Making Mechanism to Optimize Bridge Fortification in Disaster-Prone Communities</title>
      <link>https://rip.trb.org/View/2549031</link>
      <description><![CDATA[Effective operation of any society is heavily dependent on its critical infrastructures. Road infrastructure is an essential component of societies, facilitating access to different areas. This infrastructure is an integration of several modalities such as roads, highways, bridges, rails, and public transit. Among these modalities, bridges are the most vulnerable ones against disasters such as earthquakes, floods, hurricanes, and fires. This poses significant risks to the integrity and functionality of road infrastructure. Bridges have experienced the highest rates of damage during past disasters. For instance, the collapse of the I-95 bridge in Philadelphia, Pennsylvania, in June 2023, due to a tanker truck fire, disrupted transportation across a significant portion of the Eastern Seaboard, illustrating how critical this structure was in connecting the surrounding road network.]]></description>
      <pubDate>Sun, 04 May 2025 15:42:03 GMT</pubDate>
      <guid>https://rip.trb.org/View/2549031</guid>
    </item>
    <item>
      <title>Resilience Program: Preliminary Investigation of Bridge Scour Countermeasure and Mitigation Strategies in Virginia</title>
      <link>https://rip.trb.org/View/2536175</link>
      <description><![CDATA[Scour, the erosion of material from stream beds and banks due to flowing water, poses a significant threat to transportation infrastructure integrity, particularly around bridge piers and abutments, where it is a primary cause of damage and failure in bridges in the United States. While steady scour under typical flow conditions allows for monitoring and maintenance, rapid erosion during flood events can lead to infrastructure failure with limited warning. Despite extensive engineering-driven research, limited field data and focus on large bridges have left smaller infrastructures vulnerable. The Federal Highway Administration (FHWA) recommends monitoring and implementing scour countermeasures, but existing techniques lack standardized evaluation and approval. Variability in topography and geology across regions complicates the selection of effective countermeasures. The Virginia Department of Transportation (VDOT) faces increasing scour-related challenges but lacks specific procedures to address them. Therefore, a comprehensive study is crucial for VDOT to identify effective scour countermeasures adaptable to diverse topographies across Virginia's districts.]]></description>
      <pubDate>Thu, 10 Apr 2025 09:13:57 GMT</pubDate>
      <guid>https://rip.trb.org/View/2536175</guid>
    </item>
    <item>
      <title>A Comprehensive Guideline for GDOT Bridges Fire Hazard Assessment</title>
      <link>https://rip.trb.org/View/2508941</link>
      <description><![CDATA[The primary goal of this research is to develop a comprehensive guideline for the Georgia Department of Transportation (GDOT) to assess fire hazards in bridge structures.]]></description>
      <pubDate>Tue, 11 Feb 2025 16:02:30 GMT</pubDate>
      <guid>https://rip.trb.org/View/2508941</guid>
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