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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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    <item>
      <title>Development of design guidelines for protection against erosion at bridge piers of rectangular cross section and estimating effects of pressurized flow on erosion potential</title>
      <link>https://rip.trb.org/View/2706034</link>
      <description><![CDATA[Bridge piers are vulnerable to severe erosion (scour) during high-flow and flooding conditions, which can compromise structural stability and, in extreme cases, lead to bridge failure. Existing riprap design methodologies used to protect bridge piers have limitations, particularly for rectangular piers and for conditions in which bridge decks become submerged and flow transitions from open channel to pressurized regimes. Inadequate riprap sizing under such conditions increases risk of structural distress, traffic interruption, and potential safety hazards.
This project develops improved design guidelines for riprap protection at rectangular bridge piers under both open channel and pressurized flow conditions. Using validated three-dimensional numerical simulations, the research will quantify how pier geometry, aspect ratio, angle of attack, and flow regime influence critical shear stress and the Froude number associated with stone failure. The project will propose a multi-parameter riprap sizing formula applicable to a broader range of geometrical and hydraulic conditions, including overtopping scenarios. Recommendations will be provided for adapting existing HEC-18 methodologies to account for pressurized flow conditions at bridge sites.

]]></description>
      <pubDate>Sat, 23 May 2026 17:39:45 GMT</pubDate>
      <guid>https://rip.trb.org/View/2706034</guid>
    </item>
    <item>
      <title>Capital Investment, Financing, Flood Risk, and Transportation Safety in the MidAmerica Region
</title>
      <link>https://rip.trb.org/View/2706033</link>
      <description><![CDATA[Transportation agencies in the MidAmerica region face increasing pressure to manage aging infrastructure under fiscal constraints while improving transportation safety. Rural highways, freight-intensive corridors, and aging bridges experience elevated crash severity, yet capital investment timing and financing decisions are rarely evaluated through a safety-risk lens.
This project develops an integrated empirical and probabilistic framework to quantify how capital investment timing, financing mechanisms, and flood-related hazards influence lifecycle transportation safety outcomes. The study constructs project- and asset-level datasets linking capital programming records, delivery timelines, financing mechanisms, infrastructure characteristics, crash outcomes, and flood risk indicators. Econometric models estimate statistical relationships between investment timing and safety performance. Monte Carlo simulation propagates uncertainty in delivery delays, cost escalation, traffic growth, and flood exposure to produce distributions of lifecycle safety risk and cost. Results will support safety-oriented capital planning and risk-informed decision-making for transportation agencies in the MidAmerica region.

]]></description>
      <pubDate>Sat, 23 May 2026 17:36:26 GMT</pubDate>
      <guid>https://rip.trb.org/View/2706033</guid>
    </item>
    <item>
      <title>Framework for Identifying Shear Cracking in Bridge Columns Using LiDAR</title>
      <link>https://rip.trb.org/View/2633316</link>
      <description><![CDATA[The researchers will be testing bridge columns near the critical intersection of shear and flexural failure that aims to help the prioritization of bridges for seismic retrofit. That testing, along with the terrestrial lidar equipment housed at the UW’s RAPID Facility, offer an opportunity to explore the use of lidar to map cracks and subsequently use the crack information to correlate with failure modes and remaining capacity of reinforced concrete bridge components. There are several algorithms in the literature and available through open repositories such as GitHub that use lidar-derived point clouds to identify, map and measure cracks. Such algorithms will not need to be developed as part of this project, but instead the existing ones will be utilized to identify and characterize the cracks in the test columns. This project will focus on developing the link between the cracking (pattern and magnitude), the likely failure mode (shear or flexural), and the remaining capacity. Such information may be generalized to help inspectors understand what types of cracks are most problematic and might lead to deteriorated structural capacity. That information is relevant to inspection of both existing and new construction.]]></description>
      <pubDate>Tue, 02 Dec 2025 16:24:44 GMT</pubDate>
      <guid>https://rip.trb.org/View/2633316</guid>
    </item>
    <item>
      <title>Enhancing Structural Safety and Promoting Equity in Infrastructure Maintenance through Human-Centered Bridge Inspection empowered by Artificial Intelligence and Augmented Reality
</title>
      <link>https://rip.trb.org/View/2627937</link>
      <description><![CDATA[Bridges are crucial civil infrastructure, but their deterioration over time poses significant safety risks. Traditional human visual inspections are limited in accuracy and efficiency, leading to challenges in maintaining the inventory of bridges in the United States, particularly in economically disadvantaged communities. Leveraging recent advancements in computer vision (CV), artificial intelligence (AI), and augmented reality (AR), the team proposes a novel human-centered approach to enhance the accuracy and efficiency of concrete bridge inspections and promote equity in infrastructure maintenance. By automating detection and documentation of damage in concrete bridges, and empowering human inspectors by overlaying real-time detection results onto bridges thereby enabling human-machine collaboration, the project aims to improve inspection effectiveness and efficiency, promote equity in infrastructure maintenance, and enhance public safety.
]]></description>
      <pubDate>Fri, 21 Nov 2025 14:16:23 GMT</pubDate>
      <guid>https://rip.trb.org/View/2627937</guid>
    </item>
    <item>
      <title>Guidance for Identifying Nonredundant Steel Tension Members (NSTMs)
</title>
      <link>https://rip.trb.org/View/2508952</link>
      <description><![CDATA[This research directly addresses the challenges the Georgia Department of Transportation (GDOT) has faced regarding design engineers inaccurately over-specifying nonredundant steel tension members (NSTMs) on new steel bridge designs. The subsequent additional inspection requirements have resulted in increased costs, fabrication disputes, and project delays. As such, guidance is critical for practicing engineers to identify such members accurately. Further, the proposed analysis of an IRM or SRM offers GDOT significant financial, resource, and safety benefits.]]></description>
      <pubDate>Wed, 12 Feb 2025 07:10:10 GMT</pubDate>
      <guid>https://rip.trb.org/View/2508952</guid>
    </item>
    <item>
      <title>Advanced Bridge Technology Clearinghouse (ABTC) </title>
      <link>https://rip.trb.org/View/2475975</link>
      <description><![CDATA[The U.S. bridge infrastructure sector faces many critical challenges, including aging structures, growing traffic demands, and environmental impacts. With 42% of U.S. bridges over 40 years old and 7.5% classified as structurally deficient, there is an urgent need for innovative technologies to ensure long-term safety and sustainability. However, despite advancements in bridge technology, their effective dissemination and integration into practice remain significant hurdles. The Advanced Bridge Technology Clearinghouse (ABTC) program aims to bridge this gap by providing a clearinghouse to facilitate the adoption of cutting-edge solutions.
The ABTC is designed as a dynamic and secure hub for advancing bridge-related technologies. Its front-end platform will feature a user-friendly interface that integrates advanced capabilities such as metric-driven prioritization and artificial intelligence (AI)-powered approaches. These tools will enable users to explore, evaluate, and adopt technologies that prioritize sustainability, resilience, and safety, aligning with national priorities like climate change mitigation and economic viability. On the back end, a dedicated pool of experts from industry and academia will provide critical support for the evaluation framework, assessment, and effective implementation of the technologies. This dual structure aims to ensure that the ABTC platform functions as both a cutting-edge resource and a collaborative support system for the application of innovative solutions in the bridge sector.
The program plans to employ a multi-step approach that includes needs assessment, active user engagement, technology exploration, technology evaluations, platform development, and ongoing outreach.  Beyond being a repository of information, the ABTC program is set to become a driving force for transformation in bridge engineering. By advocating for technologies that minimize environmental impact and enhance durability, the platform contributes to a future where bridges are safer, more resilient, and environmentally sustainable.
]]></description>
      <pubDate>Fri, 13 Dec 2024 14:05:32 GMT</pubDate>
      <guid>https://rip.trb.org/View/2475975</guid>
    </item>
    <item>
      <title>Durability of Thermoplastic Rebars Used
in Reinforced Concrete Structures - (2.25)</title>
      <link>https://rip.trb.org/View/2410500</link>
      <description><![CDATA[The objective of this study is to study the durability of thermoplastic composite (TC) rebars. The end-use of thermoplastic rebars is as reinforcements in concrete structures. This research work focuses on the effects of moisture, alkaline exposure, and seawater wetting and drying cycles of thermoplastic rebars manufactured using CFM technology. Thermoplastic rebars will be subjected to accelerated ageing conditions at elevated temperatures. The change in mechanical properties of the thermoplastic rebars with time will be evaluated using tension and compression. In addition, changes in thermal and physical properties will be evaluated. The accelerated durability results will be summarized and used to develop a comprehensive approach to predicting long-term mechanical properties of the thermoplastic rebars for designing concrete structures.]]></description>
      <pubDate>Wed, 16 Oct 2024 15:21:38 GMT</pubDate>
      <guid>https://rip.trb.org/View/2410500</guid>
    </item>
    <item>
      <title>Time-dependent Durability of Composite-Repaired Bridge Columns</title>
      <link>https://rip.trb.org/View/2232156</link>
      <description><![CDATA[This proposal presents a comprehensive research program concerning the durability of field-assembled columns with accelerated bridge construction (ABC), including post-tensioned columns, when subjected to synergistic distress resulting from corrosion and seismic loadings. Although ABC is an emerging trend in the United States due to a number of advantages (e.g., minimal disruption to traffic and quality control), there is a lack of knowledge on the performance of corroded ABC columns and connection elements in earthquake-prone zones. Accordingly, no design provisions are available in published specifications. To address such a practical need, technical investigations are conducted through large-scale laboratory testing in conjunction with advanced analytical modeling. Of interest are the mechanisms of corrosion initiation and progression in ABC columns with and without posttensioning, hysteretic responses, ductility, structural vulnerability, failure probability, and the formation of plastic hinges. Upon elucidating the behavior of the columns, cost-effective retrofit strategies are established using non-corrosive carbon fiber reinforced polymer (CFRP) composites to extend the longevity of the deteriorated ABC systems. All findings will be integrated to develop implementation guidelines. Significant synergies are expected through the collaboration of the University of Utah and the University of Colorado Denver in terms of a scientific understanding of the subject area, educational activities, and technology transfer. Three engineers from the state Departments of Transportation (DOTs) participate in the research program to help generate practical outcomes, including two industry partners, which immediately benefit the infrastructure community. Conforming to the TriDurLE thrust areas, the project brings to light the state of the art of ABC technologies and provides opportunities to students from underrepresented groups.]]></description>
      <pubDate>Wed, 23 Aug 2023 20:59:07 GMT</pubDate>
      <guid>https://rip.trb.org/View/2232156</guid>
    </item>
    <item>
      <title>Concrete Bridge Engineering Institute (CBEI)</title>
      <link>https://rip.trb.org/View/2135507</link>
      <description><![CDATA[The overall objective of this pooled fund is to implement specific programs within CBEI that address national workforce training needs through research, development, and technology transfer activities. 

The specific objectives are to develop and implement the following programs with coordinated input of members of the pooled fund: Three initial specific training programs, a Concrete Solutions Center, and a Bridge Component Collection. The scope of each is further defined below. 

The technology transfer through training programs will draw on the latest technologies and provide an innovative approach by utilizing a hands-on intensive curriculum. The training programs will draw from the best, and most current, state of the art methods. CBEI will serve to continually gather emerging or underutilized technologies such as those above, and provide research, development, and technology transfer activities in partnership with the originators of the technology. This will result in training curricula and technology transfer documents for the concrete bridge workforce. Non-destructive Evaluation (NDE) techniques will be an overarching component included in each of the programs.  

]]></description>
      <pubDate>Thu, 09 Mar 2023 16:47:29 GMT</pubDate>
      <guid>https://rip.trb.org/View/2135507</guid>
    </item>
    <item>
      <title>Educating Professionals for Practice in Highway Bridge Engineering</title>
      <link>https://rip.trb.org/View/2096591</link>
      <description><![CDATA[This research will investigate innovative approaches to address the education gap for practicing bridge engineers.]]></description>
      <pubDate>Fri, 13 Jan 2023 14:49:30 GMT</pubDate>
      <guid>https://rip.trb.org/View/2096591</guid>
    </item>
    <item>
      <title>Determination of Bridge Element Weights based on Data-driven Models</title>
      <link>https://rip.trb.org/View/1907235</link>
      <description><![CDATA[This research aims to present data-driven models that determine bridge element weights towards overall bridge health, considering bridges in different operational and environmental conditions. The specific objectives for the research aim are: 1. Create bridge breakdown structures that define bridge elements in hierarchical structural relationships by bridge components 2. Analyze element condition data and National Bridge Inventory (NBI) condition rating data for deterioration curves at both levels of bridge elements and components 3. Apply a similarity measure to analyze the time-varying similarities of deterioration curves between a bridge element and its upper-level bridge component 4. Identify weight-changing factors that are hypothesized to change bridge element weights across bridges in different operational and environmental conditions and formulate their effects on individual bride element weights 5. Evaluate bridge element weights derived from this project]]></description>
      <pubDate>Wed, 02 Feb 2022 12:04:40 GMT</pubDate>
      <guid>https://rip.trb.org/View/1907235</guid>
    </item>
    <item>
      <title>Integrate infrastructure performance monitoring using automatic crack evaluation system and convolutional neural network</title>
      <link>https://rip.trb.org/View/1904953</link>
      <description><![CDATA[The overall goal of this study is to develop a framework integrating infrastructure performance evaluation leveraging advanced evaluation system so-called automatic crack evaluation system (ACES) and advanced machine learning (ML) techniques (e.g., convolutional neural network (CNN)), which ultimately enable reliable traffic disruption-free assessment, provide structural performance data incorporating with the damage, and help accurate prediction of structural damage with proper damage classification. The proper maintenance and operation of deteriorating infrastructures require timely detection, precise diagnosis, and accurate estimation of possible structural performance degradation induced by various damages. Current technologies have been developed to prevent time-consuming and labor-intensive field tests. However, most high-speed techniques still present practical challenges (1) still have some limitations in accuracy, sensitivity, and coverage by focusing on indirect response and external surface conditions, (2) not considering structural performances that are not readily available for engineers, decision-makers, and stakeholders.
To improve the current system, PI developed rapid damage inspection without interrupting traffic using an ACES with the current state bridge inspection project. The noncontact manner in the system enables faster, easier, and more accurate evaluations for improving timely maintenance. PI also has performed finite element (FE) analysis to simulate the real situation for damaged structure incorporating with damage index map obtained from ACES. Unfortunately, modeling of the damage simulation will be a challenge with tremendous efforts for each bridge, while ACES will provide quick and real-time internal damage. To address the challenges of developing both disruption-free damage inspection and effective integration of structural performance with their data results from FE simulation and ACES will be analyzed by ML technique including CNN, which is an artificial neural network that has so far been the most advanced and popularly used for analyzing images for efficient infrastructure maintenance. After fundamental validation of cracks on bridge decks with experiment, numerical simulation, and ML, further simulation and ML model study will be conducted with damages in other critical structural members to provide the comprehensive prediction models in different ML features by defects.
The primary objectives of the proposal are (1) to improve nondestructive testing (NDT)  systems for structural health monitoring (SHM) by using ACES with the high-speed reference-free damage detecting system and state-of-art signal processing algorithms to enhance damage recognition capability and speed, (2) to perform FE modeling for an efficient structural performance incorporating ACES data; and (3) to develop a CNN framework that provides a quick decision of its structure performance to make reliable asset management decisions.
The significant outcome is to develop a highly reliable and efficient NDT system leveraging state-of-the-art prediction models for the improvements of infrastructure maintenance. If the proposed objectives and outcomes are successfully achieved, after the deep-learning training of FE results with damage location, CNN help to understand and provide a quick solution of total infrastructure evaluation effectively. Ultimately, the proposed systems will, in turn, provide an effective monitoring system and ideal asset management strategies to extend the service life of the transportation infrastructure. Thus, the study will meet transportation departments’ goals and objectives for transportation safety in urban and rural areas. The technology can be applicable for other data analysis or classification problems, as well as a specialization for being able to pick out or detect patterns by CNN. This cutting-edge neural network technique has proven its statistical power in identifying the unknown state of objects, including image classification and natural language process.
The project will also carry out the Trans-SET missions by performing research, technology transfer, education, workforce development, and outreach activities to solve transportation challenges in Region 6.]]></description>
      <pubDate>Thu, 20 Jan 2022 13:56:18 GMT</pubDate>
      <guid>https://rip.trb.org/View/1904953</guid>
    </item>
    <item>
      <title>A Bridge Digital Twin for Enhancing Transportation Resilience and Asset Management</title>
      <link>https://rip.trb.org/View/1902374</link>
      <description><![CDATA[As the Region 6’s transportation network exponentially grows, each DOT requires seamless collaboration of relevant stakeholders with bridge infrastructure construction and maintenance data organized in an integrated, safe, trusted, and interoperable manner. Since bridges encompass several vulnerable components, they should be carefully managed, maintained, and
monitored by DOTs in each state. The Federal Highway Administration (FHWA), according to
Federal-Aid Highway Act of 1968, requires all states to perform a biennial inspection for each
bridge to document its condition for maintaining, repairing, and rehabilitating bridges. The
Louisiana Department of Transportation & Development (LaDOTD) performs inspections on
nearly 13,000 bridges at least every two years including 16,387,706 square feet of bridge deck,
which ranks 4th in total bridge area in a nation. However, state DOTs have separately stored
and managed the data of numerous bridges as engineering design/drawing information, asset
maintenance data, and field inspection data. The primary issue is that these data and database
are not consistently connected and linked. 
Currently, since the majority of bridge inspection methods use printed checklists, their 
interpretation is labor intensive, subject to personal judgment, and prone to error. In addition,
because of a large number of bridges and consequently enormous amount of maintenance data
generated by periodic inspections, it is highly possible to encounter data loss and sparse data
management. In addition, even though the bridge management system (BMS), which helps
manage bridge design, construction, and maintenance data, has been widely used in DOTs, the
systems of Region 6 DOTs have heterogeneous bridge data formats and information structures
that prevent seamless collaboration and data sharing, providing the insufficient capability to fully
integrate and exchange bridge asset and maintenance data. This challenge has been caused
by the lack of integrated digital systems for integrating all bridge facility management data.
The primary objective of this proposed project is to explore and develop a digital twin prototype
for bridge management. Bridge Information Modeling (BrIM) is the specialization of BIM to
bridge projects, but its use in transportation infrastructure is severely limited due to the lack of
standardization. Recently, there are an extensive effort such as the AASHTO Bridge Modeler
from different entities to develop, implement, standardize, and demonstrate an efficient and
robust digital data exchange protocol that could be used to digitally describe bridge engineering
information. If successful, this research outcomes will bring the new scientific knowledge on the
implications of the digital twin technology that bridge infrastructure and maintenance data using
the latest BrIM technology can be accumulated, managed, and analyzed in an integrated
platform. The revealed data exchange processes and their requirements of transportation
construction and maintenance can register new theoretical milestones in construction
management, infrastructure maintenance, and information science. In addition, this project will
provide the research community with the first bridge component detection method that is
capable of automatically creating as-built BrIM models from terrestrial laser scanning data.
Since bridge infrastructure systems are stretched over Region 6 areas, their integrated bridge
maintenance and inspection data management will be an invaluable asset for Region 6. In
addition, DOTs are responsible for performing inspections on all of all bridges at least every two
years, or more frequently if deemed necessary. If successful, the results from this study will
assist on the bridge inspection process conducted at least every two years, by reducing manual
work to save inspection time and cost, which will assure the timely inspection of the bridges
within Region 6 in case of severe broad damages caused by natural disaster.
]]></description>
      <pubDate>Mon, 10 Jan 2022 14:38:25 GMT</pubDate>
      <guid>https://rip.trb.org/View/1902374</guid>
    </item>
    <item>
      <title>Center for the Aging Infrastructure: Steel Bridge Research, Inspection, Training and Education Engineering Center - SBRITE (Continuation)</title>
      <link>https://rip.trb.org/View/1892746</link>
      <description><![CDATA[The objective of the proposal is to request a continuation of SPR-5(281) the Steel Bridge Research, Inspection, Training, and Education Engineering Center (S-BRITE Engineering Center) focused on existing steel highway bridges. This National Center when initially proposed in 2013, has become a national Center leading education, training, research, and engineering benefitting the existing aging steel bridge and structure inventory. Over the life of the project, ten (10) states, the US Army Corps, and the Federal Highway Administration (FHWA) have provided support through TPF-5(281) and continue to do so. Current funding is very strong and partner states continue to be added. Although the Center has been focused on highway bridges, it will also support stakeholders of steel railroad bridges as well as steel ancillary structures, such as lighting towers and sign supports. As a result, in-kind support from the railway industry has been strong as well. The Center has contributed to improved asset management decisions for state departments of transportation (DOTs), FHWA, and other partners relative to existing steel bridge inventory. However, since the existing TPF-5(281) needs to sunset per FHWA guidelines, the Research Team, and the current active partners are requesting a continuation of this pooled fund study, albeit under a different TPF number. The original project objectives and deliverables remain unchanged.]]></description>
      <pubDate>Wed, 17 Nov 2021 14:16:22 GMT</pubDate>
      <guid>https://rip.trb.org/View/1892746</guid>
    </item>
    <item>
      <title>Automated Data and Feature Extraction from Bridge Plan</title>
      <link>https://rip.trb.org/View/1865405</link>
      <description><![CDATA[Machine learning (ML) and artificial intelligence (AI) have significantly impacted numerous fields through their ability to tackle challenges with remarkable computational efficiency. In bridge engineering, ML/AI techniques have been employed to enhance the efficiency of the structural design phase, aid in the selection of optimal bridge types, produce cost estimates, conduct real-time structural health monitoring, predict structural response and deterioration, reconstruct data for comprehensive health assessment, and prioritize maintenance efforts. This research project applied ML/AI techniques to automate the process of extracting data and features from drawings, tables, and text blocks contained in bridge plan sets using state-of-the-art computational algorithms. The research was motivated by the critical need to report bridge inventory information to the Federal Highway Administration (FHWA) in compliance with National Bridge Inspection Standards (NBIS) reporting requirements. This research project produced a novel platform that automates the process of reviewing bridge plans to identify, extract, and report select engineering details. While the automated extraction of details from engineering documents can be a complicated task for machines due to the complex nature of plan sets, a combination of several deep learning models and various image processing techniques provided a platform to successfully extract details of interest. Furthermore, using the general models and functions developed in this research, the platform can be customized for different transportation agencies, following their formats and practices to capture bridge details available in their plan sets. ]]></description>
      <pubDate>Wed, 14 Jul 2021 17:47:46 GMT</pubDate>
      <guid>https://rip.trb.org/View/1865405</guid>
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