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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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      <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>
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    <item>
      <title>A Framework for Rapid Performance Assessment of Bridges Under Flood Hazard Using Machine Learning </title>
      <link>https://rip.trb.org/View/2646940</link>
      <description><![CDATA[Recent heavy rainfall and flood events highlight the growing threat to transportation infrastructure and their surrounding communities. Floods can severely damage bridges through pier scour, hydraulic loads, or debris impact. These effects may lead to sudden failures and impede critical emergency response and recovery operations. Traditional flood risk assessment methods often rely on expert opinion or visual inspection results, which may not accurately capture the true structural condition. Additionally, conventional stochastic approaches, while more robust, are computationally expensive and impractical for time-sensitive applications. To address these challenges, the proposed research develops a framework for rapid fragility quantification of bridges under flood conditions. The framework utilizes machine learning models that are trained to capture complex flood-structure interactions and generate predictive fragility curves for failure assessment and/or risk-based decision-making. The framework considers flood and bridge attributes to generate the fragility profile directly in real-time. Accordingly, this approach enables faster, more reliable, and computationally efficient assessment compared to traditional methods. Rather than focusing on a single bridge, the framework will be trained using high-fidelity finite element analysis results from an ensemble of models that covers a particular class of bridges. After training, the framework can generate the fragility profiles of any bridge within the class given the bridge attributes (e.g., span length, number of lanes, foundation type, etc.). At this stage, focus will be placed on single- or multi-span, simply supported girder bridges (steel or concrete). Other bridge types may be considered in subsequent studies. ]]></description>
      <pubDate>Mon, 05 Jan 2026 22:41:52 GMT</pubDate>
      <guid>https://rip.trb.org/View/2646940</guid>
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    <item>
      <title>Development of new design guidelines for protection against erosion at bridge piers and estimating effects of pressurized flow on erosion potential
</title>
      <link>https://rip.trb.org/View/2627350</link>
      <description><![CDATA[Addressing flood-induced erosion problems at bridges is critical to maintain the safety of the transportation infrastructure. Better design of scour prevention measures will result in less failure of bridges during natural disasters. A numerically-based approach will be used to propose a new design formula for determining minimum riprap stone size needed for riprap apron protection against erosion at circular, rectangular and oblong bridge piers. The proposed approach was already validated for abutments. The flow fields predicted using fully 3-D RANS simulations will be used to estimate the maximum bed shear stress over the riprap layer and the critical Froude number corresponding to the shear-failure entrainment threshold for the riprap stone. A comprehensive parametric study will be conducted to understand how pier shape and aspect ratio influence the peak shear stress over the riprap region. Results will be compared with those given by present formulas including by those recommended by HEC-18. A new multi-parameter design formula that incorporates the effect of pier shape and aspect ratio will be developed. The research also aims to develop procedures for riprap sizing at bridge piers under pressurized flow conditions due to bridge deck overtopping at high flow conditions. Simulations will be conducted to understand how the critical Froude number varies with increasing flow depth in between open-channel and pressurized flow conditions at the bridge. Recommendations will be made on how to use the design formula developed for open channel flow regime for cases when the flow at the bridge site is pressurized.
]]></description>
      <pubDate>Wed, 19 Nov 2025 14:27:06 GMT</pubDate>
      <guid>https://rip.trb.org/View/2627350</guid>
    </item>
    <item>
      <title>Resiliency Assessment of Flood Damage in Pavements</title>
      <link>https://rip.trb.org/View/2534020</link>
      <description><![CDATA[There is an ongoing need to understand the impacts of flood inundation on pavement performance throughout the United States. This is especially true in coastal states like Virginia that have robust riverine systems that are subject to flooding after heavy rainfall events. This study seeks to identify flood susceptible areas through ongoing research efforts at Virginia Department of Transportation (VDOT) and document the pavement condition before flooding and after flooding. A quantification of pavement damage in terms of layer structural capacity and visual distress due to flooding will be performed, along with an identification of specific factors that influence the pavement damage. Inexpensive moisture sensors next to the roadway will be employed to understand the drainage characteristics of the pavement system as to connect the structural performance and eventual surface distresses. The outcome of this research is an understanding of the impact of flooding on VDOT’s roads and the potential impacts on maintenance and rehabilitation schedules. This will potentially benefit VDOT as it evaluates different pavement adaptation options for flooding and future budgets to accommodate shifting maintenance and rehabilitation schedules.]]></description>
      <pubDate>Thu, 03 Apr 2025 08:54:58 GMT</pubDate>
      <guid>https://rip.trb.org/View/2534020</guid>
    </item>
    <item>
      <title>Addressing Flood Impact for Enhanced Resilience of Pavement Systems in Tribal Nations</title>
      <link>https://rip.trb.org/View/2480325</link>
      <description><![CDATA[Flooding represents a growing threat to the performance and longevity of pavement systems, particularly in areas with limited resources and infrastructure challenges, such as tribal nations. Pavements exposed to floods experience a range of detrimental effects, including soil erosion, reduced load-bearing capacity, increased maintenance costs, and diminished service life. This project aims to develop comprehensive strategies and tools to enhance the design and resilience of pavements subjected to frequent flooding events, especially in tribal communities.
The proposed research focuses on understanding the mechanisms of flood-induced pavement damage and improving adaptation and mitigation strategies for flood-prone areas. To achieve these objectives, the project will develop a systematic process for assessing the impacts of flooding on pavement systems, along with a guideline that integrates these effects into pavement design practices using AASHTOWare Pavement ME Design (PMED). The outcomes of this research will help transportation agencies and practitioners quantify flood impacts, design resilient pavements, and incorporate these strategies into long-term planning. The research goals will be accomplished through the following tasks. Task 1: Conduct a comprehensive literature review to identify the critical factors that affect the performance of pavements exposed to flood conditions. Task 2: Identify significant factors affecting pavement resilience through data collection, stakeholder surveys, and case studies selected from tribal and adjacent regions in Oklahoma. Task 3: Investigate the impacts of moisture on material properties to study how moisture impacts roadway material properties, focusing on resilient modulus adjustments and temporal effects using data from ODOT and LTPP-SMP. Task 4: Collect field performance data to analyze pavement performance data to compare flood and non-flood conditions and predict performance deterioration trends. Task 5: Perform structural analysis of flooded pavement performance to use structural simulations and moisture data to evaluate flood impacts on pavement performance and optimize traffic reopening decisions. Task 6: Develop a risk assessment framework and a pavement resilience index, which will assist inform decision-making and planning processes to enhance pavement systems' resilience to future flood events.
]]></description>
      <pubDate>Wed, 01 Jan 2025 13:39:54 GMT</pubDate>
      <guid>https://rip.trb.org/View/2480325</guid>
    </item>
    <item>
      <title>Flooded Pavements Assessment App–Phase 2</title>
      <link>https://rip.trb.org/View/1899553</link>
      <description><![CDATA[The Flooded Pavements Assessment App–Phase II project aims to enhance and amplify the recently developed post-flooding roadway assessment. More specifically, the following goals are targeted: (1) improving the user-interface, adaptive functionality such as interacting with pavement analysis software and more diverse material and load options, and computational efficiency of the application; (2) validate this mechanistically programmed application using scaled physical modeling, advance numerical simulation, and in-situ field data. All versions of application would be available for use with corresponding technical and user manual.]]></description>
      <pubDate>Wed, 22 Dec 2021 10:12:03 GMT</pubDate>
      <guid>https://rip.trb.org/View/1899553</guid>
    </item>
    <item>
      <title>Development of a Roadway Flood Severity Index</title>
      <link>https://rip.trb.org/View/1869811</link>
      <description><![CDATA[This study develops a retrospective Roadway Flood Severity Index (RFSI) capable of integrating geo-located hydrometeorological data and roadway information across localized and multi-state, sub-national regions in order to (1) categorize larger-scale, flood-related transportation disruptions, (2) understand the origins of those disruptions, and (3) identify severity risk levels of individual road segments and broader regions of transportation disruption during flood events. The fundamental question is, as flooding events unfold, can past hydrometeorological inundation information be coupled with transportation system network and mobility data to identify the most vulnerable roadway segments and regions? To address this question, the following tasks are in progress: (1) An ex-post facto analysis of historical flood events and the disruptions that they caused to multi-state, sub-national transportation mobility, focusing on how distinct flood types impact the road network and if any other compounding characteristics (e.g., regional variations, types of flooding, road-specific information) can be identified; (2) A mathematical, machine-learning based RFSI to highlight road segment flood risk for two multi-state regions; and (3) Development of a minimally-viable visualization tool to disseminate RFSI outputs with an emphasis on promotion of data interoperability.]]></description>
      <pubDate>Sat, 31 Jul 2021 22:29:33 GMT</pubDate>
      <guid>https://rip.trb.org/View/1869811</guid>
    </item>
    <item>
      <title>Projected Changes in Flood Peak Discharge Across Iowa: A Flood Frequency Perspective</title>
      <link>https://rip.trb.org/View/1696732</link>
      <description><![CDATA[Numerous modeling studies point to an intensification of the hydrological cycle under projected climate warming, with increasing frequency of extreme events, including heavy rainfall and flooding. Recently, the occurrence of extreme flooding has becoming the norm rather than the exception, with the 2008 Eastern Iowa flood representing the “poster child” for this catastrophic situation: during this event, for instance, the eastern half of our state experienced the closure of a number of roads, including Interstate 80. So, what would projected changes in flooding mean for the Iowa Department of Transportation (IDOT) and the bridges and structures that constitute Iowa’s highway system? How resilient are these highway structures to different climate warming scenarios?
Addressing these questions requires flood frequency analysis. The current methodology relies on the guidelines by Bulletin 17C. However, issues related to regionalization of at-site estimates as well as accounting for the projected changes in the climate system have received little attention despite the potentially large impacts, including to the IDOT’s infrastructure.
The proposed approach builds on methodologies and datasets with which the research team have extensive experience. Specifically, the proposed work focuses on the examination of the projected changes in flooding across Iowa using two complementary approaches: one based on the hydrologic model developed by the Iowa Flood Center (IFC), and one based on the statistical relationship between flooding and climate drivers. The focus will be on high-resolution and downscaled outputs from CMIP5 (Fifth Coupled Model Intercomparison Project) and CMIP6 (Sixth Coupled Model Intercomparison Project), and different scenarios.]]></description>
      <pubDate>Tue, 07 Apr 2020 14:00:18 GMT</pubDate>
      <guid>https://rip.trb.org/View/1696732</guid>
    </item>
    <item>
      <title>Holistic Network-level Assessment of Pavement Flood Damages using the FEMA's Hazus Flood Models and Maintenance Cost Prediction</title>
      <link>https://rip.trb.org/View/1642185</link>
      <description><![CDATA[After recent catastrophic disasters, roadways in the South-Central region suffer not only from the flood inundation, but also from the long-term recovery processes that incur enormous maintenance costs. To assess the impacts of flooding disasters on roadways, various studies have investigated sampled roadway damages with pavement engineering techniques such as a direct damage analysis using cores/bores. However, current methods for evaluating roadway damages are time-consuming and labor-intensive. In addition, even though existing methods provide a detailed damage analysis of pavement in a particular location for a particular time period, there is still a large practical knowledge gap in understanding network-level roadway functional/structural damages before-and-after historic flooding as well as assessing flooding impacts on roadways over time. 
The primary objective of this project is to develop a holistic roadway damage assessment method using the FEMA’s Hazus flood models and the pavement condition data accumulated over the years. This project also aims to provide a means for Louisiana and Texas to intuitively identify roadway damage patterns at the network level caused by flooding over time as well as accurately predict roadway maintenance cost. Research objectives will be achieved through the completion of the following tasks: (1) Investigate all roadways in Louisiana and Texas damaged by previous flood disasters using the FEMA’s Hazus models of previous flooding and hurricane events as well as the Base Flood Elevation (BFE) which is the elevation with 100-year flood; (2) analyze pavement assessment data obtained from the Pavement Management System (PMS) in the Louisiana Department of Transportation and Development (LaDOTD) as well as the Pavement Analyst in the Texas Department of Transportation (TxDOT); (3) incorporate pavement condition data into the Hazus flood model in GIS; (4) evaluate network-level functional and structural pavement damages; (5) develop damage pattern detection and spatial clustering models that indicate space-time pavement damage trends after flooding events; (6) build a prediction model of post-flood roadway maintenance cost and a quantifying model of future maintenance costs of flood damaged roadways; and (7) assess pavement damage patterns by comparing with PMS and Pavement Analyst data.]]></description>
      <pubDate>Fri, 02 Aug 2019 06:49:25 GMT</pubDate>
      <guid>https://rip.trb.org/View/1642185</guid>
    </item>
    <item>
      <title>Operation and Maintenance of a Statewide Crest-Stage Stream Gauging Network in Ohio</title>
      <link>https://rip.trb.org/View/1253393</link>
      <description><![CDATA[Flood magnitude and frequency data are not available for many stream sites in Ohio.  Floods cause serious damage to private property as well as public buildings and highways every year.  Floods also pose a risk of personal injury and death.  Further knowledge of the magnitude and frequency of flooding could be used to reduce the risk associated with flooding.  The objective of this project is to collect additional flood data at selected stream sites throughout Ohio.]]></description>
      <pubDate>Sat, 22 Jun 2013 01:02:53 GMT</pubDate>
      <guid>https://rip.trb.org/View/1253393</guid>
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