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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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      <title>Revolutionizing Coastal Infrastructure Durability with Pervious Concrete: A Cost-Effective, High-Performance Seawall</title>
      <link>https://rip.trb.org/View/2696019</link>
      <description><![CDATA[This project develops and validates a pervious concrete seawall system to reduce wave loads and mitigate scour-related degradation at lower cost and maintenance demand. The work integrates (i) high-fidelity finite element analysis for preliminary design, (ii) fabrication of pervious concrete with tuned porosity (15–35%) using durability-enhancing binders and engineered biochar, (iii) controlled wave flume experiments with instrumented specimens and backfill monitoring, and (iv) seawall design optimization accelerated by surrogate model and genetic algorithm.
To achieve the above mentioned integration, the research will proceed through a series of coordinated actions. First, the research team will build a high-fidelity finite element model, analyze the wave load in seawall, and achieve a preliminary design. Next, pervious concrete specimens with controlled porosity will be fabricated using the preliminary design and tested in a wave flume, which simulates real coastal conditions by generating programmable waves and measuring forces, displacements, and backfill scour behind the seawall. Finally, the team will apply a HyperNetwork, a neural architecture that dynamically generates predictive models, to estimate performance metrics such as energy dissipation and structural stability across different design configurations. The research team has rich experience in developing surrogate models for engineering applications and will complete building this HyperNetwork-based surrogate model in six months. This HyperNetwork will be used together with a genetic algorithm to search for Pareto-optimal designs that balance durability, hydraulic efficiency, and cost. This integrated approach ties together physical testing and advanced modeling to deliver practical, field-ready guidance with the objective of reducing wave-driven degradation and improving structural resilience in simple, cost-effective terms.
]]></description>
      <pubDate>Thu, 23 Apr 2026 16:44:25 GMT</pubDate>
      <guid>https://rip.trb.org/View/2696019</guid>
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      <title>A Dynamic Hurricane Risk Modeling Framework to Improve Bridge Safety under Changing Climate</title>
      <link>https://rip.trb.org/View/1945928</link>
      <description><![CDATA[Coastal regions have been experiencing more frequent and more intensive tropical cyclones (TC) due to climate change in recent years. In 2020, the tropical storms in the Atlantic Ocean made a number record in a season, with 30 named storms in total, 13 of which progressed into hurricanes. Global warming will continue and climate change will follow (USGCRP, 2018), leading to more severe winds and storms and threating the safety of bridges in coastal regions. In order for local governments to take pro-active adaptations and measures, it is essential to understand the local impact of global climate change. To address this, this project will develop a new, efficient hurricane wind model and then develop a new, dynamic hurricane risk modeling framework that can reflect climate change. This will inform decision-makers when they develop near-term measures and long-term plans for mitigation and adaptation to climate change. To achieve this research goal, the following two research tasks have been planned. First, by balancing the advantages and disadvantages of existing parametric TC models for engineering applications, this project will develop a high-fidelity, computationally efficient three-dimensional nonlinear TC model that can consider the varying land cover and terrains without too much simplification of the kinetic equations. Second, the developed hurricane wind model will be used to generate a great number of synthetic hurricanes to develop a hurricane risk model that can reflect the changing climate. The obtained results can be used to improve the American Association of State Highway and Transportation Officials (AASHTO) Bridge Design Specifications periodically to accommodate the future climate change, enhancing the resilience of bridges.]]></description>
      <pubDate>Sat, 30 Apr 2022 11:46:47 GMT</pubDate>
      <guid>https://rip.trb.org/View/1945928</guid>
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    <item>
      <title>Understanding of Bridge Vulnerability to Climate Change Enables Pro-active Adaptation Measures</title>
      <link>https://rip.trb.org/View/1762378</link>
      <description><![CDATA[In the past few decades, climate change has been leading to more severe extreme weather (e.g., hurricanes and heat waves), quicker sea-level rise, and more frequent flooding in coastal regions. Bridges in coastal regions are vulnerable to hurricanes, sea-level rise, and flooding. To mitigate these threats, to increase the resilience of bridges, and to take pro-active adaptation measures, the overarching goal of this research project is to understand the vulnerability of highway bridges to climate change. This will inform decision-makers when they develop near-term measures and long-term plans for mitigation and adaptation to climate change. To achieve this research goal, the following three research tasks have been planned: (1) Investigate all actions of a hurricane on a highway bridge by including waves, winds and water in the computational domain through multi-phase multi-physics computational fluid dynamics
(CFD) simulations, with the consideration of wind-wave interaction; (2) Determining the failure modes of the bridge system by considering loading induced by all factors, including wind pressure from winds, wave surge from waves, and varying hydrostatic force from flooding; and (3) Model structural vulnerability of bridges with sufficient spatial and temporal resolution by considering future climate change. The obtained results can be used to improve the American Association of State Highway and Transportation Officials (AASHTO) code periodically to accommodate the future climate change, enhancing the resilience of bridges.]]></description>
      <pubDate>Thu, 07 Jan 2021 13:44:38 GMT</pubDate>
      <guid>https://rip.trb.org/View/1762378</guid>
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