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    <copyright>Copyright © 2026. National Academy of Sciences. All rights reserved.</copyright>
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    <managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor>
    <webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster>
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      <title>Modernizing Rockfall Assessment</title>
      <link>https://rip.trb.org/View/2698372</link>
      <description><![CDATA[Using targeted remote sensing and other advanced survey techniques, combined with data-driven analysis, this pilot study will evaluate how well these approaches can identify meaningful changes in slope conditions and determine whether rockfall material reaches the roadway or is effectively contained (e.g., within ditches). The results will help 
Montana Department of Transportation (MDT) improve the consistency of slope evaluation and prioritization of mitigation efforts, supporting more efficient use of maintenance resources, improved safety, and reduced traffic disruptions. The study will also provide insight into how repeated observations can be used to track changes in slope condition and performance over time, supporting long-term planning and asset management.]]></description>
      <pubDate>Fri, 01 May 2026 16:56:00 GMT</pubDate>
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      <title>Geotechnical Asset Management for Rock Slopes and Rockfall Risk Assessment</title>
      <link>https://rip.trb.org/View/2083799</link>
      <description><![CDATA[Idaho’s current rockfall management efforts vary among ITD Districts in the state and rely on subjective input like individual experience and institutional knowledge. The lack of standardization leads to challenges with risk assessment, project prioritization, and reactionary project implementation rather than mitigation planning and application of preventive measures. To improve safety and to more efficiently implement mitigation projects, research is needed to develop and execute a standardized system to manage rock slopes, assess risks associated with rock fall hazards, and to establish a clear means of documenting and communicating the locations of hazards along Idaho’s highway network.
The desired outcomes of this project are three-fold. First, to enhance safety on Idaho’s highways through the development of a standardized system to rate slope stability and rockfall risk. Second, prioritize slope stabilization projects through a consistent evaluation of the slopes and development of a database for streamlined production of custom reports and geographic information system (GIS) mapping. Lastly, implement research by providing and testing a training program on the slope rating system, data entry and management, and production of reports and maps.]]></description>
      <pubDate>Thu, 15 Dec 2022 11:09:16 GMT</pubDate>
      <guid>https://rip.trb.org/View/2083799</guid>
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      <title>Multimodal-AI based Roadway Hazard Identification and Warning using Onboard Smartphones with Cloud-based Fusion</title>
      <link>https://rip.trb.org/View/1923105</link>
      <description><![CDATA[Road hazard is one of the significant causes of fatality in road accidents. Accurate estimation of road hazards can ensure safety and enhance the driving experience. Existing methods of road condition monitoring are time-consuming, expensive, inefficient, require much human effort, and need to be regularly updated. There is a requirement for a flexible, cost-effective, and efficient process to detect road conditions, especially road hazards. In this study, we present a new method to deal with road hazards using smartphones. Since most of the population drives cars with smartphones onboard, we aim to leverage this
to detect road hazards in a more flexible, cost-effective, and efficient way. This study proposes a cloud based deep-learning road hazard detection model based on a Long-Short Term Memory network (LSTM) to detect different types of road hazards from motion data. To address the issue of large data requests for deep learning, this study proposes to fuse both simulation data and experimental data for the learning. The proposed approaches are validated by experimental tests, and the results demonstrate the accuracy of road hazard detection based on cloud-based fusion]]></description>
      <pubDate>Sun, 06 Mar 2022 15:14:49 GMT</pubDate>
      <guid>https://rip.trb.org/View/1923105</guid>
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      <title>Hazard Mitigation Indefinite Delivery/Indefinite Quantity (IDIQ)Contract
</title>
      <link>https://rip.trb.org/View/1370407</link>
      <description><![CDATA[The scope of this contract includes conducting research in the area of bridge vulnerabilities to single or multiple hazards, development of the mitigation and adaptation countermeasures including materials for existing and new structures, development of the next generation of analysis and design tools, and methodologies to address extreme events, assessment of the condition and health of bridge networks after extreme events, and materials research to develop resilient systems. Extreme event loads include both natural hazards, such as flooding, wind, and seismic; as well as manmade hazards, such as overloads, fire, impact forces, unintentional (explosion), or intentional blast events (terrorism).
]]></description>
      <pubDate>Mon, 28 Sep 2015 11:12:45 GMT</pubDate>
      <guid>https://rip.trb.org/View/1370407</guid>
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      <title>RFID Hazard Assessment &amp; Lithium Battery Packaging Performance</title>
      <link>https://rip.trb.org/View/1361057</link>
      <description><![CDATA[No summary provided.]]></description>
      <pubDate>Thu, 16 Jul 2015 01:00:28 GMT</pubDate>
      <guid>https://rip.trb.org/View/1361057</guid>
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