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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>Subsurface Seismic Imaging Using Full-Waveform Inversion and Physics-Informed Neural Networks</title>
      <link>https://rip.trb.org/View/2387190</link>
      <description><![CDATA[Roadway subsidence presents a significant challenge in the maintenance and safety of transportation infrastructure. This localized downward movement of the ground surface is largely due to buried low-velocity anomalies, such as highly compressible soft clay or loose sand zones, voids, and abandoned mine workings. Subsidence not only compromises the integrity of the road surface but also poses a considerable risk to the safety of the traveling. The ability to effectively assess and address this geohazard is, therefore, a crucial aspect of transportation system management. The early identification of subsurface anomalies is key to mitigating risks associated with roadway subsidence. By detecting potential hazards before they manifest as surface deformations, remedial actions can be undertaken to prevent extensive damage or catastrophic collapse of the roadway. This proactive approach to roadway maintenance ensures the continuous safety and efficiency of transportation routes, thereby minimizing disruptions and potential hazards to the public. The overall objective of this research is to integrate Physics-Informed Neural Networks with full-waveform inversion to solve the elastic wave equation in heterogeneous geomaterials and invert subsurface low-velocity anomalies.]]></description>
      <pubDate>Tue, 04 Jun 2024 14:11:56 GMT</pubDate>
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      <title>Development of Three-Dimensional, Full Wave Field Seismic Imaging Technology for Transportation Infrastructure Projects</title>
      <link>https://rip.trb.org/View/1228261</link>
      <description><![CDATA[Three-dimensional (3-D) characterization of near-surface material properties is of paramount importance to transportation infrastructure projects. Conventional seismic methods are labor intensive which makes shallow 3-D imaging uneconomical. Automated 3-D seismic surveying technology, the "autojuggie," has been developed at the University of Kansas for efficient high-resolution ultra-shallow imaging of geologic materials. The proposed research will further develop the autojuggie for automated acquisition of 3-D seismic over paved surfaces and for three-component (3-C) recording. Three-component acquisition of the full seismic wavefield will allow for determination of material mechanical properties. Combined with high-resolution 3-D imaging of bedrock topography, fracture density and orientation, and void detection, it will facilitate transportation infrastructure projects. The new technology of automated 3-D&amp;3-C seismic imaging will be tested at a Kansas transportation site. This project will be used to initiate research in transportation infrastructure, levee monitoring, and void and tunnel detection with state, federal and private organizations.]]></description>
      <pubDate>Thu, 03 Jan 2013 13:17:37 GMT</pubDate>
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