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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>IoT Sensor Fusion for Low-Cost Cloud Based Monitoring for Resilient Levees and Embankments</title>
      <link>https://rip.trb.org/View/2536170</link>
      <description><![CDATA[The performance and longevity of geo-infrastructure assets such as levees and highway embankments depend on geotechnical (embankment, foundations, slopes) components, both influenced by soil conditions, hydraulic loads, and disruptions due to weather. Continuous, data-driven monitoring is essential for reliable water resource management and disaster resilience. This research advances Geotechnical Asset Management (GAM) using advanced Internet of Things (IoT)-based inertial measurement unit (IMU) sensors installed onsite combined with periodic aerial LiDAR point-cloud data collection techniques. IoT-based IMU sensors will track multi-directional displacements, while accelerometers and vibration sensors will capture performance data under various conditions. An earth dam and highway embankment site in Jackson, Mississippi, and a Levee section owned by the United States Army Corps of Engineers (USACE) will serve as test locations. A 3D geospatial model combining drone-mounted LiDAR will track structural stability and environmental impacts. Periodic assessments will detect instability, settlement, and deformation, enabling proactive maintenance to prevent failures and minimize disruptions. Enhanced monitoring will ensure reliable, connected, and risk-mitigated infrastructure to support national economic competitiveness. Collected data will be transmitted to the Amazon Web Services (AWS) cloud for remote monitoring of the embankment, dam and levee system. In addition, the analytical tools in the cloud platform will be used to analyze the data and identify threshold points based on the performance criteria to create an early detection of failure under extreme conditions. This project will develop a data-driven, scalable solution to enhance safety, efficiency, and resilience in water management infrastructure while strengthening investments, thus enabling US economic strength and global competitiveness.]]></description>
      <pubDate>Wed, 09 Apr 2025 18:28:23 GMT</pubDate>
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      <title>Deep Learning with LiDAR Point Cloud Data for Automatic Roadway Health Monitoring</title>
      <link>https://rip.trb.org/View/2440049</link>
      <description><![CDATA[Traditional methods for monitoring road conditions are fraught with challenges. Field inspections are labor-intensive and costly, aerial photography is subjective, and mobile measurement systems (MMS) require substantial investment in geospatial technology. In response to these limitations, there is a growing interest in leveraging advanced 3D scanning technologies, such as LiDAR and RGB-D scanners, in conjunction with deep learning algorithms for infrastructure assessment. 3D point cloud data, analyzed through deep learning models, offers several advantages over traditional 2D-based computer vision techniques. These include enhanced spatial resolution, superior object recognition, and the ability to handle complex scenes more effectively. However, this approach also introduces challenges, such as greater computational demands and the need for specialized hardware. Therefore, albeit with the tremendous benefits associated with the 3D point cloud, there are very few studies dedicated to the application of 3D point cloud-based deep learning models to the infrastructure operation and assessment. To bridge this research gap, this study aims to investigate the efficacy of various point cloud-based deep learning models in automating roadway health assessments. 

Given the vital nature of this topic, the research will evaluate promising deep learning architectures, such as PointNet, PointNet++, 3D-CNNs, and PointCNN, using point cloud data gathered from multiple roadways in Southern California. Additionally, some typical technical challenges such as the noise filtering, data alignment, and dimension reduction via resampling, etc., will be further explored. This investigation aims to offer valuable insights into the pros and cons of these models under diverse conditions, thereby contributing to future research in this emerging area. Most importantly, these applications combine to offer a more comprehensive, real-time understanding of roadway health, facilitating proactive maintenance, reducing costs, and improving public safety
]]></description>
      <pubDate>Thu, 10 Oct 2024 16:28:20 GMT</pubDate>
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