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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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      <link>https://rip.trb.org/</link>
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    <item>
      <title>A Photogrammetry-based Method to Determine Chip Seal Aggregate Embedment: A Feasibility Study
</title>
      <link>https://rip.trb.org/View/2627511</link>
      <description><![CDATA[Chip seals have been the most frequently used preventive maintenance treatment on flexible pavements in the United States and overseas. Chip seals enhance transportation safety through 1) sealing small cracks, 2) reducing further oxidation of the pavement, 3) improving surface texture and skid resistance, 4) preserving and extending pavement life, and 5) providing color contrast and noise differences. Despite the growing number of chip seal projects in the U.S., many chip seal surface distresses such as aggregate loss, bleeding, and skid resistance still need to be solved, which are strongly related to aggregate embedment depth. Proper chip seal aggregate embedment should be evaluated as a critical factor when considering the design and construction of a chip seal project. In addition, road users are rapidly becoming less tolerant of travel delays caused by road works, so the research will benefit road users substantially by reducing the time involved in measuring the texture of existing surfaces. However, currently there is no reliable method to measure chip seal aggregate embedment quickly and accurately in the field. The objective of this study is to develop a photogrammetry-based method to rapidly determine the embedment depth of a uniformly placed chip seal of known aggregate gradation, easy to use, reasonably accurate, and inexpensive. The study will start with laboratory explorations with a photogrammetry-based method to measure the emulsion/binder application rate, final cover aggregate rate, and aggregate embedment. The effect of design factors (i.e., binder type, application rate, aggregate size, shape, and gradation) will be assessed as well. 
]]></description>
      <pubDate>Thu, 20 Nov 2025 16:29:52 GMT</pubDate>
      <guid>https://rip.trb.org/View/2627511</guid>
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    <item>
      <title>Measuring Rebound and Evaluating Pile Resistance During Installation Using Ultra-High Speed/Resolution Photogrammetry</title>
      <link>https://rip.trb.org/View/2553997</link>
      <description><![CDATA[The primary objectives of the study are: (1) provide equipment capable of monitoring pile rebound accurately, improving estimates of pile resistance and increasing safety during pile driving operations, and (2) evaluate the possibility of estimating pile resistance using high-speed cameras.]]></description>
      <pubDate>Fri, 16 May 2025 07:26:56 GMT</pubDate>
      <guid>https://rip.trb.org/View/2553997</guid>
    </item>
    <item>
      <title>Failure Surface Identification, Structural Domain Segregation, and Rock Mass Characterization Using Drone-Based Structure from Motion Photogrammetry (UTI-UTC 16)
</title>
      <link>https://rip.trb.org/View/2543411</link>
      <description><![CDATA[This project aims to improve geotechnical characterization of rock masses in transportation infrastructure by leveraging drone-based Structure from Motion (SfM) photogrammetry. The research focuses on identifying failure surfaces, segregating structural domains, and assessing rock mass quality in complex geological environments. High-resolution imagery captured by unmanned aviation vehicles (UAVs) is processed to generate three-dimensional (3D) models of exposed rock faces, enabling detailed mapping of discontinuities, bedding planes, and joint sets. These models are used to extract quantitative parameters such as joint spacing, orientation, and roughness—key inputs for stability analysis and tunnel design. The project applies this methodology in field sites like Clear Creek Canyon, demonstrating its capability to support slope stability studies and tunnel alignment planning. By offering a rapid, safe, and accurate approach to rock mass characterization, the research contributes to more resilient and cost-effective design strategies for underground infrastructure.
]]></description>
      <pubDate>Wed, 07 May 2025 18:38:59 GMT</pubDate>
      <guid>https://rip.trb.org/View/2543411</guid>
    </item>
    <item>
      <title>Develop Data Collection Requirements and Strategic Research Roadmap to Support the Digital Delivery Program</title>
      <link>https://rip.trb.org/View/2447029</link>
      <description><![CDATA[Texas Department of Transportation (TxDOT) is transitioning from GeoPak to OpenRoads Designer (ORD) as part of the Digital Delivery initiative. The Department is also migrating bridge design, storm drain design, and survey as part of this transition. Recently, TxDOT engaged the services of a consultant (Kimley-Horn) to develop a strategic plan for the Digital Delivery initiative, as well as assist with its implementation. The consultant's contract runs through 2028 and has a clearly defined scope of work. However, research is needed in several areas to facilitate the Digital Delivery initiative and the consultant's work. One of those areas is related to procedures for data collection and processing to support digital delivery and asset management. The consultant is developing 3D model standards and workspaces for typical 3D objects that are being designed. However, a critical research need is how to extract cost-effective 3D models (or digital twins) efficiently from a multiplicity of data sources, including, but not limited to aerial and underground imagery, LiDAR point clouds, photogrammetry products, etc. These digital twins must meet relevant requirements for both design and construction workflows. Research is also needed to identify software and other industry trends that might affect the Digital Delivery initiative and recommend strategies on how to anticipate and prepare for those trends. Building information modeling (BIM) and digital twin technology for horizontal infrastructure are evolving quickly in the United States, not just in terms of CAD software technology and capabilities, but also in terms of industry standards such as data exchange and software interoperability. These tools have enormous potential not just during all phases of project delivery but also for infrastructure condition monitoring as well as maintenance and improvement need assessments.]]></description>
      <pubDate>Wed, 30 Oct 2024 15:06:16 GMT</pubDate>
      <guid>https://rip.trb.org/View/2447029</guid>
    </item>
    <item>
      <title>Photogrammetry and LiDAR-Based Precast Concrete Railroad Crossties Abrasion Damage Detections</title>
      <link>https://rip.trb.org/View/2314007</link>
      <description><![CDATA[Recent derailment accident that happened in East Palestine, Ohio has drawn huge public attention to railroad system safety. While this accident is under investigation, one of the major contributions to many other derailment accidents is the precast concrete crossties abrasion damage. Concrete crossties can lose concrete sections on portions of the tie bottom and sides during service. Identifying the abrasion damage of precast concrete crossties is critical to extend the railroad service life and prevent the potential derailment. The ultimate goal of this research is to develop mitigation measures to reduce concrete railroad tie section loss at the ballast interface based on expected service life for a given track’s loading and environmental conditions. As a first step to achieve this goal, this project proposes to develop a photogrammetry and LiDAR scanning-based precast concrete crossties abrasion damage detection system. The recent development of photogrammetry and LiDAR technologies provides the possibility of measuring the crossties loss to millimeter level.]]></description>
      <pubDate>Sun, 24 Dec 2023 08:30:43 GMT</pubDate>
      <guid>https://rip.trb.org/View/2314007</guid>
    </item>
    <item>
      <title>Development of a Multi-Camera Based Photogrammetric Method for Improving Three-Dimensional Full-Field Displacement Measurements of Geosynthetics During Tensile Test</title>
      <link>https://rip.trb.org/View/2289619</link>
      <description><![CDATA[The research aims to develop a low-cost photogrammetric method for continuously measuring and tracking the 3-D full-field displacements and complete strains of geosynthetics during tensile tests. The proposed method will be non-contact, cost-effective, accurate, and capable of measuring the 3-D displacements of the geosynthetics at any location within the geosynthetics and at any moment during the tensile test. The proposed method can also identify any localized strains at any location within the specimen. The developed photogrammetric method from this study can be used in dynamic tests where the objects are continuously moving/deforming, such as tensile tests on the geosynthetics, which cannot be done by using the conventional one-camera-based photogrammetric method. Departments of Transportation (DOTs) and contractors can use the method for measuring the deformational response of geosynthetics with continuous movements or deformation.]]></description>
      <pubDate>Tue, 14 Nov 2023 20:30:39 GMT</pubDate>
      <guid>https://rip.trb.org/View/2289619</guid>
    </item>
    <item>
      <title>System Design for Highly Accurate and Efficient Target Detection in Triaxial Testing</title>
      <link>https://rip.trb.org/View/2289621</link>
      <description><![CDATA[For photogrammetry-based volume measurement, existing coded target (CT) recognition and identification algorithms have limitations in perspective deformation, freely rotated CTs, and unfavorable light conditions. This study will develop an innovative system design for highly accurate and efficient target detection in triaxial testing. The proposed method will remain all the merits in existing methods and have several improvements, including blob analysis, automatic outlier identification, and an increased number of points on the membrane for more representative 3-D results. The developed photogrammetry-based volume measurement method with the target detection technology will be applied in the widely used triaxial tests to evaluate stress-strain behavior of geomaterials. The method will improve the testing accuracy and efficiency. The low-cost testing system has the potential to be widely adopted by government agencies, contractors, and research institutes.]]></description>
      <pubDate>Tue, 14 Nov 2023 20:26:03 GMT</pubDate>
      <guid>https://rip.trb.org/View/2289621</guid>
    </item>
    <item>
      <title>Volumetric Measurement of Salt Piles using Photogrammetry, LiDAR and Depth Cameras</title>
      <link>https://rip.trb.org/View/2262824</link>
      <description><![CDATA[This research will use photogrammetry, LiDAR and depth cameras as 3 efficient volumetric mapping tools for the measuring and recordkeeping of available salt in storage facilities.]]></description>
      <pubDate>Fri, 06 Oct 2023 13:36:31 GMT</pubDate>
      <guid>https://rip.trb.org/View/2262824</guid>
    </item>
    <item>
      <title>Automated and Contactless Identification of Asphalt Pavement Surface Friction Based on Computer Vision Image-based 3D Reconstruction and Machine Learning Techniques</title>
      <link>https://rip.trb.org/View/1987459</link>
      <description><![CDATA[The objective of this research is to develop an automated and contactless 3D scanning and analysis method to identify hot-mix asphalt (HMA) pavement surface friction using close-range photogrammetry. To achieve this objective, a pipeline of (a) computer vision methods for image-based 3D reconstruction and (b) machine learning methods will be used to generate 3D point cloud and mesh models, characterize the geometry and appearance of the pavement surfaces, and infer the observed surface friction. Pavement surface texture will be obtained based on any feed of overlapping images and videos. To streamline the reality capture process, an automatic image acquisition system will be employed. Building on prior work by Ibrahim et al. (2022), a pipeline of image-based 3D reconstruction, multi-view dense reconstruction and mesh modelling will be used to reconstruct the geometry and appearance of the pavement surface texture. Through a user interface, the obtained texture information will be labelled with ground-truth data, and the mapping will be stored in a data set along with the corresponding friction measurements.]]></description>
      <pubDate>Wed, 29 Jun 2022 16:31:22 GMT</pubDate>
      <guid>https://rip.trb.org/View/1987459</guid>
    </item>
    <item>
      <title>A Multiple Camera System to Determine the Absolute Volume of Soil Specimens During Dynamic Triaxial Testing (yr 1)</title>
      <link>https://rip.trb.org/View/1868764</link>
      <description><![CDATA[Triaxial tests have been widely used to evaluate stress-strain behavior for geomaterials. In the past few decades, several methods have been developed to measure the volume changes of unsaturated soil specimens during triaxial tests. Literature review indicates that all existing methods can only measure relative soil volume and it remains a major challenge for researchers to measure the absolute volume changes of soil specimens during dynamic triaxial testing. The research will develop a computer vision/photogrammetry-based multiple camera system for measuring the absolute volume change for soil specimen during dynamic triaxial testing. Methodology will be developed to analyze the videos taken from multiple cameras by combining deep-learning techniques and modern close-range photogrammetry. Three-dimensional models of the soil specimen with high accuracy will be constructed using the videos and will be compared and validated using different methods. Post-processing algorithms will be developed to automatically calculate the absolute volume, titling, eccentricity, as well as localized displacement/strains at any arbitrary locations. This method for 3D reconstruction will provide us a non-contact, high accuracy, low cost, and easy-to-operate tool for absolute volume measurements for soil specimen during dynamic triaxial testing.]]></description>
      <pubDate>Tue, 27 Jul 2021 18:19:37 GMT</pubDate>
      <guid>https://rip.trb.org/View/1868764</guid>
    </item>
    <item>
      <title>UAV-enabled Structure from Motion Photogrammetry for Bridge Crack Detection and Characterization</title>
      <link>https://rip.trb.org/View/1868767</link>
      <description><![CDATA[Bridge is a common structure form widely adopted in the engineering construction, which plays an important role in traffic and transportation system. Crack is considered as an indicator for a bridge’s structural and functional failures, and crack detection is one of the major tasks during bridge inspection to maintain the structure health and serviceability of a bridge. Literature review indicated that until now detection of 3D bridge crack is still a great challenge for structural engineer and there is little research on the automatic characterization of 3D bridge cracks. The objective of this proposed research is to develop a UAV-Enabled Structure-From-Motion Photogrammetry for detection and characterization for 3D bridge crack detection. Commercially available low-cost UAVs will be used to take the images needed for the analyses. A Structure-From-Motion Photogrammetry algorithm will be developed to reconstruct the 3D models of the bridges and deep learning will be used to automatically determine the cracks from the 3D modes. With the 3D models, crack characterization such as crack lengths, depths, widths, and patterns on bridge components can be automatically measured with high accuracy. The crack measurements will be compared and validated against results obtained from other existing methods such as local sensors and the high accuracy LiDAR system. This method for 3D crack mapping will provide us a high accuracy, low cost, and easy-to-operate tool for bridge maintenance and management.]]></description>
      <pubDate>Tue, 27 Jul 2021 15:52:39 GMT</pubDate>
      <guid>https://rip.trb.org/View/1868767</guid>
    </item>
    <item>
      <title>SPR-4549: Salt Monitoring and Reporting Technology (SMART): Development of a Photogrammetric System for Salt Inventory Reporting</title>
      <link>https://rip.trb.org/View/1783588</link>
      <description><![CDATA[Salt is an expensive commodity that is critical for successful winter operation. This project proposes the development of a stationary multi-camera photogrammetric system for salt inventory (volume estimation and reporting) in an indoor environment – specifically, dome-like and rectangular storage facilities.]]></description>
      <pubDate>Tue, 02 Mar 2021 09:25:05 GMT</pubDate>
      <guid>https://rip.trb.org/View/1783588</guid>
    </item>
    <item>
      <title>Mining of Unmanned Aerial System Operations and Data to Improve Emergency Operations during Natural Disasters</title>
      <link>https://rip.trb.org/View/1751140</link>
      <description><![CDATA[In the midst of a natural disaster, information and the ability to access various locations may be compromised by blockage of roads and bridges, collapsed buildings, flooded roads, electricity outages, among other possible circumstances. Having the ability to communicate and access any given location is of extreme importance to ensure the safety of the population. Unmanned Aerial Systems (UAS) can be used to access hard-to-reach areas and gather vital data such as the current state of infrastructure, water levels in dams and levees, road blockages, waste debris, and many other information of importance before, during and after a natural disaster. The proposed research will improve the efficiency of the current methods that emergency operation centers (EOC) currently use to gather data and aid in the decision-making process during and after a natural disaster. The information will also be made available to State transportation agencies. The findings of this research will result in more effective site recognition and data acquisition for the states in the South Central area and all other States as they are all prone to the adverse effects of natural disasters. A final report will detail information on the methodology used for this research and data acquisition of pertinent information during and after natural disasters such as the condition of flood control structures, power lines, street level conditions of roads, rising water levels, number of damaged homes and waste debris piles, among many other.]]></description>
      <pubDate>Tue, 10 Nov 2020 16:22:42 GMT</pubDate>
      <guid>https://rip.trb.org/View/1751140</guid>
    </item>
    <item>
      <title>Automated Detection of Characterization of Cracks Using Structure-From-Motion Based Photogrammetry: A Feasibility Study</title>
      <link>https://rip.trb.org/View/1742799</link>
      <description><![CDATA[In infrastructure such as pavement, bridges and tunnels, crack widths and patterns on surfaces are two of the most important signs used to estimate durability. Conventional techniques suffer from challenges such as tediousness, subjectivity, and high cost. A new measurement technique that overcomes these challenges while measuring crack displacement with high accuracy and low cost in aging structures is needed. The research will develop a Structure-from-Motion Based photogrammetry technique for measuring crack widths and patterns using videos taken by commercially available low cost digital cameras. Software will be developed to analyze the videos by combining deep-learning techniques and modern close-range photogrammetry. 3D models of the pavement and bridge structures with high accuracy will be constructed using the videos and will be compared and validated using the results generated from high accuracy LiDAR system. Post-processing algorithms will be developed to automatically calculate the real lengths as well as the real width and depth of a crack at any arbitrary locations. This method for 3D crack mapping will provide us a high accuracy, low cost, and easy-to-operate tool for pavement and bridge management.]]></description>
      <pubDate>Mon, 05 Oct 2020 17:01:12 GMT</pubDate>
      <guid>https://rip.trb.org/View/1742799</guid>
    </item>
    <item>
      <title>Image-Based 3D Reconstruction of Utah Roadway Assets</title>
      <link>https://rip.trb.org/View/1687826</link>
      <description><![CDATA[LiDAR (Light Detection and Ranging) is a mature and efficient technology currently used by various transportation agencies for highways asset management and data collection purposes. While effective, there are some limitations in using LiDAR as a common engineering tool: The technology is pretty expensive; certain levels of expertise and training are required to use LiDAR scanners for data collection and processing results, and finally it might not be available to all units and individuals. Close-range photogrammetry is another emerging technology that could be considered as a potential alternative for LiDAR scanning devices. The technology is based on processing images and videos simply captured by off-the-shelf cameras or smartphones. Unlike LiDAR, close-range photogrammetry is very cost effective, simple, and easy-to-use. This project is an attempt to study the feasibility of using photogrammetry for highway asset management purposes within the state of Utah. The project includes two major components: (1) evaluating available photogrammetric software packages in terms of generating high-quality point clouds of highway assets and (2) developing and evaluating necessary hardware settings (type and resolutions of cameras, using existing image repositories such as google street views, etc.) for data collection purposes.]]></description>
      <pubDate>Wed, 19 Feb 2020 20:14:13 GMT</pubDate>
      <guid>https://rip.trb.org/View/1687826</guid>
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