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    <title>Research in Progress (RIP)</title>
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    <atom:link href="https://rip.trb.org/Record/RSS?s=PHNlYXJjaD48cGFyYW1zPjxwYXJhbSBuYW1lPSJkYXRlaW4iIHZhbHVlPSJhbGwiIC8+PHBhcmFtIG5hbWU9InN1YmplY3Rsb2dpYyIgdmFsdWU9Im9yIiAvPjxwYXJhbSBuYW1lPSJ0ZXJtc2xvZ2ljIiB2YWx1ZT0ib3IiIC8+PHBhcmFtIG5hbWU9ImxvY2F0aW9uIiB2YWx1ZT0iMTYiIC8+PC9wYXJhbXM+PGZpbHRlcnM+PGZpbHRlciBmaWVsZD0iaW5kZXh0ZXJtcyIgdmFsdWU9IiZxdW90O0dyb3VuZCBwZW5ldHJhdGluZyByYWRhciZxdW90OyIgb3JpZ2luYWxfdmFsdWU9IiZxdW90O0dyb3VuZCBwZW5ldHJhdGluZyByYWRhciZxdW90OyIgLz48L2ZpbHRlcnM+PHJhbmdlcyAvPjxzb3J0cz48c29ydCBmaWVsZD0icHVibGlzaGVkIiBvcmRlcj0iZGVzYyIgLz48L3NvcnRzPjxwZXJzaXN0cz48cGVyc2lzdCBuYW1lPSJyYW5nZXR5cGUiIHZhbHVlPSJwdWJsaXNoZWRkYXRlIiAvPjwvcGVyc2lzdHM+PC9zZWFyY2g+" rel="self" type="application/rss+xml" />
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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>
    <image>
      <title>Research in Progress (RIP)</title>
      <url>https://rip.trb.org/Images/PageHeader-wTitle-RIP.jpg</url>
      <link>https://rip.trb.org/</link>
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    <item>
      <title>Continuous and Rapid Detection Methods for Segregation in Asphalt Mixture Paving</title>
      <link>https://rip.trb.org/View/2734857</link>
      <description><![CDATA[Segregation in asphalt mixtures, where coarse aggregates become separated from fine aggregates, leads to non-uniform pavement surfaces with reduced density and durability. This deficiency significantly impacts the performance of asphalt pavements, often resulting in premature failures such as raveling, cracking, and potholes. Identification of segregation during or immediately after asphalt paving operations is crucial for mitigating these potential issues, ensuring higher-quality, longer lasting, and more durable pavements while minimizing future repair needs.

Historically, segregation detection has relied on visual inspection methods or density measurements, which are both time-consuming and susceptible to errors. Recent advances in real-time monitoring and continuous inspection technologies, such as infrared imaging, Ground Penetrating Radar (GPR), continuous density and macrotexture measurement, and machine learning-driven analysis present opportunities for detecting segregation as it occurs. These innovations promise to improve the detection process, allowing for more immediate interventions that preserve pavement quality and minimize costs.

Segregation is a leading cause of premature asphalt pavement failure. Thermal, density, and gradation inconsistencies create weak areas in pavements that deteriorate faster and cost more to maintain. The purpose of this study is to explore, identify, and validate advanced technologies for detecting and quantifying segregation in asphalt pavements both during and immediately following paving operations. The focus will be on the development and implementation of continuous, real-time detection methods that facilitate immediate corrective actions and improve the overall quality and longevity of pavements.
]]></description>
      <pubDate>Thu, 23 Jul 2026 07:25:06 GMT</pubDate>
      <guid>https://rip.trb.org/View/2734857</guid>
    </item>
    <item>
      <title>Structural Safety Evaluation from Computational Modeling of Unknown Bridges Using LiDAR Point Cloud and Nondestructive Testing Data
</title>
      <link>https://rip.trb.org/View/2703878</link>
      <description><![CDATA[This project aims to convert LiDAR point cloud data into a finite element model of an unknown bridge by integrating steel bars identified from nondestructive testing into structural geometries based on LiDAR point cloud and validating the computational model against a reference model created manually using structural drawings. The aim of this study will be achieved by executing four tasks: (1) Data collection from a bridge using drone-based LiDAR flights and nondestructive testing, such as ground penetrating radar for detection and identification of steel reinforcement grids hidden in concrete members. (2) 	Data processing through registration, noise removal, and down-sampling. (3) Automated finite element model generation by integrating hidden features into structural components with outlining geometry of point cloud and discretizing them. (4) Condition assessment by running the computational model with estimated material properties under overloaded trucks and/or earthquake loads.]]></description>
      <pubDate>Mon, 18 May 2026 17:09:05 GMT</pubDate>
      <guid>https://rip.trb.org/View/2703878</guid>
    </item>
    <item>
      <title>Pavement-Maintenance-GPT: Optimizing Pavement Maintenance Decisions Using Generative AI </title>
      <link>https://rip.trb.org/View/2652179</link>
      <description><![CDATA[Pavement maintenance has significant impacts on transportation safety, mobility, and asset management. However, current decision-making for prioritizing pavement repairs often relies on subjective assessments, manual reviews, or disparate datasets, causing inefficiencies, increased vehicle emissions, and occupational health risks. This project proposes “Pavement-Maintenance-GPT,” a large language model designed to optimize repair prioritization decisions using high-fidelity Ground Penetrating Radar (GPR) video log data. 
 
Pavement-Maintenance-GPT leverages advanced generative AI to simulate expert decision-making, synthesizing pavement condition data into actionable maintenance strategies. The LLM will be trained on historical data, expert judgments, and GPR metrics to replicate and enhance human diagnostic capabilities. By providing precise and efficient repair recommendations, the model significantly improves mobility efficiency by reducing unnecessary lane closures while decreasing vehicle idling and associated fuel consumption. 
 
Aligned with CHEM’s focus areas of “Occupational Health and Efficient Mobility,” this research addresses occupational hazards by minimizing workers’ exposure to construction-related risks through optimized maintenance schedules. Additionally, it promotes efficient mobility by lessening disruptions, thereby improving overall roadway mobility. 
 
The anticipated outcomes include improved resource allocation, reduced vehicle emissions, enhanced safety for workers and road users, lower lifecycle costs, and greater transportation system performance. Pavement Maintenance GPT represents a transformative advancement, providing transportation agencies with a robust, scalable, and sustainable solution for optimized infrastructure maintenance, directly contributing to safer, healthier, and more efficient mobility. ]]></description>
      <pubDate>Tue, 13 Jan 2026 15:10:33 GMT</pubDate>
      <guid>https://rip.trb.org/View/2652179</guid>
    </item>
    <item>
      <title>2502 Evaluation of In-Place Asphalt Mix Density using DPS</title>
      <link>https://rip.trb.org/View/2606538</link>
      <description><![CDATA[Inadequate and non-uniform compaction during construction is a leading cause of premature distresses in asphalt pavements including cracks, potholes, permanent deformation, and reduced service life. Issues with compaction generally result in too high or too low density of the asphalt layer which may reduce the long-term durability of the flexible pavement. Like many state agencies, Oklahoma Department of Transportation (ODOT) requires coring to obtain field samples to assess and control the density of the asphalt layer during construction. The drawbacks of pavement coring are that the locations are selected randomly, and the process is destructive, relatively expensive, and labor intensive. A Dielectric Profiling System (DPS) is a Ground Penetrating Radar (GPR)-based rolling density system that provides real-time density measurement for the entire pavement section. This study will investigate the suitability of the DPS as a quality assurance (QA) tool for evaluating the quality of compacted pavements in Oklahoma. Working with ODOT and industry partners, this study plans to collect field data using DPS and develop dielectric-density calibration curves for different ODOT asphalt mixes. The dielectric-density calibration curves will be validated by comparing with conventional density measurement methods. In addition, information needed for the future implementation of this technology in Oklahoma will be developed.]]></description>
      <pubDate>Fri, 03 Oct 2025 11:26:13 GMT</pubDate>
      <guid>https://rip.trb.org/View/2606538</guid>
    </item>
    <item>
      <title>Evaluating and Implementing Ground Penetrating Radar (GPR) for Continuous and Rapid Monitoring of Moisture Fluctuations in In-Service Roads</title>
      <link>https://rip.trb.org/View/2487309</link>
      <description><![CDATA[Traditional methods for measuring pavement moisture, including in-place sensors and indirect assessments like FWD, can be costly, invasive, slow, offer limited spatial coverage, and disrupt traffic. In contrast, ground penetrating radar (GPR) offers a non-invasive, portable solution for swiftly evaluating extensive road segments, detecting subsurface moisture levels with reasonable cost, thereby supporting local road authorities in promptly assessing moisture conditions in critical pavement areas. The aim of this research study is to advance the validation and implementation of GPR-based pavement moisture assessments on actual low-volume roads.]]></description>
      <pubDate>Fri, 18 Jul 2025 09:49:56 GMT</pubDate>
      <guid>https://rip.trb.org/View/2487309</guid>
    </item>
    <item>
      <title>Large-scale Testing for Detecting Changes in Track Modulus with Low-cost Sensors Installed on Rolling Stock



</title>
      <link>https://rip.trb.org/View/2572334</link>
      <description><![CDATA[U.S. railroads transport 1.6 billion tons of freight over more than 140,000 miles of track each year. Safe and efficient operation of such a vast infrastructure requires extensive monitoring, evaluation, and maintenance of its track systems. Track modulus is a critical parameter in the design, analysis, and maintenance of railroad track, as it is indication of  the material stiffness below the rail comprising the combined stiffnesses per unit length of rail, plates, ties, ballast, and subgrade. Locations of “soft” track can cause increases in rail deflections and stresses, which increases the rate of rail deterioration. Traditionally, track modulus is measured onsite using static deflection testing where a known load is applied to the track, the resulting deflection is measured, and this deflection is extrapolated to the track modulus. However, this is time consuming and labor-intensive, especially if measurements are to be taken at multiple points along the track. To address the challenges with track modulus measurement, this research attempts to refine existing methods and expand the scale of monitoring by leveraging data from railcars to identify problem locations on the track and relate measured responses to specific track deficiencies. The approach allows making continuous estimations over long sections of the track and also is less costly, as self-contained acceleration and data acquisition systems are inexpensive and easy to attach to the vehicle bodies. This research will use technologies such as the ground penetrating radar (GPR) and track geometry cars in combination with low-cost sensors placed on the existing plant of rolling stock for accelerated track monitoring. Track response to track conditions will be measured. Constitutive load-deflection relationships will be established between track condition, loading, and deflection to determine the track modulus. The modulus will be characterized by leveraging the mechanics that relates vehicle accelerations to the condition of track upon which the instrumented vehicle travels. The research team is experienced in the use of low-cost accelerometers for bridge monitoring and assessment, where it established the mechanistic relationships between loading and response under various conditions and showed the feasibility of determining quantifiable, specific conditions from the acceleration data. Using this expertise and experience, the team now seeks to develop mechanics-based relationships that correlate railcar body acceleration profiles to track behavior and, eventually, track condition. This project is the next step towards full implementation, where it will use previously characterized conditions on a model test to support the constitutive load-deflection relationships. The overall impact of this work will be the widespread monitoring of track infrastructure through the installation of low-cost accelerometers on existing rollingstock. The industrial partner, BNSF, will provide support and help with implementation.]]></description>
      <pubDate>Wed, 09 Jul 2025 16:08:22 GMT</pubDate>
      <guid>https://rip.trb.org/View/2572334</guid>
    </item>
    <item>
      <title>SPR-4951: Development of a Ground Penetrating Radar Based Testing Program for Mechanically Stabilized Earth Walls</title>
      <link>https://rip.trb.org/View/2553989</link>
      <description><![CDATA[INDOT Research & Development engineers have identified GPR as a potential method for assessing the extent of voids in MSE walls. However, current GPR configurations cannot be used for scanning vertical surfaces. Therefore, GPR testing equipment specially designed for MSE wall void detection must be built. This project aims to produce and validate a GPR test system capable of locating voids in MSE walls.]]></description>
      <pubDate>Thu, 15 May 2025 15:51:21 GMT</pubDate>
      <guid>https://rip.trb.org/View/2553989</guid>
    </item>
    <item>
      <title>Assessment and Pilot Testing of Three-Dimensional Ground Penetrating Radar for Evaluating Florida's Infrastructure</title>
      <link>https://rip.trb.org/View/2548651</link>
      <description><![CDATA[This project will assess the Florida Department of Transportation (FDOT)'s 3D Ground Penetrating Radar (GPR) system for its application in infrastructure evaluations. The focus is on expanding the capabilities of the overall Ground Penetrating Radar effort by developing a first-pass implementation program for the 3D GPR. The implementation program will include establishing the Florida Method Standard Operating Procedure (SOP) for 3D GPR system usage including calibration protocols and training programs.]]></description>
      <pubDate>Wed, 30 Apr 2025 08:02:03 GMT</pubDate>
      <guid>https://rip.trb.org/View/2548651</guid>
    </item>
    <item>
      <title>Analysis of 2018-2024 Network Level Pavement Structural Testing with the TSD</title>
      <link>https://rip.trb.org/View/2495008</link>
      <description><![CDATA[The Traffic Speed Deflectometer (TSD) is a device used to measure the structural response of pavements while traveling up to the prevailing traffic speed. Virginia Department of Transportation (VDOT) has previously collected data on more than 8,000 lane miles of its roadway network using the TSD. This study seeks to combine thickness data from ground penetrating radar (GPR) and VDOT traffic data to calculate the remaining structural life of the network tested between 2018 and 2024 and to upload the data to VDOTs Pavement Management System.  ]]></description>
      <pubDate>Sat, 25 Jan 2025 10:49:20 GMT</pubDate>
      <guid>https://rip.trb.org/View/2495008</guid>
    </item>
    <item>
      <title>RES2023-28:  Field Validation of Asphalt Pavement Density Using Ground Penetrating Radar</title>
      <link>https://rip.trb.org/View/2487458</link>
      <description><![CDATA[One of the final steps of evaluating the quality of asphalt pavement construction is measuring density of newly constructed pavement. The key to successful asphalt pavement construction is obtaining the desired density uniformly. Current construction and quality assurance procedures require measuring localized density with nuclear densometer or random core sampling from constructed pavement to measure density in the laboratory. This localized and random measuring may not represent the final quality of entire pavement when variations exist in density reading. OBJECTIVES: The primary objectives are as follows: Comparison of the accuracy and variability of the rolling density meter density data with existing density measurement procedures (nuclear density, coring, etc.); Evaluation of the reliability and ease of use of the rolling density meter; Evaluation of the mix design module to determine if the dielectric constant can be accurately determined from lab compacted asphalt specimens to eliminate the need for coring for calibration.]]></description>
      <pubDate>Wed, 08 Jan 2025 14:56:22 GMT</pubDate>
      <guid>https://rip.trb.org/View/2487458</guid>
    </item>
    <item>
      <title>SPR-4915:  3D GPR Applications for Network Level Testing</title>
      <link>https://rip.trb.org/View/2444906</link>
      <description><![CDATA[The main objective of this study is the integration of 3D GPR data with deflection data either from existing FWD or soon to be available traffic speed deflectometer (TSD). To relate deflection measurements to pavement health metrics such as the modulus of elasticity or the structural number, finite element analysis requires estimates of pavement layer thickness and pavement type information. This project will investigate the use of 3D GPR for layer thickness estimation and perform a sensitivity analysis to quantify the effect of errors in layer thickness estimates in structural number computation.]]></description>
      <pubDate>Thu, 24 Oct 2024 12:16:58 GMT</pubDate>
      <guid>https://rip.trb.org/View/2444906</guid>
    </item>
    <item>
      <title>Machine Learning and Railway Track Deterioration Part 1:  Degree of Railroad Ballast Fouling Using Gaussian Process Regression</title>
      <link>https://rip.trb.org/View/2431698</link>
      <description><![CDATA[This project aims to investigate the intensity of ballast fouling on a railroad using track geometry data and data from ground penetrating radar generated from an 1820ft railway line. The data from the railway line was segmented, and each segment comprised mostly geometric data and one variable of both ballast properties and environmental data. The Gaussian process regression model used in this paper shows a significant relationship between the predictor and response variables. In addition, the model generated a feature importance plot to ascertain the contribution of each variable to ballast fouling on the rail line. The performance metrics generated from the model and the surface response show that Gaussian process regression can be used to gain insight into the nature of fouling on a railway track.]]></description>
      <pubDate>Fri, 20 Sep 2024 21:14:38 GMT</pubDate>
      <guid>https://rip.trb.org/View/2431698</guid>
    </item>
    <item>
      <title>Avalanche Risk and Forecasting using small, uncrewed Aircraft Systems</title>
      <link>https://rip.trb.org/View/2431161</link>
      <description><![CDATA[The study objectives include the development of a small, uncrewed aircraft system (sUAS)-based ground-penetrating radar (GPR) system to assess avalanche risk and forecasting. The difference between this research and R219.03 - UASnow is that the research team will take advantage of prior University of Southern California (USC) and U.S. Geological Survey (USGS) assets and investments. This particular research will advance the team's previous design to provide data that can be used to assess avalanche risk in real-time using a platform, which will provide a low-cost, rapid, and high-resolution tool for field assessments of snow properties that may be indicative of avalanche risk. The purpose is to provide a platform where real-time decisions can be made to implement corrective measures after an avalanche-prone area has been identified. 

The integration of a Radio Frequency System on a Chip (RFSoC) will allow for faster sUAS flights, radar processing, and communications to the ground control system. Other platforms such as lidar-based sUAS will be collocated to validate the SD GPR data and conventional ground-based snowpack observations. Flights and field data collection will be conducted at an avalanche risk area along the U.S. 550 Mountain Corridor and specifically in the area of Coal Bank or Molas Pass. sUAS and ground-based snowpack data (snow cores, snow pits, and snow probes) will be collected to validate the lidar and SD GPR returns; however, extreme care will be taken when entering avalanche prone areas. Lidar flights will be conducted during snow-on and snow-off periods to map bare-earth elevations and spatial variations in snow depths derived from difference maps. The study objectives include the following: Top of snow; Bare earth; Snow depth; Bare earth, aspect and slope and snow surface slope (lidar derived); Spatial distribution of snowpack properties; Snow grain type at the surface (assuming a nominal 1mm diameter for snow grains, achieving this objective can only be assessed after the radar returns are processed); Layering in the snowpack; Snow-density profile; and Snow-water equivalent (SWE).
]]></description>
      <pubDate>Mon, 16 Sep 2024 08:40:37 GMT</pubDate>
      <guid>https://rip.trb.org/View/2431161</guid>
    </item>
    <item>
      <title>Automated 3DGPR Analysis for Concrete Pavement Evaluation</title>
      <link>https://rip.trb.org/View/2342073</link>
      <description><![CDATA[The objective of this project is to identify missing, misplaced, or misaligned dowel and tie bars, voids under joints, and other deficiencies in concrete pavement by automating and improving upon the accuracy and repeatability of the analysis of 3DGPR data. The scope will include collection of 3DGPR data on concrete pavement sections at various field sites and on laboratory test slabs, and using the collected data to develop analysis routines that can be used by Department of Transportation (DOT) personnel to evaluate the conditions of interest. 3DGPR is a relatively new technology for subsurface condition evaluation. It has been implemented by state DOTs through the SHRP2 R06D IAP program and more recently as part of TPF-5(385) and is currently the primary technology being considered in TPF-5(504) led by State. 3DGPR differs from conventional Ground Penetrating Radar (GPR) in that it provides detailed information across the width of a lane, enabling it to detect tie bars, dowel bars, and other spatial features that might be missed by conventional GPR systems. Qualitative review of 3DGPR data can reveal important features in small sections of pavement, but qualitative review requires special expertise and is not practical for larger pavement sections. The purpose of this project is to automate the analysis of the 3DGPR data in a way that produces the relevant useful information needed by the owner agency for making decisions. This collaborative project combines the expertise of Contractor in 3DGPR data acquisition and processing with the expertise of the Marquette University in GPR automated data analysis and machine learning.]]></description>
      <pubDate>Tue, 20 Feb 2024 14:42:57 GMT</pubDate>
      <guid>https://rip.trb.org/View/2342073</guid>
    </item>
    <item>
      <title>Rapid Assessment of Network-Level Pavement Conditions Using Novel Tools</title>
      <link>https://rip.trb.org/View/2291289</link>
      <description><![CDATA[In this collaborative project, two leading Oklahoma universities – the University of Oklahoma (OU) and Oklahoma State University (OSU) – will work with the Texas A&M Transportation Institute (TTI) to assess network-level pavement conditions rapidly and cost-effectively, using novel tools. Roadway pavements constitute a critical element of surface transportation infrastructure. With a large portion of pavements in poor condition and reaching the end of their service lives, pavement maintenance and rehabilitation are becoming increasingly challenging tasks for many state DOTs, including DOTs in Region 6. 
Recent developments have spotlighted the Traffic Speed Deflection (TSD) Device as a valuable technology for measuring surface deflections at short intervals and capturing data on roughness, texture, and rutting at traffic speed. The evaluation of pavement conditions and their rating typically depend on such parameters as deflections, slope deflection indices, structural considerations, and remaining service life. In this context, the potential advantages of deriving network-level pavement condition ratings from TSD data could be enhanced through the implementation of other novel technologies developed by the consortium members collaborating on this project. Lack of access to a TSD device and high cost associated with data collection necessitate the pursuit of innovative in-house technologies, which will not only increase efficiency but reduce costs significantly.
As part of a pooled fund study (TPF-5 (385)) participated by ODOT, pavement conditions data from I-35 and I-40 in Oklahoma were collected recently using a TSD. The proposed study focuses on developing tools for analyzing these TSD data for network-level assessment or rating of the associated pavements. A complementary objective is to collect data from the same pavements using in-house technologies, namely Pave3D 8K available at OSU and an air-coupled Ground Penetrating Radar (GPR) and Fast Falling Weight Deflectometer (FFWD) available at TTI. 
For this purpose, with assistance of the Strategic Asset and Performance Management (SAPM) personnel at ODOT, the research team seeks to gain access to the TSD data from I-35 and I-40 and review these data closely. Leveraging different pavement condition indicators, the I-35 and I-40 pavement sections will be divided into five different categories, namely very poor, poor, fair, good, and excellent. This categorization will facilitate the subsequent selection of experimental sites for an in-depth evaluation, each spanning 3 to 5 miles. The OSU team will employ Pave3D 8K for the acquisition of 2D/3D surface images and detailed pavement roughness and texture data from the evaluation sites. The OSU team will then analyze the Pave3D 8K data and compare them with the TSD data. The results of these comparisons will assist in the establishment of definitive rating thresholds.
FFWD tests will be conducted by TTI at the selected I-35 and I-40 sections. Measured deﬂections will be used to determine structural conditions and remaining life and to compare with the corresponding TSD results. A subsurface GPR survey will be conducted on the above mentioned I-35 and I-40 sections with the help of TTI. The GPR data will be used to determine layer thicknesses and used to identify areas with subsurface defects. 
Based on the pavement conditions, cores will be extracted selectively from distressed locations as well as from some good locations. A visual observation of the extracted cores and limited laboratory test results will be used to validate the pavement rating from the TSD data and Pave3D 8K and FFWD data. The research teams from all three institutions will work together to establish pavement condition thresholds. These thresholds can be used readily by ODOT and other DOTs in Region 6. These thresholds can be adjusted in the future as more network-level data becomes available.

]]></description>
      <pubDate>Wed, 15 Nov 2023 21:46:41 GMT</pubDate>
      <guid>https://rip.trb.org/View/2291289</guid>
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