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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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    <item>
      <title>Solar Noise Barrier Industry Discovery and Prototyping</title>
      <link>https://rip.trb.org/View/2784616</link>
      <description><![CDATA[The North Carolina Clean Energy Technology Center (NCCETC) is proposing to support the North Carolina Department of Transportation (NCDOT) by conducting background research, technical modeling and a prototype implementation plan for implementation of proposed solar noise barriers along to be determined highway corridors in North Carolina. The scope of work provides a framework that outlines a comprehensive, phased approach to evaluate the technical, economic, and regulatory feasibility of integrating solar photovoltaic technology into highway noise barriers. The work will conclude with a project report that documents the findings regarding the energy production modeling, stakeholder and utility engagement, capital and O&M cost analysis, and long-term monitoring considerations to successfully transition from a pilot-scale test to full-scale regional deployment.]]></description>
      <pubDate>Wed, 30 Sep 2026 11:25:18 GMT</pubDate>
      <guid>https://rip.trb.org/View/2784616</guid>
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    <item>
      <title>Advancing Pavement Design Inputs Through Improved R-Value Predictions</title>
      <link>https://rip.trb.org/View/2780407</link>
      <description><![CDATA[The objective of this research is to develop improved R-value prediction equations that enhance accuracy while remaining practical for Arizona Department of Transportation (ADOT) pavement design use. To achieve this objective, the project will: 
(1) Collect and compile available ADOT soil and R-value datasets from different Arizona regions, including measured R-values, soil classification, index properties, gradation data, project location, material source, and other routinely available laboratory or field information. (2) Perform regression and statistical analyses to evaluate the accuracy of the current ADOT R-value correlation and identify soil parameters that can improve prediction performance for Arizona materials. (3) Develop and validate enhanced R-value prediction equations using practical input parameters such as PI, percent passing the No. 200 sieve, gradation indices, USCS classification, and regional soil information where appropriate.
(4) Provide implementation recommendations for ADOT, including recommended equations, input requirements, limitations, example calculations, and a simple calculation tool for use by Pavement Design and Geotechnical staff.]]></description>
      <pubDate>Fri, 18 Sep 2026 17:07:19 GMT</pubDate>
      <guid>https://rip.trb.org/View/2780407</guid>
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    <item>
      <title>Improved Prediction of Long-Term Camber for Prestressed Bridge Girders Build in Arizona</title>
      <link>https://rip.trb.org/View/2780406</link>
      <description><![CDATA[To be determined.]]></description>
      <pubDate>Fri, 18 Sep 2026 17:02:28 GMT</pubDate>
      <guid>https://rip.trb.org/View/2780406</guid>
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    <item>
      <title>Fiber Reinforcement in Concrete Pavement</title>
      <link>https://rip.trb.org/View/2780393</link>
      <description><![CDATA[This research identified the major gaps in the analysis and design of fiber reinforced concrete (FRC) pavements and gathered various structural-mechanics methods and solutions to various FRC-based concrete application that include: fatigue, tension stiffening, shrinkage cracking, multiple parallel cracking distribution, toughening of hybrid systems by fibers, development of tensile stress distribution across the thickness of a slab, and the moment-curvature response of a section, limit analysis of plates, as well as the load-deflection response of a slab.]]></description>
      <pubDate>Fri, 18 Sep 2026 16:13:55 GMT</pubDate>
      <guid>https://rip.trb.org/View/2780393</guid>
    </item>
    <item>
      <title>Evaluating Impacts of Street Repurposing Interventions in U.S. Cities (Phase II)</title>
      <link>https://rip.trb.org/View/2780209</link>
      <description><![CDATA[Cities are increasingly repurposing street space to support pedestrian activity and economic vitality through interventions such as sidewalk cafés, parklets, streateries, and pedestrian plazas. While these interventions are widely implemented, their impacts are often evaluated using limited case studies or coarse spatial data. There is a need for scalable, data-driven approaches to evaluate their effects on mobility, economic activity, and safety at the street segment level across diverse urban contexts.

This project is Phase II of the prior study "The Effects of Street Repurposing on Pedestrian, Vehicle and Visitor Patterns," which focused on selected sites in Seattle and developed methods to quantify pedestrian and vehicle movement patterns using Location-Based Services (LBS) data. Unlike Phase I, which focused on a small number of Seattle sites and mobility-related outcomes, the proposed research expands the analysis to multiple cities, introduces computer vision–based intervention detection, and incorporates safety-related outcomes. Building on established workflows, the project will develop scalable data capture and analysis pipelines that can be applied across diverse urban settings.

The project will leverage anonymized LBS trajectory data and street-level imagery. Computer vision object detection methods will be applied to identify and characterize street repurposing interventions, including their type and spatial configuration. The study will examine street segments across cities including Seattle, Chicago, San Francisco, and New York City. A quasi-experimental design will be used, comparing conditions before and after intervention implementation at treated sites and matched control locations to isolate the effects of interventions from broader temporal and spatial trends.

The analysis will evaluate impacts on mobility, economic activity, and safety-related outcomes, including counts and rates of observed behaviors such as vehicle speeding, mid-block pedestrian crossings, and pedestrian–vehicle conflicts. The study will also document contextual characteristics such as land use, neighborhood activity levels, and intervention type to support interpretation of results and assess the transferability of findings across urban environments.

This project will produce a scalable framework for evaluating street repurposing interventions and provide practical evidence to support transportation planning and policy decisions.]]></description>
      <pubDate>Fri, 18 Sep 2026 10:42:32 GMT</pubDate>
      <guid>https://rip.trb.org/View/2780209</guid>
    </item>
    <item>
      <title>Smart Trailer Technologies for Data-Driven Safety, Efficiency, and Policy Innovation in Heavy-Duty Fleet Operations</title>
      <link>https://rip.trb.org/View/2778229</link>
      <description><![CDATA[In 2022, 4,764 fatalities in the United States involved heavy-duty trucks, with 66% of these deaths affecting passenger vehicle occupants. Many resulted from underride collisions, where smaller vehicles slide under the high-clearance sides or rear of trailers, often causing catastrophic outcomes. Despite the severity of this issue, the transportation industry lacks a clear, standardized definition of “smart trailers” and guidelines for evaluating and mitigating such risks. This absence hinders the development of cohesive safety policies, the adoption of effective safety measures, and the creation of implementation roadmaps. Unlike Europe, where side underride guards are mandated and proven effective, the U.S. has yet to establish similar frameworks, contributing to uncertainty for policymakers, fleet managers, and technology developers.

This project addresses these challenges by clarifying the definition of smart trailers and establishing a data-driven framework to evaluate their safety, efficiency, and economic benefits. Smart trailers integrate advanced sensors, telematics, and artificial intelligence (AI) to monitor operations, predict risks, and optimize maintenance schedules. A key focus is determining the direct and indirect costs and benefits of equipping trailers with side and rear underride guards and evaluating proactive warning systems or additional technical and policy solutions to reduce trailer-related collisions. Tackling the limited real incident data, the study will integrate data-driven analysis with physics-informed machine learning to guide policymakers in developing practical, safety-enhancing regulations.

To achieve these objectives, the project will synthesize data and physics-based models from multiple sources, including accident reports, vehicle physics models, historical crash data, regulatory documents, and insurance industry records. This information will support the development of simulation-driven models to assess both the safety and economic implications of underride guards or alternative solutions. By leveraging these models, the research will provide actionable insights into the optimal design and implementation of safety measures and how existing smart trailer technologies can support evidence-based regulatory decisions.

Collaboration with key partners will ensure the research's practical relevance and impact. Clarience Technologies will evaluate current smart trailer technologies, identify limitations, and help define a standardized framework. Safety Emissions Solutions will contribute inspection and regulatory data to enhance cost-benefit analyses, while the Regional Industrial Development Corporation (RIDC) will assess how smart trailer technologies can address regional transportation challenges and resource allocation challenges. Pitt Ohio’s participation in validating the system through real-world testing and operational data collection is tentative and subject to confirmation.

The outcomes of this project include a standardized definition of smart trailers, recommendations for safety policy, and a framework for adopting advanced safety technologies. By addressing critical gaps in definitions, data, and evaluation methods, this research will guide the development of effective regulations and implementation roadmaps. Through collaboration with industry leaders, this project aims to enhance public safety, operational efficiency, and sustainable fleet management, paving the way for a safer and more technologically integrated future in heavy-duty transportation.]]></description>
      <pubDate>Thu, 17 Sep 2026 16:22:36 GMT</pubDate>
      <guid>https://rip.trb.org/View/2778229</guid>
    </item>
    <item>
      <title>StreetSavvi: Complete Street AI Companion for Digital Twin Driving</title>
      <link>https://rip.trb.org/View/2778225</link>
      <description><![CDATA[This project StreetSavvi is an effort to bring the power of Artificial Intelligence (AI), Digital Twin technology, advanced simulation and visualization to local Stakeholders such as the City of Philadelphia, Pennsylvania Department of Transportation (PennDOT) and SEPTA for a partnership during the Route for Change Project which will redevelop the Philadelphia Roosevelt Boulevard.

In an ever-increasing Digital Age, the US DOT has put great effort into developing a large number of databases that encompass infrastructure, travel behavior and safety, and context as a way to answer the formidable challenge of moving people and goods in a safe and efficient way. Interestingly, commercial satellite and street imagery such as those produced by Google and other geospatial data providers emerge as an opportunity for an up-to-date inventory of transportation infrastructure. In addition, the acceleration of Artificial Intelligence through Machine Learning and its inference processes for data processing or the Large Language Models generative processes offer phenomenal opportunities to take a global approach to the field of transportation. 

In this context, the team proposes the development of StreetSavvi, an advanced Digital Twin development platform, which applies AI computer vision processes to geospatial data, namely satellite imagery, street imagery, and camera data, to develop photorealistic 3D models of neighborhoods and develop a scalable, efficient, and user-centric digital twin platform for urban transportation planning. 

This project expands on a previous Safety21 University Transportation Center feasibility project.  It also expands on the installation of speed cameras on the Roosevelt Blvd. This proposed Streetsavvi project will closely follow the design alternatives developed by PennDOT, the City of Philadelphia, and SEPTA as they include components such as  Neighborhood boulevard, Partially-capped expressway, Bus Rapid Transit (BRT), Light Rail Transit (LRT), Subway into the new design. The simulation and visualization tools will allow the research team to partner with the stakeholders to assess the impact of various designs on multiple metrics such as traffic congestion, safety, and cost. The virtual models will allow an efficient assessment of the proposed infrastructural changes, such as lane removals, enhanced bicycle/pedestrian facilities, and exclusive bus only lanes. The research team proposes with this project to automate the ideation and simulation process by creating a friendly AI based user interface that is easy to use by state, local, and tribal transportation agencies who are stakeholders in the design of our streets.]]></description>
      <pubDate>Thu, 17 Sep 2026 15:05:42 GMT</pubDate>
      <guid>https://rip.trb.org/View/2778225</guid>
    </item>
    <item>
      <title>The Effects of Curb Extensions and Stormwater Infrastructure on Traffic Safety in Philadelphia</title>
      <link>https://rip.trb.org/View/2778224</link>
      <description><![CDATA[In order to meet the goals set out in the City's landmark stormwater management plan, and to satisfy state and federal regulations, the Philadelphia Water Department (PWD) is planning, designing, and constructing a portfolio of stormwater infrastructure systems.  These systems capture, filter, and store stormwater run-off utilizing sub-surface trenches, and vegetated features, from raingardens to street trees, mimicking natural processes where possible.  As part of this program PWD has developed 156 stormwater management bumpouts, and anticipates bringing 342 more online within the next 5 years. These bumpouts have vegetated curb extensions that protrude into the street at mid-block or intersection locations. When located at crosswalks, curb extensions are thought to improve traffic safety by reducing the crossing time of pedestrians, improving visibility at intersections, and slowing drivers by reducing the turning radii at intersections. 

This proposed project will examine the effects of these curb extensions over time on traffic collisions, injuries and fatalities relative to similar roadways where curb extensions will but have not yet been installed. Although pre-existing crash data featured in the selection of treated locations, the focus on stormwater management and long list of treated and untreated intersections provide unique, quasi-experimental conditions under which to examine the effects of curb extensions. Although curb extensions are widely accepted roadway design interventions in the fields of traffic calming and traffic safety, little is known about their effects on crashes or how these effects may vary by roadway conditions, location, or other features. 

While data processing is in progress, the City and Water Department have geocoded the locations of the existing and proposed curb extensions. Crashes from Pennsylvania Department of Transportation's (PennDOT’s) crash reporting system will be to the nearest intersection nodes of the eligible bump-outs. From here, the number of crashes, fatal and serious injury crashes, and injury crashes within each buffer can be counted and aggregated by year, with results associated with their corresponding intersection nodes and curb extensions.]]></description>
      <pubDate>Thu, 17 Sep 2026 14:57:48 GMT</pubDate>
      <guid>https://rip.trb.org/View/2778224</guid>
    </item>
    <item>
      <title>Clearview: Revisiting the Safety Benefits of this Long-Used Sign Font</title>
      <link>https://rip.trb.org/View/2778040</link>
      <description><![CDATA[The research team will evaluate the safety, visibility, and operational implications of the Clearview typeface compared with the Federal Highway Administration (FHWA) Standard Alphabets under contemporary roadway, vehicle, and sign-material conditions. The research team will review prior research, national practices, and the Texas Department of Transportation's (TxDOT) historical use of Clearview to identify remaining knowledge gaps and determine the most relevant fonts, sign types, and test conditions for further evaluation. The research team will conduct a safety and mobility investigation using surrogate measures at selected field sites to assess operational effects associated with Clearview installations. The research team will conduct a human-subjects driving simulator study to quantify legibility, recognition, comprehension, and visual-workload differences between Clearview and alternative fonts under controlled but realistic driving conditions. The research team will conduct a complementary Visibility Laboratory study to evaluate the effects of font, retroreflective sheeting, headlamp type, glare, and luminance on nighttime legibility performance.]]></description>
      <pubDate>Mon, 14 Sep 2026 16:19:27 GMT</pubDate>
      <guid>https://rip.trb.org/View/2778040</guid>
    </item>
    <item>
      <title>Performance Evaluation of Intermediate and Base Asphalt Mixtures in Virginia</title>
      <link>https://rip.trb.org/View/2775952</link>
      <description><![CDATA[The Virginia Department of Transportation (VDOT) has implemented a balanced mix design (BMD) specification for the design and acceptance of dense-graded asphalt surface mixtures (SM) with A and D designations. The BMD specification incorporates performance tests and corresponding criteria to screen out potentially poor-performing mixtures by balancing their cracking and rutting resistances during mix design. Since the BMD specification is applicable to SMs, it does not address potential deficiencies of the underlying asphalt layers such as asphalt intermediate mixtures (IMs) and base mixtures (BMs). 

The IM and BM layers correspond to most of the material used in the asphalt concrete pavement and, thus, have a key role in the pavement structural capacity. Nonetheless, IMs and BMs are not often included in routine maintenance schedules. Besides, changes in the mix design and in-place density requirements for BMs starting in 2026 may lead to changes in BMs expected performance, potentially affecting the decision-making during pavement design. Collectively, these factors highlight the need to design and produce good-performing IMs and BMs.

The goal of this study is to benchmark the performance of IMs and BMs with respect to fatigue cracking and rutting resistances and evaluate mix design strategies to potentially improve the performance of BMs, such as incorporating the recycled binder availability (RBA) concept during mix design. The scope of work will include analyzing VDOT's mix design, production, and in-place density data, performing laboratory tests (index-based and fundamental), and conduct pavement performance simulations. 

Better understanding the performance of these mixtures will not only contribute to the design and production of structurally sound pavements but will also support a succesful and responsible BMD implementation for SMs.
]]></description>
      <pubDate>Thu, 10 Sep 2026 09:45:29 GMT</pubDate>
      <guid>https://rip.trb.org/View/2775952</guid>
    </item>
    <item>
      <title>Near Real-time Construction Quality Monitoring and Inspection Protocols
Using Uncrewed Aerial Vehicles</title>
      <link>https://rip.trb.org/View/2691499</link>
      <description><![CDATA[Innovative quality control technologies are used for asphalt materials to ensure smooth and long-lasting pavements. Paver Mounted Thermal Profilers (PMTPs) and Intelligent Compactors (ICs) are among the technologies used for freshly paved mat quality monitoring. However, these technologies are expensive and have some limitations in retrofitting the existing pavers and rollers from different equipment manufacturers. In the first phase of our research, aerial thermal images captured from an Uncrewed Aerial Vehicle (UAV) were used to develop a framework for hot-mix asphalt (HMA) construction quality monitoring. Thermal segregation risk was quantified using three different metrics. An object detection machine learning algorithm was implemented to develop compaction efficiency metrics that includes roller pass counts and roller speed. Various construction sites were visited in 2023 and 2024 to develop models and framework. The main goal of the second phase of the study is to extend capabilities of the models and algorithms of the aerial pavement quality monitoring protocol and improve the readiness level for implementation in real-life scenarios. The focus of the proposed work in the second phase is to develop the probabilistic density models and extend the limits of the surveyed mat. The ultimate vision is to commercialize the proposed technology as a contractor tool that can provide near real-time quality data from the ongoing construction with actionable feedback. When such data is made available to the paving crew, likelihood of achieving uniform and target in-place densities will increase and overall pavement quality will be improved. ]]></description>
      <pubDate>Thu, 10 Sep 2026 08:58:47 GMT</pubDate>
      <guid>https://rip.trb.org/View/2691499</guid>
    </item>
    <item>
      <title>Advancing Slope Stability in Roadway Embankments: The Role of Innovative Geosynthetics and Numerical Modeling Under Dynamic Traffic Load and Extreme Rainfall Conditions</title>
      <link>https://rip.trb.org/View/2691527</link>
      <description><![CDATA[Slope stability in roadway embankments remains a critical concern in geotechnical engineering. The slopes are often mechanically stabilized by using conventional geosynthetics such as geotextiles, geogrids, and geomembranes for separation, reinforcement, filtration, drainage, and erosion control under static conditions. However, evolving infrastructure demands and increasing exposure to extreme weather and dynamic loading necessitate the adoption of advanced geosynthetics. New materials such as wicking geotextiles, which combine mechanical reinforcement with moisture regulation via capillary action, and geocomposites, which integrate multiple geosynthetic components for enhanced drainage and barrier performance, offer promising solutions. Numerical modeling techniques, including finite element and finite difference methods, are essential for evaluating the behavior of these materials under various environmental loading scenarios, capturing critical parameters such as rainfall infiltration, matric suction variations, and pore water pressure fluctuations. Furthermore, modeling under dynamic traffic loading enables the assessment of cyclic stress response, shear deformation, and stress redistribution in reinforced embankment systems. Despite these advances, research remains limited regarding the coupled effects of environmental and traffic-induced loading on advanced geosynthetic systems. This study proposes a comprehensive numerical modeling framework for advancing slope stability in roadway embankments by incorporating comprehensive laboratory test results, with a focus on evaluating the performance of advanced geosynthetics under combined environmental and traffic loading conditions. The outcomes are expected to support the development of resilient and sustainable transportation infrastructure systems. ]]></description>
      <pubDate>Thu, 10 Sep 2026 08:54:26 GMT</pubDate>
      <guid>https://rip.trb.org/View/2691527</guid>
    </item>
    <item>
      <title>Potential Use of Nano Clay in Waste Polymer Modified Asphalt</title>
      <link>https://rip.trb.org/View/2696930</link>
      <description><![CDATA[The U.S. has 2.8 million miles of paved roads, with 94% surfaced with asphalt. Asphalt binder, derived from fossil fuels, necessitates the search for sustainable alternatives. This project explores using waste Low-Density Polyethylene (LDPE) plastics as a partial asphalt replacement to enhance durability at a lower cost. Additionally, we will investigate the effects of incorporating nano montmorillonite clay into LDPE-modified asphalt. To ensure compatibility, surfactants will convert the hydrophilic clay into a hydrophobic form before mixing.
Performance evaluation will include dynamic shear rheometer analysis across three asphalt categories: unaged, short-term aged (using a rolling thin film oven), and long-term aged (using a pressure aging vessel). We will compare four sample types: conventional asphalt, LDPE-modified asphalt, nano montmorillonite clay- modified asphalt, and a combination of LDPE and nano clay-modified asphalt. The Hamburg Immersion Wheel Tracker (HIWT) test will be conducted to assess the rutting resistance of the most effective binder by simulating the combined impact of moisture and traffic loads, replicating field conditions. Beyond performance analysis, the study will assess durability and cost reductions associated with these modified materials. By integrating waste plastics and nanomaterials, this research aims to enhance the sustainability, cost-effectiveness, and durability of pavement materials.]]></description>
      <pubDate>Thu, 10 Sep 2026 08:34:45 GMT</pubDate>
      <guid>https://rip.trb.org/View/2696930</guid>
    </item>
    <item>
      <title>Performance Evaluation of Valmont Dampers for Traffic Signal and Sign Structures</title>
      <link>https://rip.trb.org/View/2772643</link>
      <description><![CDATA[Problem Statement and Research Objectives: The pending updates to the
American Association of State Highway and Transportation Officials Load and Resistance Factor Design (AASHTO LRFD) SLTS (Specifications for Structural Supports for Highway Signs, Luminaires, and Traffic Signals) Specifications allow for the quantification of effective vibration mitigation devices to provide fatigue protection for sign and traffic signal structures. Applying the AASHTO fatigue loading criteria to traffic signal and sign support structures in conjunction with an effective mitigation device, will result in a more efficient design that reduces the cost of new structures, improve safety to the traveling public, extend the life of new and existing structures, and lower maintenance, inspection and repair cost. Valmont Industries has vibration mitigation devices available for these types of structures. While Valmont vibration mitigation devices have been successfully deployed in many states throughout the country, there is little/no validated performance in Massachusetts. For the State of Massachusetts to fully adopt these vibration mitigation devices, there needs to be validation on the common types of traffic signal and sign structures deployed by Massachusetts Department of Transportation (MassDOT). This project will validate the performance of the Valmont vibration mitigation devices over the range of traffic signals and sign structures in Massachusetts. Successful completion of this project will provide definitive guidance on the specific sign and traffic signal structures on which to deploy vibration mitigation devices.
Anticipated Products: The deliverables will be documented field test performance of Valmont dampers on four (4) cantilever sign structures, including two (2) monotubes and two (2) dual tube (F-style) and two (2) traffic signal structures; validation of the Valmont models to predict performance on these six (6) structures; and predicted performance, using the validated numerical models, of the Valmont dampers on six (6) additional sign and six (6) additional traffic signal support structures. Documentation material will be generated as needed to satisfy the code for use of the Valmont vibration mitigation devices.]]></description>
      <pubDate>Wed, 02 Sep 2026 13:31:08 GMT</pubDate>
      <guid>https://rip.trb.org/View/2772643</guid>
    </item>
    <item>
      <title>Performance of Cement-Treated Aggregate (CTA) and Full-Depth Reclamation (FDR) Bases for Semi-Rigid Pavement Design</title>
      <link>https://rip.trb.org/View/2772642</link>
      <description><![CDATA[Cement-treated aggregate (CTA) and cement-stabilized full-depth reclamation (CS-FDR) are both used to increase pavement foundation stiffness, distribute wheel loads, reduce stresses transmitted to underlying layers, and support asphalt-surfaced pavement systems. Although both are cementitiously stabilized base materials, they are produced and constructed in fundamentally different ways. CTA is generally plant-produced from virgin materials while CS-FDR is produced in place by pulverizing existing bound and unbound pavement materials and stabilizing the reclaimed blend with cement. Consequently, CS-FDR may contain variable quantities of existing materials depending on original construction uniformity. CTA is expected to be more uniform, but Virginia experience has also shown that transport, placement, moisture loss, grading, and compaction can produce substantial field variability.

This is important as Virginia Department of Transportation (VDOT) begins the use of semi-rigid pavement analysis in AASHTOWare Pavement ME Design. Cement stabilized layers require material properties that represent their strength and stiffness rather than treating them as conventional unbound aggregate. 

Laboratory characterization and stiffness testing of field placed materials is necessary to describe properties of these materials for design, but laboratory testing alone is not sufficient. Repeated loading can produce progressive stiffness loss and damage even when a stabilized layer does not reach a clearly defined binary failure state. Accelerated pavement testing provides the necessary link between material properties and full-scale performance. Laboratory results can be interpreted with FWD-backcalculated stiffness, rutting and surface-distress progression, measured structural response, and post-loading forensic observations. 

This study therefore focused on the direct comparison of field-produced CTA and CS-FDR and on the development of Pavement ME inputs supported by laboratory, FWD, APT, and forensic evidence.
]]></description>
      <pubDate>Wed, 02 Sep 2026 13:15:30 GMT</pubDate>
      <guid>https://rip.trb.org/View/2772642</guid>
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