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    <title>Research in Progress (RIP)</title>
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    <language>en-us</language>
    <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>
      <url>https://rip.trb.org/Images/PageHeader-wTitle-RIP.jpg</url>
      <link>https://rip.trb.org/</link>
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    <item>
      <title>Pavement Condition Rating Method and Use for Local Agencies 
</title>
      <link>https://rip.trb.org/View/2618201</link>
      <description><![CDATA[The Ohio Department of Transportation (ODOT) collects pavement condition ratings (PCR) on the state network annually and a subset of the local network that is federal aid eligible on a biennial basis. This data is made available to local public agencies (LPAs) through the TIMS system. Many LPAs also collect their own set of pavement condition ratings on all pavements within their jurisdiction to identify roads for resurfacing, repair, and other planning purposes. The data sets collected by LPAs may differ significantly from ODOT's PCR and in most cases the detailed level of distress information collected in ODOT PCR may not be necessary for their purposes. In addition, the collection methods, schedules, and data types differ from locality to locality statewide.

Metropolitan Planning Organizations (MPO's) use ODOT's PCR ratings to help compare the condition of various areas and for grant applications. While ODOT PCR may be helpful to MPOs, the feedback ODOT has received from LPAs who are responsible for maintaining the local roads is that ODOT's PCR data may not be helpful in many cases. In addition, LPAs would prefer to have data on the whole local network as opposed to a subset. Since ODOT collects and reports pavement data on federal aid eligible roads, identifying a pavement rating methodology that would be useful for all parties (LPAs and MPOs) is desired.
 
The goal of this research is to recommend pavement rating methods that would be useful to cities, counties, townships, and MPOs. Findings from this research will help ODOT to focus current efforts to collect local pavement condition ratings to be useful to the agencies responsible for the routes the data represents. Identifying and implementing a pavement rating methodology that would be useful for all parties (LPAs and MPOs) would help reduce duplication of effort and enhance data integrity and utilization. A more unified approach to pavement data collection can ultimately improve pavement management for local agencies.
                 ]]></description>
      <pubDate>Tue, 04 Nov 2025 15:32:41 GMT</pubDate>
      <guid>https://rip.trb.org/View/2618201</guid>
    </item>
    <item>
      <title>Identifying the Underlying Reasons Driving the Shifts in Bridge Performance Rating Trends in Georgia with a Particular Focus on the Notable Inversion Observed since 2019
</title>
      <link>https://rip.trb.org/View/2589066</link>
      <description><![CDATA[
The objectives of this research are to (1) identify the underlying factors driving the shifts in bridge performance rating trends in Georgia since 2016, emphasizing the notable inversion observed from 2019, and (2) investigate opportunities for improving maintenance practices/policies, leading to enhanced bridge management strategies. ]]></description>
      <pubDate>Thu, 14 Aug 2025 14:29:51 GMT</pubDate>
      <guid>https://rip.trb.org/View/2589066</guid>
    </item>
    <item>
      <title>How Complete are Your City’s Streets? Evaluating the Completeness of Urban Streets Using Big Data and Computer Vision</title>
      <link>https://rip.trb.org/View/2553162</link>
      <description><![CDATA[The main objectives of this project are: (1) development and validation of detection methods on the presence and width of individual elements of complete streets at street level, (2) development of a numeric index and typology to rate the completeness of streets, (3) curation of a publicly accessible database of various elements of complete street data in Atlanta metro; and (4) demonstration of how the rating/typology can be used to help users easily communicate and utilize the data in planning and policy decisions. Additionally, this project will provide an interactive map dashboard of non-residential urban streets in the Atlanta metropolitan region to visualize and communicate the data with the stakeholders. Understanding the current condition of complete street networks is an imperative first step in planning and policy interventions. The presence and the conditions of the eight elements of complete street segments, as reflected in the rating system, will offer critical information about which areas in the street network should be prioritized for complete street upgrades and what specific form these upgrades would entail. The knowledge of complete street elements will also help in assessing whether investing in complete street design and construction influence people’s mode choices towards more active mobility and transit. While there are numerous elements that form the concept of complete streets such as public transit facilities, pedestrian and bicyclist accommodations, traffic calming, and streetscaping, this project focuses on the elements that determine the allocation of street space, such as sidewalks, bike lanes, and street parking, and save other non-surface objects for future studies, such as signboards, walk signals, and other fixtures.

This project plans to collect data on both the presence of complete streets elements and their cross-sectional width where applicable. This detection uses aerial and street view images together as one input. The primary data source for image data will be Google Street View and Google Maps API. By validating the detected result through the comparison with the well-established data, this project will test the potential of the methodology as a low-cost alternative to the existing data collection system. All data will be collected at the street segment level. The relationship between complete streets and travel behavior outcomes in terms of urban vitality and public health will be demonstrated by using urban vitality data (e.g., daily median spend for each POI from Safegraph) and mobility pattern (foot traffic data from ADVAN Research). All data will be collected at the street segment level.]]></description>
      <pubDate>Thu, 15 May 2025 14:42:58 GMT</pubDate>
      <guid>https://rip.trb.org/View/2553162</guid>
    </item>
    <item>
      <title>Revisions for the TxDOT Pavement Management Information System (PMIS) Rater’s Manual</title>
      <link>https://rip.trb.org/View/2437686</link>
      <description><![CDATA[The research team will revise the Texas Department of Transportation (TxDOT) Pavement Management Information System (PMIS) Rater’s Manual. The research team will review, analyze, and propose changes to the current distress rating definitions for flexible and concrete (jointed and continuously reinforced) pavements. The researchers will hold collaborative workshops to gather inputs from TxDOT’s Division and Districts on pavement condition data collection challenges using the current manual. The research team will assess the impact of the rating changes to the PMIS scores and recommend changes to distress utility curves if needed. In collaboration with TxDOT, the research team will make adjustments to determine acceptable impacts. The researchers will develop revised manual based on the research results and through collaboration between the research team and TxDOT.]]></description>
      <pubDate>Thu, 03 Oct 2024 10:16:09 GMT</pubDate>
      <guid>https://rip.trb.org/View/2437686</guid>
    </item>
    <item>
      <title>A Review of Florida's FC-5 Raveling Condition Assessment and Measurement Methods</title>
      <link>https://rip.trb.org/View/2425104</link>
      <description><![CDATA[The objective of this project is to determine an appropriate method to account for raveling in Florida's pavement condition survey and subsequent pavement performance forecasting. The research should consider survey approaches as well as the rating system.]]></description>
      <pubDate>Tue, 03 Sep 2024 13:18:50 GMT</pubDate>
      <guid>https://rip.trb.org/View/2425104</guid>
    </item>
    <item>
      <title>Develop a Methodology for Pavement Drainage System Rating</title>
      <link>https://rip.trb.org/View/2379652</link>
      <description><![CDATA[The objective of this research is to explore the use of existing pavement and LiDAR data to develop a pavement drainage system rating index as part of pavement condition assessment in Louisiana, potentially by creating a drainage rating index as part of pavement condition assessment.]]></description>
      <pubDate>Tue, 14 May 2024 10:42:55 GMT</pubDate>
      <guid>https://rip.trb.org/View/2379652</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>
    </item>
    <item>
      <title>Rating Concrete Permeability Based on Resistivity Measurements</title>
      <link>https://rip.trb.org/View/1877221</link>
      <description><![CDATA[No abstract provided.]]></description>
      <pubDate>Thu, 09 Sep 2021 10:01:20 GMT</pubDate>
      <guid>https://rip.trb.org/View/1877221</guid>
    </item>
    <item>
      <title>Updating and Implementing the Grade Severity Rating System for Wyoming Mountain Passes, Phase 2</title>
      <link>https://rip.trb.org/View/1715319</link>
      <description><![CDATA[This project is set up to validate the grade severity rating system (GSRS) model for trucks that have one drum brake installed, make the GSRS fully implementable, and to develop a program that simplifies the implementation of the GSRS and formulation of weight specific speed signs.  The project shall validate the GSRS model for trucks fitted with only drum brakes; incorporate curves into WSS sign formulation; develop a program to automate formation of WSS signs; and update the GSRS user manual.  ]]></description>
      <pubDate>Thu, 18 Jun 2020 13:58:02 GMT</pubDate>
      <guid>https://rip.trb.org/View/1715319</guid>
    </item>
    <item>
      <title>SPR-4423: Pavement Markings for Asphalt and Concrete Pavements</title>
      <link>https://rip.trb.org/View/1646228</link>
      <description><![CDATA[Research will characterize the adhesion and durability of pavement marking materials to asphalt and concrete pavement surfaces including temporary tapes and paints as well as permanent markings. Variables will include marking material composition, application temperature and relative humidity, and pavement age, roughness, and chemistry, including sealed and unsealed pavements.]]></description>
      <pubDate>Thu, 15 Aug 2019 16:33:09 GMT</pubDate>
      <guid>https://rip.trb.org/View/1646228</guid>
    </item>
    <item>
      <title>Quantitative Bridge Inspection Ratings Using Autonomous Robotic Systems (IM-2)</title>
      <link>https://rip.trb.org/View/1482754</link>
      <description><![CDATA[The 2001 study sponsored by FHWA raised serious concern on the consistency and reliability of visual inspection. Although consistent ratings can be obtained with a good QA/QC program, based on a recent study by the PI, the concern for reliability of defect detection remains. With the adoption of the recent AASHTO Manuel for Bridge Element Inspection, the new inspection approach not only requires rating for bridge elements, but also the location and extent of deterioration. Since autonomous robotic systems (UAS) generate an enormous amount of inspection data, deducing from the data to a simple rating along with the location and extent of deterioration is a significant challenge. For example, RABITTM has been used to inspect concrete bridge decks with six devices, including ground penetrating radar, impact-echo and ultrasonic surface wave. However, the probability of detection (POD) for damage has not been fully demonstrated to be significantly improved using multiple devices.
Approach and Methodology. Data fusion will be used to derive a rating from test data from multiple NDE devices and visual inspections in two steps. First, location and extent of deterioration, such as delamination, will be determined by fusing data from NDE devices. Currently, almost all data fusion techniques are based on the measurement outputs of multiple devices. In this study, the wave-structure interaction leading to the measurement outcomes will be taken into account in data fusion, potentially resulting in more consistent identification of deterioration. Second, once identified reliably, damage data and visual inspection findings can be fused to determine a rating through algorithms such as artificial neural network and Fuzzy logic, while minimizing false positives and particularly false negatives. Training data for the algorithms will come from existing experience on the type and extent of deterioration and damage through inspection reports and experience of inspectors (e.g. 21 bridge inspection teams that the PI has worked with during the recent study).
Overall Objectives. This project aims to develop new fusion strategies of data collected from multiple NDE devices for improved POD based on further understanding and modeling of damage detection mechanisms, and to develop algorithms for the derivation of bridge ratings from identified damage and visual inspection findings.

Scope of Work in Year 1: (1) Develop a framework of quantitative bridge inspection using relevant data from the literature and those derived from NDE devices, (2) Identify potential NDE devices for different bridge elements, (3) Characterize POD and its improvement through data fusion.

Scope of Work in Year 2: (1) Review UAS-assisted bridge inspection literature and identify a suitable UAS platform for cost-effective yet reliable inspection of bridges, (2) Develop a detailed plan of a comparative study between regular inspection and the UAS-based inspection that mimics the regular inspection and optimize the UAS-based inspection after practical limitations have been identified, (3) Carry out UAS inspection of selected bridges, and (4) Conduct and report a comparative analysis of UAS and regular inspections.

Scope of Work in Year 3: (1) Develop an electronic sounding tool that could be incorporated into crawlers and UAS, and (2) Carry out a preliminary investigation on the use of very high resolution laser vibrometer for damage detection of concrete surface.
]]></description>
      <pubDate>Thu, 14 Sep 2017 20:51:59 GMT</pubDate>
      <guid>https://rip.trb.org/View/1482754</guid>
    </item>
    <item>
      <title>Development of Automated Pavement Condition Score and Decision Tree Logic
</title>
      <link>https://rip.trb.org/View/1480945</link>
      <description><![CDATA[The Ohio Department of Transportation (ODOT) rates pavements annually using the pavement condition rating (PCR) system developed for ODOT in the mid 1980's.  The PCR is used in the pavement management decision tree logic, and over 30 years of historic PCR data was used to develop performance prediction equations.  This detailed and long history has provided ODOT with the ability to have a complex and detailed pavement management system to assist with decision-making across the state.

ODOT started exploring the transition from manual to automated pavement distress collection with a research project completed in 2013.  The research identified limitations with the ability to mirror the manual distress collection by ODOT.  After the project concluded, ODOT outfitted a state-owned Pathway data collection van with the 3D equipment needed to collect the downward images used in automated distress classification.  This data is collected statewide on a two year cycle with data available back to the 2014 collection season.  Due to the inability of the automated distress collection to generate the PCR, a transition to automated distress collection requires the development and creation of a new pavement condition score (PCS).  Along with this new condition score and distress classification, new decision tree logic will also need to be developed in order to incorporate the PCS into the pavement management system.

The goal of this research is to enable ODOT's Office of Pavement Engineering (OPE) to transition from a manually collected pavement condition based management system to a new and equally robust automated pavement condition based management system. 
]]></description>
      <pubDate>Thu, 24 Aug 2017 15:13:33 GMT</pubDate>
      <guid>https://rip.trb.org/View/1480945</guid>
    </item>
    <item>
      <title>Determination of In-Situ Precast Concrete Girder Compressive Strength</title>
      <link>https://rip.trb.org/View/1441774</link>
      <description><![CDATA[There are no as-built precast concrete girder strength test results for the United States' inventory of over 1,200 precast girder bridges. When analyzing these structures for shear capacity under permit loading, the original "design" values for concrete compressive strength do not provide sufficient shear capacity under Load and Resistance Factor Design (LRFD) code and current design permit loading resulting in rating drops and subsequent route restrictions for the State's trucking industry.]]></description>
      <pubDate>Wed, 04 Jan 2017 10:52:04 GMT</pubDate>
      <guid>https://rip.trb.org/View/1441774</guid>
    </item>
    <item>
      <title>SPR-4012: Effects of Bridge Surface and Pavement Maintenance Activities on Asset Rating</title>
      <link>https://rip.trb.org/View/1368756</link>
      <description><![CDATA[The study will enable INDOT to ascertain the impacts of different M&R treatments to the bridge wearing surface and pavements. The research product will be numerical statements (expressed in terms of appropriate performance indicators) (a) average, (b) minimum, maximum, and standard deviation, (c) probability distribution, (d) a statistical model, and (e) sensitivity charts. The numerical statements will serve as default reset values for purposes of “what-if” analysis. That way, the post-treatment level of performance can be predicted. Implementation is expected to occur in the use of INDOT’s asset decision making and programming software packages, as well as pavement design processes.]]></description>
      <pubDate>Mon, 14 Sep 2015 16:33:31 GMT</pubDate>
      <guid>https://rip.trb.org/View/1368756</guid>
    </item>
    <item>
      <title>Human Rating of Commercially Operated Spacecraft
</title>
      <link>https://rip.trb.org/View/1367919</link>
      <description><![CDATA[Human Rating is a broad-reaching topic that brings together the process of integrating a human into a spacecraft system for safe and reliable operations. This process first requires ensuring that fundamental human physiological needs are satisfied, makes use of human capabilities as an integral element of design and operation of the vehicle, and controls hazards and manages safety risks intended to protect the public, the flight crew and passengers, and ground personnel to the maximum extent possible during all phases of the mission. The commercial space industry has no clear definition for the criteria for human-rating of an integrated commercial spacecraft and launch vehicle system. This information will support the Federal Aviation Administration's (FAA’s) safety regulatory responsibilities.
]]></description>
      <pubDate>Thu, 03 Sep 2015 09:41:35 GMT</pubDate>
      <guid>https://rip.trb.org/View/1367919</guid>
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