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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>
    <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>Real-Time Anomaly Detection System for Signalized Intersections</title>
      <link>https://rip.trb.org/View/2673038</link>
      <description><![CDATA[Automated Traffic Signal Performance Metrices (ATSPMs) rely on high-resolution controller data to support signal operations and maintenance. While existing ATSPM Watchdog tools can identify certain detector malfunctions, they typically rely on static thresholds set across the entire system, limiting their ability to detect issues promptly and adapt to site-specific traffic patterns. This project develops and evaluates a statistical, spatiotemporal anomaly detection framework that leverages historical detector behavior and agreement among neighboring detectors to improve alert timeliness and accuracy. The research will characterize practitioner needs, define normal detector behavior, develop and test statistical detection and classification methods, and quantify benefits relative to static thresholds. Results will inform a roadmap for potential real-time implementation within Virginia Department of Transportation (DOT) systems.]]></description>
      <pubDate>Tue, 24 Feb 2026 10:25:57 GMT</pubDate>
      <guid>https://rip.trb.org/View/2673038</guid>
    </item>
    <item>
      <title>Enhancing Airport Runway Safety through Drone-Based Inspection Systems</title>
      <link>https://rip.trb.org/View/2652212</link>
      <description><![CDATA[Kansas Department of Transportation (KDOT) aims to improve the safety and efficiency of airport runway inspections using drone technology. Currently, runway inspections are carried out through manual and vehicle-based methods, which are time-intensive, costly, and may not provide the level of detail necessary for identifying all potential safety issues. Additionally, these methods can disrupt runway operations and pose risks to inspection personnel.
Integrating high-accuracy drones equipped with imaging technology and deep learning algorithms provides a solution. By leveraging AI models for automated defect detection and classification, this approach enables KDOT to quickly identify potential hazards, quantify runway conditions, and develop a standardized health index, such as the Pavement Condition Index (PCI), for long-term maintenance planning.]]></description>
      <pubDate>Tue, 13 Jan 2026 15:04:34 GMT</pubDate>
      <guid>https://rip.trb.org/View/2652212</guid>
    </item>
    <item>
      <title>Multi-Sensory System for Railway Track Defect Detection </title>
      <link>https://rip.trb.org/View/2646942</link>
      <description><![CDATA[Railway transportation is essential for moving passengers and freight across the U.S., but accidents continue to pose serious safety and economic risks. In 2022 alone, there were about 950 rail-related fatalities and 6,400 injuries nationwide. While human error and reckless behavior are major contributors, defective track infrastructure is a significant and preventable cause of accidents. Railway tracks are complex systems consisting of steel rails, crossties, fasteners, and ballast, all subject to heavy loads, temperature fluctuations, and environmental impacts. These stresses lead to issues such as broken rails, cracked or spalled crossties, loose or missing fasteners, geometry defects, and cross-level variations. Extreme weather conditions can further cause rail buckling or fracture. Failures in these components can trigger derailments, collisions, hazardous material spills, and major service disruptions. Although manual inspections and specialized vehicles are used, many defects go undetected between inspection cycles. Traditional manual inspections, although reliable for identifying visible rail defects, are labor-intensive and limited in scalability. To improve efficiency, various nondestructive testing (NDT) technologies, such as infrared imaging, acoustic emission, ultrasonic, and electromagnetic techniques, have been used primarily for internal defects. As surface defects become more prevalent, various methods have also been developed for detecting surface-level flaws, which can be broadly categorized into three approaches: static monitoring where sensors at fixed locations provide localized coverage; inspection trolleys which integrate sensors generally in the laboratory setting; and onboard sensing systems which enable real-time detection ahead of moving trains but suffer from high cost with varying imaging quality under different weather and lighting conditions. The primary objective of this project is to develop a comprehensive but low-cost multi-sensory system for railway track defect detection. The system will integrate binocular stereovision cameras, Global Navigation Satellite System / Global Positioning System (GNSS/GPS), and IMU sensors. The scope of this project includes development of a multi-sensory system including controller and field data acquisition, development of real-time data fusion and detection algorithms, and recommendations for system deployment on railway tracks. ]]></description>
      <pubDate>Mon, 05 Jan 2026 23:04:12 GMT</pubDate>
      <guid>https://rip.trb.org/View/2646942</guid>
    </item>
    <item>
      <title>Local Resonances-based NDE Technique for Rail Flaw Detection</title>
      <link>https://rip.trb.org/View/2572328</link>
      <description><![CDATA[Rail internal defects have been one of the leading causes of track-related accidents. Rail internal defects can reduce cross-sectional area and introduce stress concentration. Moreover, they can develop with normal, rapid, and sudden growth rates. If left undetected, internal defects can result in broken rails, train accidents, and derailments, where sudden rail rupture can occur without warning. Accurate and reliable rail flaw detection is therefore critically important for improving safety and reliability and minimizing the risks of accidents induced by rail internal defects. Nondestructive evaluation (NDE) techniques, including roller search unit (RSU), ultrasound A-Scan, and phased array, have been employed to detect rail internal defects but their performance or accessibility has been limited. This project will develop a new technology using a newly identified wave propagation phenomenon  (local resonances in rails) for rail defect detection.  These local resonances feature highly localized energy and signature frequencies that are governed by the geometry and material properties of a rail. These local resonances were found to be sensitive to internal defects over the full rail cross-section and are easy to measure. A low-cost contactless acoustic sensing prototype will be developed that would generate local resonances in rails. These resonances will provide flaw detection capability over the full rail section. The prototype’s sensing configuration will be simple and robust and, compared with existing NDE techniques, it will not require sophisticated/expensive sensors or data acquisition systems. It will combine fast data collection with efficient data processing to produce timely critical damage alert. If successful, the project is expected to have a significant impact of the current state of practice for accuracy and practicality with regard to determining the presence and severity of internal rail defects.

 ]]></description>
      <pubDate>Tue, 08 Jul 2025 16:46:41 GMT</pubDate>
      <guid>https://rip.trb.org/View/2572328</guid>
    </item>
    <item>
      <title>Computer Vision Tools for Bridge Inspections and Reporting</title>
      <link>https://rip.trb.org/View/2512617</link>
      <description><![CDATA[This project focuses on research to develop practical artificial intelligence (AI) tools that help bridge inspectors with defect detection and measurements and to facilitate the inspection and reporting following National Bridge Inventory (NBI) and AASHTO Manual for Bridge Element Inspection (MBEI) requirements. The research team will produce a final report with recommendations including a set of verified open-source computer vision codes for damage detection and measurements, a user-friendly software for routine inspection and reporting, as well as a user guide and training sessions for Alaska Department of Transportation and Public Facilities (DOT&PF) engineers.]]></description>
      <pubDate>Fri, 21 Feb 2025 21:12:47 GMT</pubDate>
      <guid>https://rip.trb.org/View/2512617</guid>
    </item>
    <item>
      <title>NRRA: Establishing Applicability of NDT Methods for Project-Level Evaluation</title>
      <link>https://rip.trb.org/View/2434199</link>
      <description><![CDATA[Despite the accelerated development of an array of nondestructive testing (NDT) devices in the last decades, their use in a meaningful way in the design stage has not been done systematically. Each device has certain limitations and is applicable under specific conditions. The objective of this project is to establish the usefulness and the probability of successful detection of defects by traditional and new NDT methods so that pavement engineers will know which NDT device to deploy when, and how to use the data. The objectives of this project can be summarized in the following manner:
What is the extent of defects that different NDT devices can detect with sufficient confidence in different pavement layers?
What is the accuracy of the prediction of responses of pavement under different NDT devices for different pavement types?]]></description>
      <pubDate>Thu, 26 Sep 2024 15:22:10 GMT</pubDate>
      <guid>https://rip.trb.org/View/2434199</guid>
    </item>
    <item>
      <title>Computer Vision Tools for Bridge Inspections and Reporting</title>
      <link>https://rip.trb.org/View/2420106</link>
      <description><![CDATA[The Alaska Department of Transportation & Public Facilities (DOT&PF) is responsible for condition assessment of approximately 1000 bridges in the state.  Each year, Alaska DOT&PF engineers inspect about 500 bridges.  Per the Alaska Bridge Inspection Program, the inspector must complete both a National Bridge Inventory (NBI) inspection (following the FHWA Recording and Coding Guide) and an element level inspection (following the AASHTO Manual for Bridge Element Inspection, MBEI) for each bridge.  Using either NBI or MBEI, a significant amount of data must be collected and reported.  However, the data collection/reporting is usually done manually, which is time consuming, error prone, and sometimes not consistent when repeated.  For example, the deck defect mapping requires manual detection and measurement of delaminated concrete, patch repairs, exposed reinforcing steel, and spalling.  Computer vision, a type of image processing that incorporates artificial intelligence (AI) for analyzing the surroundings, can significantly expedite the process of damage/defect identification and measurement only using photographs of bridge deck and other elements.  Furthermore, this and other AI tools can be utilized to expedite and unify reporting.  The main goal of the present study is to develop practical AI tools that help inspectors with measurements and reporting of bridge defects following NBI and MBEI requirements.  To achieve this goal, a few bridge elements (e.g., decks and girders) will be targeted for further investigation, inspection database including photographs of the selected elements with/without damage will be compiled, and computer vision tools will be developed for the selected elements to recognize the element defects, quantify the defect per NBI/MBEI, and produce a report following the DOT&PF standard practice.  The tools, which can be standard software or web-based, will incorporate mobile devices for the ease of data collection, access, sharing, and reuse in future inspections.]]></description>
      <pubDate>Fri, 23 Aug 2024 14:40:27 GMT</pubDate>
      <guid>https://rip.trb.org/View/2420106</guid>
    </item>
    <item>
      <title>FAST® Loop Comparison of Onboard Condition Monitoring Versus Wayside Detection Systems</title>
      <link>https://rip.trb.org/View/2405271</link>
      <description><![CDATA[Prior research at the University Transportation Center for Railway Safety (UTCRS) has demonstrated that onboard sensor technology can make early and accurate detections of defect initiation in railcar bearings and wheels. Hum Industrial Technology, Inc., has further developed this technology to the point of field deployment in commercial applications with multiple railcar operators. Although these field deployments have already shown the ability to detect defective wheelsets, there is a lack of head-to-head comparison data in which the same bearings and wheels are monitored using both conventional wayside sensors (Hot Bearing Detectors (HBD) and Wheel Impact Load Detectors (WILD)) and onboard monitoring (commercial units from Hum and next generation prototypes from UTCRS), at the same time on the same track. This is a crucial step in validating the current onboard technology, as well as an initial field test of newer experimental techniques.
The research team proposes a large scale (40+ sensor units) test to be conducted at the MxV Rail FAST® (Facility for Accelerated Service Testing) track loop. The test will acquire data from (a) commercial Hum Boomerang wireless sensors, (b) UTCRS prototype sensors developed during the 2023-2024 funding cycle, (c) an existing HBD installed at FAST®, and (d) an existing WILD installed at FAST®. The test will include several randomly selected cars, and one car intentionally installed with a combination of healthy wheels/bearings and wheels/bearings with known early-stage defects. At the end of the test run, selected bearings and wheels will be pulled and inspected for a "ground truth" evaluation of defect severity.
The outcomes of this project would include quantitative, calibrated comparisons of (a) temperature only (HBD) versus temperature and vibration (onboard) measures of bearing health and (b) wheel flat measurements from onboard accelerometers versus WILD. It will allow direct evaluation of the relative performance of onboard and wayside in early detection. The UTCRS prototype portion of the project would also demonstrate the viability of new techniques such as synchronized sampling and adaptive filter cutoffs and provide a large-scale public database for training artificial intelligence/machine learning (AI/ML) systems.
]]></description>
      <pubDate>Mon, 22 Jul 2024 08:12:35 GMT</pubDate>
      <guid>https://rip.trb.org/View/2405271</guid>
    </item>
    <item>
      <title>Development of a Nondestructive Imaging Tool to Identify Deficiencies in External Tendons Containing Flexible Fillers</title>
      <link>https://rip.trb.org/View/2353363</link>
      <description><![CDATA[The technical goal of this work is to modify the design of unit hardware and software for application to detection in wax-filled external post-tensioned tendons. The modification will be informed through modeling and experimental efforts to identify optimal operating parameters (frequency range, amplitude, sensor type and size, etc.) that maximize the sensitivity of TIU for detection of filler deficiencies within wax-filled external tendons.]]></description>
      <pubDate>Mon, 03 Jun 2024 14:50:07 GMT</pubDate>
      <guid>https://rip.trb.org/View/2353363</guid>
    </item>
    <item>
      <title>Drilled Shaft Imaging With 2d Ultrasonic Waveform Tomography</title>
      <link>https://rip.trb.org/View/2339973</link>
      <description><![CDATA[The objective of this research is to develop a new ultrasonic technique for full-volume imaging of drilled shafts. The technique will enable to characterize the whole drilled shaft at high-resolution (cm-pixels) for assessment of concrete and defects both inside and outside the rebar cage.]]></description>
      <pubDate>Thu, 15 Feb 2024 13:07:15 GMT</pubDate>
      <guid>https://rip.trb.org/View/2339973</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>Characterization of Weld Flaw Size</title>
      <link>https://rip.trb.org/View/2100875</link>
      <description><![CDATA[This study will provide an evaluation of weld flaw detection thresholds.]]></description>
      <pubDate>Wed, 18 Jan 2023 11:17:25 GMT</pubDate>
      <guid>https://rip.trb.org/View/2100875</guid>
    </item>
    <item>
      <title>AI-enabled Interactive Threats Detection using a Multi-camera Stereo Vision System</title>
      <link>https://rip.trb.org/View/2085750</link>
      <description><![CDATA[This research will develop a novel multi-camera stereo vision system for in-line pipeline inspection; the design integrates hardware prototyping and software development for fast, accurate, automated pipeline anomaly detection. Physics-based models are developed to evaluate the effects of identified threats and their interactions on pipeline safety.]]></description>
      <pubDate>Fri, 16 Dec 2022 14:15:44 GMT</pubDate>
      <guid>https://rip.trb.org/View/2085750</guid>
    </item>
    <item>
      <title>Distributed Fiber Optic Sensor Network for Real-time Monitoring of Pipeline Interactive Anomalies</title>
      <link>https://rip.trb.org/View/2085749</link>
      <description><![CDATA[The overarching goal of this research is to pave a path which may transform the current pipeline anomaly detection technologies to a distributed fiber optic sensor network for real-time detection, localization, and quantification of interactive anomalies of pipelines, thus improving the pipeline safety and management.]]></description>
      <pubDate>Fri, 16 Dec 2022 14:15:43 GMT</pubDate>
      <guid>https://rip.trb.org/View/2085749</guid>
    </item>
    <item>
      <title>Validate In-Line Inspection (ILI) Capabilities to Detect/Characterize Mechanical Damage</title>
      <link>https://rip.trb.org/View/2085746</link>
      <description><![CDATA[The project will involve In-Line Inspection (ILI) vendors, pipeline operators and mechanical damage assessment subject matter experts to employ and extend existing testing protocols aimed at evaluating ILI system performance and identifying areas for improvement. Understanding current performance of ILI systems will support technology enhancements and identify requirements for new technologies.]]></description>
      <pubDate>Fri, 16 Dec 2022 14:15:38 GMT</pubDate>
      <guid>https://rip.trb.org/View/2085746</guid>
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