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    <title>Research in Progress (RIP)</title>
    <link>https://rip.trb.org/</link>
    <atom:link href="https://rip.trb.org/Record/RSS?s=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" rel="self" type="application/rss+xml" />
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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>
    <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>Deep Learning–Based Digital Image Correlation for Fatigue Crack  Characterization in Steel Structures
</title>
      <link>https://rip.trb.org/View/2703927</link>
      <description><![CDATA[This proposal presents a strategic approach to improving transportation safety through the advancement of deep learning–based Digital Image Correlation (DIC) for fatigue crack characterization in steel structural components. With aging transportation infrastructure and increasing cumulative traffic loading, fatigue-related deterioration in steel bridges and related systems presents ongoing safety risks. Accurate measurement of crack-induced displacement fields is critical for reliable structural assessment and informed maintenance decisions. The primary objectives of this proposal are to advance artificial intelligence (AI)-driven DIC methods beyond the limitations of conventional correlation-based approaches by enabling sub-pixel displacement learning through synthetic data generation, incorporating physics-informed modeling of crack-induced displacement discontinuities, and supporting high-resolution analysis of large image regions without loss of spatial detail. The methodology involves grayscale synthetic speckle data generation for sub-pixel displacement learning, mechanics-based displacement field modeling using finite element simulations, and development of an attention-enhanced deep learning architecture for full-field displacement prediction. Experimental validation against commercial DIC systems will establish a transferable methodology supporting safer fatigue crack evaluation practices.
]]></description>
      <pubDate>Tue, 19 May 2026 13:48:19 GMT</pubDate>
      <guid>https://rip.trb.org/View/2703927</guid>
    </item>
    <item>
      <title>An Innovative Technology To Prevent Wind-Induced Fatigue Cracks In The Astoria-Megler Bridge</title>
      <link>https://rip.trb.org/View/2594025</link>
      <description><![CDATA[The Astoria-Megler has experienced fatigue cracks in many of the long vertical members of its truss. These cracks required expensive remediation. Oregon Department of Transportation (ODOT) has unsuccessfully attempted to stiffen the structural members to prevent additional cracks from forming.]]></description>
      <pubDate>Thu, 28 Aug 2025 15:43:01 GMT</pubDate>
      <guid>https://rip.trb.org/View/2594025</guid>
    </item>
    <item>
      <title>Experimental Determination of Crack Growth in Rails Subjected to Long-Term Cyclic Fatigue Loading</title>
      <link>https://rip.trb.org/View/2573184</link>
      <description><![CDATA[It is well known that one of the most significant causes of train derailments within the U.S. is due to rail fracture. Despite this fact, a reliable model for predicting fatigue fracture in rails has not yet been deployed within the U.S. The research team has recently been developing a multiscale computational algorithm for predicting crack evolution in ductile solids subjected to long-term cyclic loading. In this part of the UTCRS the team will perform intricate experiments on rails with internal cracks as a means of both obtaining material properties and validating an advanced computational model under development in their companion proposal entitled Computational Model for Predicting Fracture in Rails Subjected to Long-Term Cyclic Fatigue Loading. Furthermore, with funding provided by MxV, the team has recently completed cyclic crack growth experiments on seven bi-axially loaded rails with internal cracks that had previously been in service. The team is therefore in this research developing the ability to: a) characterize fracture parameters for deploying their advanced fracture mechanics model; b) utilize these parameters to predict crack growth due to cyclic fatigue in rails; and c) utilize the experimental results obtained over the previous decade of testing to validate the computational predictive methodology. Should this model development prove to be useful, it is the team’s ultimate intention to utilize this new advanced technology as a tool for determining how long rails in which flaws have been detected can be safely retained in service.]]></description>
      <pubDate>Mon, 14 Jul 2025 13:01:56 GMT</pubDate>
      <guid>https://rip.trb.org/View/2573184</guid>
    </item>
    <item>
      <title>Computational Model for Predicting Fracture in Rails Subjected to Long-Term Cyclic Fatigue Loading</title>
      <link>https://rip.trb.org/View/2573185</link>
      <description><![CDATA[It is well known that one of the most significant causes of train derailments within the U.S. is due to rail fracture. Despite this fact, a reliable model for predicting fatigue fracture in rails has not yet been deployed within the U.S. The research team has recently been developing an advanced computational algorithm for predicting crack evolution in ductile solids subjected to long-term cyclic loading. In this UTCRS project, the research team will continue to adapt this model to the prediction of crack growth in rails. Concomitantly, with funding provided by MxV Rail, the research team has recently completed a decade-long series of experiments designed to provide data usable for the purpose of developing just such a model. The research team, therefore, possesses the ability to both predict crack growth due to cyclic fatigue in rails, as well as to utilize our previously obtained experimental results to validate our predictive methodology. Hence, the research team has begun the following rather challenging task of: 1) modifying our computational model for predicting crack growth for application to cyclic fatigue in rails; 2) developing an experimental protocol for obtaining the material properties required to deploy their computational fracture model (described in their companion project entitled Experimental Determination of Crack Growth in Rails Subjected to Long-Term Cyclic Fatigue Loading); 3) demonstrate the effectiveness of their model for predicting the effects of long-term cyclic loading on rail fracture; and 4) develop a procedure based on their model for railway engineers to utilize to determine when rails should be inspected and potentially removed from service for cause, thereby increasing rail safety. This project will be carried out with direct interaction and supervision by MxV Rail engineers. ]]></description>
      <pubDate>Mon, 14 Jul 2025 12:45:49 GMT</pubDate>
      <guid>https://rip.trb.org/View/2573185</guid>
    </item>
    <item>
      <title>Refining Asphalt Mixture Fatigue Cracking Design Criteria Based on Overlay Testing </title>
      <link>https://rip.trb.org/View/2480361</link>
      <description><![CDATA[Improving fatigue characterization is essential for enhancing the durability and sustainability of asphalt mixtures, leading to increased road resilience and reduced maintenance costs. The Overlay Test (OT), a crucial method for assessing the susceptibility of asphalt mixtures to fatigue cracking, involves subjecting asphalt mixture specimens to repeated direct tension loads. The cracking susceptibility of a mix with OT is assessed based on performance indices, such as Critical Fracture Energy (CFE) and Crack Propagation Rate (CPR). The existing fatigue cracking criteria for developing a balanced mixture do not account for the aging of the mix and are based on testing minimally aged asphalt mixtures. As asphalt materials age, they become more brittle and less flexible, making pavements more susceptible to cracking and reducing their capacity for healing. Although past research has helped to calibrate OT testing factors (i.e., NCHRP 09-57), little attention has been given to refining design criteria at different aging levels. This research aims to establish criteria that consider the oxidation effect for asphalt mixture design using the OT method. The study will involve thorough data analysis to adjust threshold limits for CFE and CPR when evaluating aged asphalt mixture specimens and generate a research output that differentiates from past studies. The findings obtained from the data analysis will be validated through an experimental investigation. Lastly, recommendations will be provided to implement the research findings. 
The objectives of this study are the following: (1) Determine if OT criteria used for Balanced Mix Design (BMD) needs to be refined to account for aging; (2) Establish a threshold level for OT criteria at a specific aging level, and (3) Draft an implementation plan to adopt refined OT criteria that consider aging for BMD. Accordingly, the research project is divided into four tasks. Task 1 focuses on comprehensively analyzing OT data from previous research endeavors associated with implementing BMD in Texas and formulating accelerated aging protocols. The primary objectives of this analysis are to quantify the variations in CFE and CPR parameters attributed to asphalt mixture constituents, production, and aging impacts and to establish criteria that account for aging. Task 2 involves conducting experimental validation that corroborates the proposed criteria. This laboratory work will help refine asphalt mixture fatigue cracking parameters based on their constituent materials and aging effects. Task 3 will develop recommendations for adjusting CPR and CFE parameters. The recommendations will provide a practical framework for implementing the research findings and focus on enhancing the durability of asphalt pavements. Task 4 encompasses preparing the final deliverables which will summarize the research results, findings, conclusions, recommendations, and implementation plan.
]]></description>
      <pubDate>Wed, 01 Jan 2025 17:09:36 GMT</pubDate>
      <guid>https://rip.trb.org/View/2480361</guid>
    </item>
    <item>
      <title>Weight-In-Motion (WIM) Analysis for New Jersey Bridges for Establishing Various Live Load Models for Design and Bridges Management Task</title>
      <link>https://rip.trb.org/View/2410355</link>
      <description><![CDATA[The goal of the study is to analyze NJ’s recorded weigh-in-motion (WIM) data for establishing various live load models for the design and evaluation of bridges. In addition, the objective is to calibrate the load factors based on the latest edition of the “Manual of Bridge Evaluation (MBE)” for Specialized Hauling Vehicles (SHVs) to avoid (if possible) load posting of bridges. The main tool to analyze the live load effect on bridges is utilizing reliable WIM data. Although there is a gigantic, collected WIM database for New Jersey, there is a need for a reliability-based analysis to update and improve the live load models for bridges in the state of New Jersey as follows:

• Permit trucks with various axle configurations will be identified using WIM data analysis. This will provide an opportunity for NJDOT to add additional live load models (i.e., live load models exceeding the gross weight of more than 80,000) for load rating and evaluate its process for issuing or granting annual permits.

• Validate NJ’s existing LRFD permit load model (i.e., 8-axle & 200 kips) and make necessary changes if needed. Different live load factors would be established for both new bridges and existing bridges.

• Analyze NJ’s existing steel bridge data (e.g., Rolled steel I girder with E and E’ fatigue category) to identify the risk of load-induced fatigue cracking.

• Analyze NJ’s existing steel bridge data (e.g., welded plate girder with skew angle equal to or greater than 30 degrees with staggered cross frames/diaphragms) to identify the risk of distortion-induced fatigue cracking.

• Validate NJ’s existing load factor of 1.30 for operating rating of SHVs to avoid (if possible) load posting of bridges.

]]></description>
      <pubDate>Mon, 29 Jul 2024 10:40:18 GMT</pubDate>
      <guid>https://rip.trb.org/View/2410355</guid>
    </item>
    <item>
      <title>Automated Bridge Inspection using Digital Image Correlation and other Vision-based Methods</title>
      <link>https://rip.trb.org/View/2341501</link>
      <description><![CDATA[Building on the work previously performed as part of this study, the methods developed for fatigue crack characterization using digital image correlation (DIC) will be applied to full-scale structures prone to both fatigue and fracture failures. This will include full-scale sign structure components experiencing fatigue loading and subsequent cracking, as well as representative large-scale girder specimens prone to constraint-induced fracture failure. Additionally, optical data will be generated and collected to use in evaluating the potential for machine learning and artificial intelligence methods in fatigue crack identification and characterization. This phase of the project represents deployment of the previously-developed methodologies while still looking forward to other enhanced vision-based tools. Deployment mechanisms will include various hand-held and stationary cameras, unmanned aerial vehicles (UAVs), and/or augmented reality devices such as the Microsoft HoloLens2. It is anticipated this research program will lead to vision-based inspection tools that can potentially be used in automated bridge inspections.]]></description>
      <pubDate>Sat, 17 Feb 2024 16:13:37 GMT</pubDate>
      <guid>https://rip.trb.org/View/2341501</guid>
    </item>
    <item>
      <title>Long-Term Pavement Structural &amp; Functional Evaluation on State Route 76</title>
      <link>https://rip.trb.org/View/2264428</link>
      <description><![CDATA[MDOT funded the construction of an instrumented semi-rigid pavement test section at the National Center for Asphalt Technology (NCAT) test track.  An unexpected but interesting finding was observed in strain gage readings located at the interface between the hot mix asphalt (HMA) and the cementitious stabilized base (CTB) layer: during hot summer months, compressive strain measurements were recorded under truck traffic loading, contrary to expected tensile strain measurements.  NCAT described these findings in a TRB paper that help to explain in part why some MDOT semi-rigid pavement sections may experience fatigue cracking originating at the mid-depth of the HMA rather than at the typical bottom of HMA location for this type of cracking.  MDOT is further investigating the phenomenon observed in the MDOT NCAT structural section by building a test section in Mississippi, specifically located on SR 76 in District 1.  NCAT will install strain gages and monitor the same.  

Pavement structural and functional evaluation using non-destructive testing on the SR 76 test section is needed to accurately monitor pavement condition and deterioration rate of pavement layers with time. Pavement performance monitoring will be accomplished using Applied Research Associates, Inc. (Consultant) testing equipment including the three dimensional (3-D) Ground Penetration Radar (GPR), Falling Weight Deflectometer (FWD) and the state-of-the-art high accuracy GPS semi-automated pavement distress survey vehicle equipped with the Laser Crack Measurement System (LCMS). This State Study (SS) will focus on data collection and analysis to monitor the reduction in modulus of both the cementitious stabilized soil base layer and HMA layers, and development of distresses within the pavement structure from the time of new construction up to three (3) years after opening to traffic. 

]]></description>
      <pubDate>Mon, 09 Oct 2023 09:22:26 GMT</pubDate>
      <guid>https://rip.trb.org/View/2264428</guid>
    </item>
    <item>
      <title>Prediction of Moisture Resistance of Polymeric Asphalt Binders Through the Atomic Force Microscopy (AFM) Technique</title>
      <link>https://rip.trb.org/View/1948642</link>
      <description><![CDATA[Moisture-induced damage in asphalt concrete is a major concern to the transportation agencies. Existing moisture sensitivity tests are mostly conducted at the macro- or micro-level and focused on the qualitative measurements only. The proposed study will investigate the interaction between asphalt binder and aggregates at the interface level measuring adhesion forces between asphalt binder samples and minerals of different chemical compositions using an Atomic Force Microscope (AFM). To this end, all three Arkansas Department of Transportation (ARDOT) approved Performance Grade (PG) binders (PG 64-22, PG 70-22, and PG 76-22) from two different sources and two types of commonly used aggregates (e.g., limestone and sandstone) in Arkansas will be evaluated in the laboratory. Besides the positive impacts of a selective anti-stripping agent (Kao Gripper® X2), the effects of aging (short-term and long-term) on the stripping resistance of binders will be evaluated in the laboratory.
In regards to the AFM tests, the tips will be modified with comparable aggregate minerals, thus, the adhesion force between asphalt and minerals will be measured. The adhesion force will then be used to estimate the work of adhesion between asphalt binders and materials resembling the aggregates. The AFM-based nano-level mechanistic properties such as modulus and adhesion over the scanned area of the sample will be recorded in the form of numerical values and images. The captured AFM images will be analyzed by using a commercial tool such as MATLAB® or open-source software (e.g., ImageJ). Routine rheological tests (viscosity, penetration, etc.) will be conducted on asphalt binders as they are indirect indicators of stripping resistance. Elemental analyses (e.g., aromatic hydrogen and aromatic carbon) of solution-state and solid-state asphalt binders will also be tested by using a nuclear magnetic resonance (NMR) spectroscopy. Further, selected aggregates coated with asphalt binders will be tested to evaluate their adhesive and cohesive failures by computing binders’ direct tension using a Pneumatic Adhesion Tensile Testing Instrument (PATTI) device. Furthermore, the striping resistance of fully coated aggregates (loose mixes) will be evaluated by following the Texas Boiling Test (TBT). Two-dimensional images of failure surfaces of the PATTI and post-TBT specimens will be captured and analyzed to determine failure pattern and percentage of retained binder, respectively. Finally, the AFM-based data will be compared with macro-level test data of asphalt binders and loose mixes (asphalt aggregate systems) to draw meaningful conclusions and recommendations.
The technical findings of the proposed study are expected to give pavement professionals and researchers a better understanding of moisture-related damage in asphalts at the molecular level. Implementation of the learned knowledge will assist the transportation agencies to avoid premature pavement distresses and save taxpayers’ money. Experimental data gathered from this study are expected to give confidence to state and local transportation agencies, and contractors in the region. The design and quality-control guidelines developed from the proposed study are expected to be implemented by state and industry partners. The proposed study will facilitate in meeting the following objectives of Tran-SET: (i) Introduce and implement cost-effective solutions to the transportation infrastructure backlog of projects; (ii) Develop cost-effective solutions for the construction and maintenance of the transportation infrastructure in metropolitan and rural areas; (iii) Promote workforce development through learning and continuous education.
This project strongly supports the Center’s focus areas 4 and 5. This study will develop tools and materials for longer-lasting infrastructure, assess the feasibility of using local industrial wastes, and enhance collaborative records with industry partners. The major benefits of the proposed study are to (a) reuse of waste materials, (b) enhance training opportunities for students in the region and build a future workforce.]]></description>
      <pubDate>Fri, 06 May 2022 12:23:32 GMT</pubDate>
      <guid>https://rip.trb.org/View/1948642</guid>
    </item>
    <item>
      <title>Field Testing and Long-Term Monitoring of Selected High-Mast Lighting Towers
</title>
      <link>https://rip.trb.org/View/1877400</link>
      <description><![CDATA[In 2013, WYDOT Projects B139025 and B133026 installed a combined 21 high-mast light towers
(HMLT) in District 1 and District 3. The HMLTs were fabricated by Valmont Industries according to WYDOT
Standard Plan and were installed by Modern Electric of Casper, WY. In May 2016, after only three years of
service, inspectors discovered fatigue cracks on two HMLTs located at the Wagonhound Rest Area of of I-80
near Exit 267. One of the HMLTs was missing a luminaire. Further inspection revealed that one more HMLT
(also located at the Wagonhound Rest Area) had significant fatigue damage too. Weather data collected by
a nearby weather station was reviewed by WYDOT officials showing high winds, rain and freezing
temperatures. The cause of the fatigue cracking cannot be determined at this time.
Monitoring has been on-going at the selected locations for several months. Based on review of the
data, one, and possibly two significant events have been observed at the Dwyer Junction site. The first
occurred in April 2018, with very high stress ranges being observed. The ice sensor indicated the presence
of ice during the event. The second event, in October of 2019 at the same location also occurred during a
period when the sensors indicated icing on the pole. The measured stress ranges were not as high as the
April 2018 event, but were still significant. To increase the likelihood of recording additional an events to
better characterize the conditions under which such events are produced, it is proposed to extend the
monitoring for an additional 18 months beyond the end date of March 2020. This proposal summarizes this
work.
]]></description>
      <pubDate>Wed, 08 Sep 2021 12:26:05 GMT</pubDate>
      <guid>https://rip.trb.org/View/1877400</guid>
    </item>
    <item>
      <title>Development of a Cyclic Semi-Circular Bend Test to Evaluate Asphalt Mixture Crack Propagation Properties at Intermediate Temperature</title>
      <link>https://rip.trb.org/View/1765419</link>
      <description><![CDATA[The objectives of this study are to acquire and set up the digital image correlation system that is optimized for deformation and crack propagation measurements in asphalt concrete testing; and to develop a standard cyclic semi-circular bend (SCB) test method coupled with the digital image correlation (DIC) technique for identification of fatigue crack propagation properties of asphalt concrete.
In order to achieve the objectives, three asphalt mixtures will be designed and used for sample preparation, which are expected to clearly exhibit different fatigue crack propagation resistance. The DIC system will be set up and optimized for displacement measurement and crack path identification with minimal noise interference. Various specimen thickness and notch depth are expected to be investigated to find an optimal dimension that yields reasonable results with the lowest test variability per material composition. The test data from the finalized specimen dimension and optimized DIC setup will be processed to obtain the crack propagation relationships and determine the fatigue lives of asphalt mixtures.
]]></description>
      <pubDate>Tue, 26 Jan 2021 10:55:56 GMT</pubDate>
      <guid>https://rip.trb.org/View/1765419</guid>
    </item>
    <item>
      <title>Viability Assessment and Cost-Effectiveness of Using High-Modulus Asphalt Concrete (HMAC) as Base Course in Asphalt Pavements in Louisiana</title>
      <link>https://rip.trb.org/View/1751117</link>
      <description><![CDATA[The main objective of this research study is to evaluate whether local virgin and recycled materials in Louisiana could be used to produce Enrobé à Module Élevé- (EME) mixtures so that it can used as a base course in asphalt pavements. To achieve this objective, high-modulus asphalt mixtures mimicking the European approach will be prepared using the Superpave specifications. These mixtures will include different asphalt binder grades, polymer contents, and reclaimed asphalt pavement (RAP) percentages. The dynamic modulus as well as the performance of these mixes against rutting, moisture damage, and fatigue cracking will be evaluated. The results of this study will provide solutions for fatigue and rutting failures in asphalt pavements in Region 6, hence, enhancing the durability and service life of the road infrastructure.]]></description>
      <pubDate>Tue, 10 Nov 2020 08:33:51 GMT</pubDate>
      <guid>https://rip.trb.org/View/1751117</guid>
    </item>
    <item>
      <title>Fatigue Crack Inspection Using Computer Vision and Augmented Reality</title>
      <link>https://rip.trb.org/View/1749081</link>
      <description><![CDATA[Fatigue cracks developed under repetitive traffic loads are a major threat to maintaining the structural integrity of steel bridges. Human visual inspection is currently the de facto approach for fatigue crack detection. However, due to human limitations and the complex nature of bridge structures, fatigue crack inspections are time consuming, labor intensive, and lack reliability. Inspecting the large steel bridge inventory in the United States hence remains a great challenge due to the lack of a human-centered, efficient and cost-effective methodology for detecting, tracking, and documenting fatigue cracks. On the other hand, if crack inspections could inform the inspector in the field, more reliable, efficient, and accurate assessment of the inventory could be achieved and documented. Recently, computer vision has shown great potential as a non-contact, low-cost, and versatile platform for structural health monitoring (SHM). However, most computer-vision-based crack detection methods rely on still images to extract edge features of cracks. As a result, distinguishing real fatigue cracks from crack-like surface features, such as scratches, corrosion marks, and structural boundaries remains a major challenge. In addition, inspectors currently lack an effective way to efficiently interact with new and historic inspection data.  Such human-centered ability has been identified as one of the top interests of bridge inspectors, as it not only improves inspection quality but also facilitates decision-making in the field. To overcome the above challenges, this project proposed integrating computer-vision-based motion tracking and augmented reality (AR) techniques to empower bridge inspectors to perform robust fatigue crack detection, characterization, tracking, and documentation in the field. The developed computer vision algorithm does not rely on edge features of images. Instead, it is based on recording a short video of the structure under fatigue loading, tracking the surface motion through the proposed algorithm, and analyzing the surface motion pattern to reveal the &lsquo;breathing' of fatigue cracks. In addition, the crack width could be quantified with sub-millimeter accuracy using the tracked surface motion. To overcome the limitation of the technique in the field for the inspectors, this project research integrated computer vision with Augmented Reality (AR) to enable inspectors see crack information such as the crack geometry, realized via holograms overlaid on top of the bridge surface. The developed wearable AR device is expected to greatly increase bridge inspectors' ability to perform accurate and reliable on-site inspection in a human-centered manner. Furthermore, inspectors will be able to interactively manage inspection results and compare with historic data for efficient decision-making.
The final report is available.]]></description>
      <pubDate>Wed, 04 Nov 2020 11:33:45 GMT</pubDate>
      <guid>https://rip.trb.org/View/1749081</guid>
    </item>
    <item>
      <title>Automated Bridge Inspection using Digital Image Correlation Phase III – Examination Alternative Vision-based Methods and Deployment Mechanisms for Field Implementation</title>
      <link>https://rip.trb.org/View/1741269</link>
      <description><![CDATA[Building on the previous years of this study, the methods developed for fatigue crack characterization using digital image correlation (DIC) will be examined using open-source software and off-the-shelf hardware components. Previous development and analysis of the DIC-based crack inspection methodology relied on a commercial package of hardware and software. Results indicate that hardware setup, system calibration, and environmental conditions greatly influence the reliability of the methodology. Open-source solutions may allow for more flexibility and customization of the system, potentially allowing for a more reliable and robust application of the developed crack characterization methodology. Open-source software will be identified and evaluated with existing data sets. Additionally, alternative vision-based inspection tools will also be investigated. As vision-based technology is rapidly advancing, potential for application to infrastructure inspection should be evaluated. Comparisons will be made with the existing DIC-based protocol to determine whether alternative tools are currently feasible for fatigue crack identification and characterization. Finally, deployment mechanisms will be identified and investigated. This includes commercially-available and research-focused tools such as unmanned aerial vehicles (UAVs) and robotic crawlers. This research program is anticipated to lead to implementation of a vision-based inspection tool for use in automated bridge inspections.]]></description>
      <pubDate>Fri, 25 Sep 2020 14:55:00 GMT</pubDate>
      <guid>https://rip.trb.org/View/1741269</guid>
    </item>
    <item>
      <title>Evaluation of Vibration Mitigation Techniques for KDOT Cantilever and Butterfly Sign Structures</title>
      <link>https://rip.trb.org/View/1736399</link>
      <description><![CDATA[Cantilever and butterfly sign structures are susceptible to free vibrations induced by natural wind and truck gust loadings, as well as “galloping” responses to wind. When these vibrations are left uncontrolled, they can lead to cracking at the box connections between the cantilevered truss and the vertical support. Calculations performed by Kansas Department of Transportation (KDOT) engineers have indicated that the box connection details are susceptible to fatigue, and in fact, have recently replaced a cantilever sign that experienced severe cracking. Solutions are urgently needed to lower the vibration-induced fatigue stresses at box connections in these structures to effectively lengthen the safe useable lives of these expensive and low-redundancy structures.]]></description>
      <pubDate>Tue, 01 Sep 2020 13:47:09 GMT</pubDate>
      <guid>https://rip.trb.org/View/1736399</guid>
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