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    <title>Transport Research International Documentation (TRID)</title>
    <link>https://trid.trb.org/</link>
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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>Transport Research International Documentation (TRID)</title>
      <url>https://trid.trb.org/Images/PageHeader-wTitle.jpg</url>
      <link>https://trid.trb.org/</link>
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    <item>
      <title>Distributed Fiber Optic Sensing Integrating TCN-Transformer for Damage Evolution Analysis of Asphalt Concrete under Freeze–Thaw Cycles</title>
      <link>https://trid.trb.org/View/2686600</link>
      <description><![CDATA[In this paper, an artificial intelligence-driven distributed fiber optic sensing technology was used to monitor the mechanical behavior of asphalt concrete under different loading forms to address the problem of damage evolution prediction of asphalt concrete. Experiments of static loading, fatigue loading, postdamage unsaturated freezing and thawing cyclic action, and postdamage saturated freezing and thawing cyclic action were designed in order to collect the strain data of the specimens under the four working conditions. Subsequently, the time-series prediction model of the temporal convolutional network-Transformer (TCN-Transformer) was developed to predict the strain distribution of specimens obtained from distributed fiber optic sensors under the four aforementioned conditions. In order to evaluate the accuracy of the TCN-Transformer time-series prediction model in predicting the strain distribution of asphalt concrete under different damage cycles, the root mean square error, the mean absolute percentage error, and the coefficient of determination (R²) were calculated to quantify the accuracy of the model, utilizing the spatial distribution points of the fiber optic sensors as samples for comparing the true values with the predicted values. Subsequently, the performance and prediction capabilities of the proposed model were evaluated across datasets of varying sizes. The performance of the model and the fit of the predicted values to the true values were evaluated for different sizes of datasets.]]></description>
      <pubDate>Fri, 31 Jul 2026 09:23:50 GMT</pubDate>
      <guid>https://trid.trb.org/View/2686600</guid>
    </item>
    <item>
      <title>Key Technologies of Aircraft Flight Test Based on Structural Control</title>
      <link>https://trid.trb.org/View/2732292</link>
      <description><![CDATA[A comprehensive solution integrating advanced sensor technology, structural dynamics models, and intelligent control algorithms is proposed to address the shortcomings of traditional flight testing techniques in monitoring and controlling aircraft structures under complex flight conditions. By establishing precise aircraft structural dynamic equations through fiber optic sensors, an accurate description of the dynamic characteristics of the aircraft structure can be achieved. A distributed structural monitoring system is constructed through FBG to monitor the physical quantities, such as strain and temperature, of key parts of the aircraft in real time during flight testing. Based on the real-time monitoring data, the structural state of the aircraft can be predicted, and the structural response can be actively adjusted by controlling the actuator. The experimental results show that this technology system effectively improves the accuracy of structural monitoring and the effectiveness of control during aircraft flight testing, providing strong guarantees for the safety and reliability of aircraft flight testing, and laying a solid foundation for aircraft structural design optimization and flight performance improvement.]]></description>
      <pubDate>Tue, 21 Jul 2026 11:33:35 GMT</pubDate>
      <guid>https://trid.trb.org/View/2732292</guid>
    </item>
    <item>
      <title>Fiber Optic Sensors in Asphalt Pavements: Investigation of the Sensor and the Asphalt Pavement</title>
      <link>https://trid.trb.org/View/2671798</link>
      <description><![CDATA[Implementing fiber optic sensors (FOS) in asphalt pavements provides a wealth of data with multiple applications. Successful integration of FOS into asphalt pavements depends on two key requirements. First, the sensor embedded in the asphalt must withstand the paving process without damage. Second, the cable must neither compromise the performance nor the durability of the asphalt. These requirements were rigorously evaluated in a joint effort between the Technical University of Darmstadt and RINA Consulting S.p.A. Using realistic forces and material temperatures, asphalt samples were compacted and cable functionality and integrity were non-destructively evaluated. Standardized mechanical tests were used to conduct asphalt performance under dynamic loading. Void distribution within the asphalt specimens were evaluated using asphalt petrology techniques. Results confirmed the integrity of nearly all cables tested, with minimal impact on asphalt void content and structure. Mechanical tests provided insight into the durability and performance of FOS-containing specimens.]]></description>
      <pubDate>Tue, 30 Jun 2026 16:05:35 GMT</pubDate>
      <guid>https://trid.trb.org/View/2671798</guid>
    </item>
    <item>
      <title>Distributed Sensing System Based on FOS Technology for Road Infrastructure Management and Maintenance</title>
      <link>https://trid.trb.org/View/2671110</link>
      <description><![CDATA[In the context of European road transport, where safety is paramount, this paper presents an innovative approach to road infrastructure management and maintenance, exploiting Fiber Optic Sensors (FOS) technology for asphalt-paved roads. The work integrates autonomous robotized solutions and modularization techniques, developing a real-time monitoring system using FOS embedded in the road wear layer. Extensive laboratory tests verified FOS parameters and performance, reporting a 100% success rate in surviving to asphalt realization process; functionality tests showcased real-time monitoring capabilities, detecting residual stress during the pavement realization. The already available results indicate the technology’s potential to revolutionize road maintenance, enhancing safety and minimizing maintenance costs, representing a significant stride toward proactive and efficient road infrastructure management.]]></description>
      <pubDate>Tue, 30 Jun 2026 09:45:10 GMT</pubDate>
      <guid>https://trid.trb.org/View/2671110</guid>
    </item>
    <item>
      <title>Fiber-Optic Technology for Monitoring Asphalt Roads-Results of a Feasibility Study</title>
      <link>https://trid.trb.org/View/2671034</link>
      <description><![CDATA[Infrastructure operators such as the Autobahn GmbH of the Federal Government in Germany depend on being able to use as much information as possible about the existing infrastructure for planning and maintenance. In addition to the existing non-destructive measurements, monitoring with sensor technology built into the infrastructure can also provide useful information for efficient maintenance planning. As part of a feasibility study, various setups of new developments in fiber-optic sensors were installed in a rehabilitated autobahn section in order to investigate traffic-related information as well as long-term integrity. In addition to temperature monitoring during the road construction, strain and acoustic measurements were recorded and evaluated during defined crossings of traffic. The preliminary results show that traffic relevant information such as the counting of passing vehicles, their velocity, vehicle type and axle load can be derived from the fiber-optic data. Furthermore, the results suggest that relevant parameters about the elastic properties of the roadwork (Young’s Modulus and material fatigue) can be extracted. At the present time, considerable efforts are still required for data preparation and data analysis to apply this monitoring technology permanently. This is the objective of the current and future development stages.]]></description>
      <pubDate>Tue, 30 Jun 2026 09:45:09 GMT</pubDate>
      <guid>https://trid.trb.org/View/2671034</guid>
    </item>
    <item>
      <title>Multihop Subterahertz Free Space Optics: A Technique for High-Rate Uninterrupted Backhauling in 6G</title>
      <link>https://trid.trb.org/View/2672981</link>
      <description><![CDATA[Moving toward 6G, backhaul networks require significant improvements to support new use-cases with restricted joint capacity and availability requirements. In this article, we investigate the potentials and challenges of joint subterahertz (sub-THz) and free space optics (FSO), in short sub-THz-FSO, multihop networks as a candidate technology for future backhaul communications. As we show, with a proper deployment, sub-THz-FSO networks have the potential to provide high-rate reliable backhauling, while there are multiple practical challenges to be addressed before they can be used in large-scale.]]></description>
      <pubDate>Wed, 24 Jun 2026 13:22:07 GMT</pubDate>
      <guid>https://trid.trb.org/View/2672981</guid>
    </item>
    <item>
      <title>Identifying heat generation/dissipation mechanisms in cylindrical Li- and Na-ion cells of different formats using fibre optic sensors</title>
      <link>https://trid.trb.org/View/2706736</link>
      <description><![CDATA[Innovation in battery formats and chemistries has been one of the most significant revolutions in commercial battery technology over the past decade. However, the potential thermal challenges accompanying such advances remain insufficiently unexplored. Herein, we implant fibre Bragg grating sensors in a variety of cylindrical battery formats to monitor the temperature and heat evolutions of lithium- and sodium-ion chemistries operando. Combining experiments with modelling, we demonstrated that NaMO₂/hard carbon cells had lower thermal dissipation than LiFePO₄/graphite cells. Furthermore, axial heat dissipation becomes increasingly important with larger cell sizes. Moreover, analysis of heat generation reveals different heat sources in the two chemistries and emphasizes the equal importance of tab number and spatial distribution in thermal design. This work provides a comprehensive comparison of thermal behaviours across cell chemistries and sizes, offering new insights into battery thermal design.]]></description>
      <pubDate>Thu, 11 Jun 2026 09:29:17 GMT</pubDate>
      <guid>https://trid.trb.org/View/2706736</guid>
    </item>
    <item>
      <title>Assessing the reliability of the cargo deck of a hovercraft through fiber optic strain measurements</title>
      <link>https://trid.trb.org/View/2667028</link>
      <description><![CDATA[While traditional sensing systems can suffer from low spatial sampling density and often face significant operational challenges in the maritime environments routinely experienced by naval craft, fiber optics provide a distributed sensing solution that is insensitive to many of these environmental stressors. Through the use of fiber optic sensing systems, strain data was collected along the cargo deck of two naval hovercraft during various maneuvers and loading conditions. This work presents a methodology for translating those strain measurements into deflection estimates, and ultimately reliability analyses, through optimization and analytical modeling tools. Specifically, by treating the cargo deck of the hovercraft as a large, thin plate, direct relationships between strain, bending curvature, and deflection can be reliably established. Comparisons of probability distributions of maximum absolute deflections experienced during set maneuvers and loading conditions reveal differences in the response between the two craft. These differences are further explored in the context of reliability indices based on limit deflections.]]></description>
      <pubDate>Tue, 26 May 2026 11:56:25 GMT</pubDate>
      <guid>https://trid.trb.org/View/2667028</guid>
    </item>
    <item>
      <title>Camera-Distributed Fiber Optic Sensing Fusion-Based Vehicle Behavior Estimation for Safe Onramp Merging Support in Mixed Traffic</title>
      <link>https://trid.trb.org/View/2701375</link>
      <description><![CDATA[Onramp merging zones are critical areas in highway networks where interactions between mainline and onramp vehicles often disrupt traffic flow, causing congestions and safety concerns. The transition toward mixed-traffic environments, where human-driven vehicles (HDVs) coexist with connected and automated vehicles, introduces complexity because of behavioral variability and uncertainty in HDV behavior. Irregular HDV behaviors such as sudden lane changes and speed variance, challenge intention prediction. Monitoring and detecting HDV intentions near onramp merging zones is essential to ensure safe and efficient traffic flow. Distributed fiber optic sensing (DFOS) technology offers promising capabilities for real-time, continuous, wide-range vehicle behavior monitoring but faces limitations in detecting key vehicle attributes such as type, size, and lane-of-travel. In this paper, we propose an integrated traffic monitoring methodology that combines high accuracy and reliability of cameras that measure vehicle attributes with wide-area monitoring capabilities of DFOS systems to enable robust vehicle tracking and behavior estimation. The proposed sensor fusion involves detecting and tracking vehicles within the camera field-of-view, matching vehicles with their corresponding DFOS trajectories, and monitoring vehicle behavior by detecting lane changes. This approach enables robust vehicle tracking and behavior estimation by matching camera-derived vehicle attributes with corresponding DFOS-derived vehicle properties. The proposed methodology was validated through a field trial conducted on the Shin-Tomei expressway in Japan. The evaluation demonstrated vehicle matching accuracy of 92% and continuous lane-change detection accuracy of 82%, respectively. These results highlight the potential of combining DFOS and point sensors to support safe and efficient onramp merging in mixed-traffic environments.]]></description>
      <pubDate>Fri, 15 May 2026 09:18:58 GMT</pubDate>
      <guid>https://trid.trb.org/View/2701375</guid>
    </item>
    <item>
      <title>Vehicle-induced bridge dynamic response monitoring and displacement estimation using distributed acoustic sensing</title>
      <link>https://trid.trb.org/View/2672197</link>
      <description><![CDATA[Many bridges in operation today are aging, highlighting the need for advanced structural health monitoring solutions to ensure continued safety and performance. In this study, we propose an integrated framework that leverages Distributed Acoustic Sensing (DAS) with self-deployed fibers to monitor vehicle-induced dynamic response of bridges and to convert DAS-observed strain rate data into displacement—allowing direct alignment with standard engineering evaluation metrics used in structural codes. Validation against Laser Doppler Vibrometer (LDV) measurements shows high accuracy of the DAS-estimated bridge displacements, with an Average Percentage Variation Error (APVE) below 4.98%. Additionally, vehicle speeds are estimated from DAS data to demonstrate the feasibility of real-time and anonymous traffic monitoring. The observed vehicle speed distribution closely aligns with the tiered penalty thresholds defined in traffic regulations, indicating that DAS holds strong potential for future traffic monitoring and management applications. This study offers a practical and innovative approach to dynamic bridge monitoring and displacement estimation using DAS technology.]]></description>
      <pubDate>Wed, 13 May 2026 09:33:31 GMT</pubDate>
      <guid>https://trid.trb.org/View/2672197</guid>
    </item>
    <item>
      <title>On-Engine Measurement of Automotive Turbocharger Turbine Blade Vibration</title>
      <link>https://trid.trb.org/View/2691953</link>
      <description><![CDATA[Automotive turbochargers are carefully designed to avoid resonance of the turbine blades and backwall, which can result in High Cycle Fatigue failures. Blade Tip Timing is an established technique which utilizes fiber optic probes to measure turbine blade displacements in real time on turbochargers spinning at upwards of 150,000 RPM. Historically, Blade Tip Timing measurements of automotive turbochargers have been made under steady-state conditions using a Hot Gas Stand. In an industry first, General Motors conducted testing of a turbocharger on a running gasoline engine to capture realistic exhaust pressure dynamics. A reference turbocharger was measured on an engine testbed running a production calibration; the same turbocharger was then tested on a Hot Gas Stand to observe how the blade behavior changed. Blade displacements were found to be lower on engine, because the dynamics of engine pulsation reduced the in-phase work available to drive the turbine blades, resulting in lower blade stresses and an improvement in calculated blade fatigue life. Testing also confirmed that key blade resonances had been successfully moved out of the operating space of the engine. Additionally, blade vibration was measured at multiple temperatures on the hot gas stand, and a clear trend was observed between blade temperature and frequency of vibration. The conclusion is that the new turbine design is ready for adoption and poses no concerns for High Cycle Fatigue. While on-engine testing is more challenging to perform, significant advantages are noted; on-engine testing provides a more realistic life estimate for turbine stages than can be obtained using hot gas stand data alone.]]></description>
      <pubDate>Tue, 14 Apr 2026 15:11:22 GMT</pubDate>
      <guid>https://trid.trb.org/View/2691953</guid>
    </item>
    <item>
      <title>Precision Traffic Monitoring: Leveraging Distributed Acoustic Sensing and Deep Neural Networks</title>
      <link>https://trid.trb.org/View/2561835</link>
      <description><![CDATA[Distributed Acoustic Sensing (DAS) has recently emerged as a promising technology for traffic monitoring. It transforms standard fiber-optic telecommunication cables into an array of vibration sensors capable of capturing vehicle-induced subsurface deformation with high spatio-temporal resolution. In this study, we propose a deep learning framework for the detection and velocity estimation of traffic flow. Our neural network based model yields accurate and well-resolved vehicle localization and speed tracking, outperforming off-the-shelf Dynamic Time Warping based solutions while achieving an order of magnitude faster processing time. A multi-day comparison with dedicated sensors installed along an urban highway shows a strong correlation, even under dense traffic conditions.]]></description>
      <pubDate>Mon, 23 Mar 2026 17:14:34 GMT</pubDate>
      <guid>https://trid.trb.org/View/2561835</guid>
    </item>
    <item>
      <title>Evaluating Variations of Strains in Geocell under Freeze-Thaw Cycles Using Distributed Fiber Optic Sensors</title>
      <link>https://trid.trb.org/View/2663279</link>
      <description><![CDATA[In cold climates, cyclic freeze-thaw (F-T) action poses significant challenges to the mechanical stability of geocell-reinforced earth structures. Limited studies have evaluated the F-T responses of the geocell-reinforced earth structures; however, no research has reported the performance of geocell itself under F-T cycles. To fill this research gap, this study investigated the strain of novel polymeric alloy (NPA) geocells immersed in water under F-T cycles with high-resolution distributed fiber optic sensors (DFOS). Leveraging the unique capability of DFOS to provide continuous, distributed measurements of physical parameters along the optical fiber’s path, it captured localized strain heterogeneity and material degradation patterns across the geocell structure. This technology provides unparalleled benefits as compared to conventional point-based and electrical measurement methods, such as strain gauges. Experimental results revealed compressive strain accumulation during the freezing phase, partial strain recovery in the thawing phase, and cumulative residual deformation across successive F-T cycles. Variations in results across measurement points highlighted spatial heterogeneity in geocell response. The findings from this ongoing study provided some insights into the material durability, while the longer-term monitoring would advance the understanding of geocell material degradation by F-T cycles, informing more resilient design and improved long-term performance predictions for geocell-reinforced earth structures.]]></description>
      <pubDate>Thu, 12 Mar 2026 08:52:52 GMT</pubDate>
      <guid>https://trid.trb.org/View/2663279</guid>
    </item>
    <item>
      <title>Performance Comparison Steel Rebar vs GFRP in Reinforced Concrete Structures</title>
      <link>https://trid.trb.org/View/2659367</link>
      <description><![CDATA[The paper outlines the history of innovative ASTM specification for corrosion resistant steel and GFRP bars for concrete reinforcement. A review of the mechanical properties of steel vs GFRP rebars will be presented along with the design codes and tools that are available for engineers. Extensive design examples utilizing the ACI and AASHTO codes will be presented along with a thorough evaluation of the carbon footprint (EPDs) for all the reinforcing products.]]></description>
      <pubDate>Thu, 12 Mar 2026 08:52:52 GMT</pubDate>
      <guid>https://trid.trb.org/View/2659367</guid>
    </item>
    <item>
      <title>C11 Development of a System-Level Distributed Sensing Technique for Long-Term Monitoring of Concrete and Composite Bridges</title>
      <link>https://trid.trb.org/View/2669644</link>
      <description><![CDATA[The research problem we are trying to solve is the long-term monitoring problem of bridges (e.g., concrete and composite bridges), using multiple modes of sensing technology including fiber optic (BOTDA), optical, and electromagnetic (GPR) sensors. We instrumented a composite bridge (Grist Mill Bridge in Hampden, Maine) with sensing textiles. We have developed structural health monitoring algorithms to process the experimental measurement collected from Grist Mill Bridge (Hampden, ME) to study the long-term bridge monitoring problem. We have developed bridge models for extracting the flexural rigidity (EI) of the bridge. We will use it as one of the indicators for long-term health monitoring. We developed a bound approach to determine the structural properties of bridges by using single-point optical measurement.]]></description>
      <pubDate>Mon, 02 Mar 2026 13:24:07 GMT</pubDate>
      <guid>https://trid.trb.org/View/2669644</guid>
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