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    <title>Transport Research International Documentation (TRID)</title>
    <link>https://trid.trb.org/</link>
    <atom:link href="https://trid.trb.org/Record/RSS?s=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" rel="self" type="application/rss+xml" />
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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>Transport Research International Documentation (TRID)</title>
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      <link>https://trid.trb.org/</link>
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    <item>
      <title>Integration of fatigue life assessment into structural optimization of a railway vehicle bogie frame: development and application of a new methodology</title>
      <link>https://trid.trb.org/View/2685669</link>
      <description><![CDATA[Rolling stock manufacturers are increasingly developing innovative structural solutions aimed at enhancing the quality and reliability of railway vehicle components, thereby enhancing the existing standard platforms. Structural optimization processes represent an effective strategy to reduce manufacturing costs by promoting geometries that are simpler to design and fabricate. While structural optimization is now a well-established practice in the railway sector, the integration of fatigue considerations into this process remains limited. Although several studies in the literature attempt to address fatigue through various approaches, none have proven entirely satisfactory. This research aims to bridge this gap by introducing a novel methodology capable of automatically computing fatigue-related parameters, thereby enabling a parallel fatigue performance evaluation throughout the entire optimization process, iteration by iteration. The methodology is implemented via a dedicated software tool, which can be adapted with minimal modifications to interface with most commercial finite element platforms. The proposed approach has been applied to the structural optimization of a metro bogie frame. The methodology was then employed in multiple activities: iterative fatigue monitoring during the optimization process; extension of a previous study by incorporating fatigue behavior; reconstruction of the final post-optimization geometry based on fatigue-driven design considerations, thus ensuring manufacturability and fatigue resistance. Although not all potential uses have been fully explored, the proposed methodology has demonstrated its effectiveness as a valuable tool for integrating fatigue into structural optimization, ultimately enabling the designer to reconstruct fatigue-aware final geometries.]]></description>
      <pubDate>Mon, 13 Jul 2026 13:58:16 GMT</pubDate>
      <guid>https://trid.trb.org/View/2685669</guid>
    </item>
    <item>
      <title>Full-scale fatigue performance evaluation of a novel frame-type ballastless slab track: Insights from the Istanbul airport metro project</title>
      <link>https://trid.trb.org/View/2691019</link>
      <description><![CDATA[In recent years, ballasted railway tracks have increasingly been replaced by ballastless slab track systems, including area- and frame-type designs, on critical infrastructure such as high-speed railways, tunnels, and bridges. These systems offer enhanced stability, faster and more comfortable travel, and reduced maintenance costs. Among these, frame-type slab tracks are more cost-effective to produce compared to area-type tracks. Therefore, this study evaluates the fatigue behavior of a newly developed frame-type ballastless slab track system installed on the Istanbul Airport metro line. A full-scale experimental setup was used, following an established testing protocol. While the system successfully satisfied fatigue performance criteria over three million cycles, with maximum railhead displacements below 1.1 mm, it failed to meet vertical static stiffness requirements due to variations in under-rail pad properties. Subsequent component-level tests revealed high stiffness heterogeneity among pads. These findings underscore the importance of quality control in pad production and suggest that current testing procedures should incorporate variable stiffness values to better simulate in-service behavior.]]></description>
      <pubDate>Mon, 13 Jul 2026 10:45:19 GMT</pubDate>
      <guid>https://trid.trb.org/View/2691019</guid>
    </item>
    <item>
      <title>Impact of slip velocity-dependent friction coefficient on surface traction, wear, RCF and curve squeal noise prediction in wheel-rail contact</title>
      <link>https://trid.trb.org/View/2685647</link>
      <description><![CDATA[Wheel-rail contact friction coefficient is often assumed to be constant through the entire contact patch for the calculation of surface traction. In reality, however, the friction value in a certain point decreases when transitioning from adhesion to slip regimes. Including this friction coefficient behaviour in the estimations of surface traction on the contact patch can potentially provide more accurate calculations of wear and rolling contact fatigue (RCF). In the present work, a slip velocity-dependent friction coefficient is implemented in the tangential contact solver using the concept of ‘Friction Memory’. The effect of this implementation on traction estimations and on the prediction of wear and RCF is analysed by comparing the results with a case with constant friction coefficient in the contact patch. Furthermore, the slip velocity-dependent friction coefficient provides a creep curve with a maximum creep forces value, and a decreasing creep force for higher creepages. This is commonly known as one of the possible mechanisms of curve squeal noise generation. The results provide insights into the likelihood of curve squeal generation, and an on-set curve squeal noise detection technique is proposed that also accounts for the influence of profile changes due to wear.]]></description>
      <pubDate>Wed, 01 Jul 2026 09:39:43 GMT</pubDate>
      <guid>https://trid.trb.org/View/2685647</guid>
    </item>
    <item>
      <title>Some Impacts of Traffic Management Decisions on the Residual Life of a Long-Span Bridge</title>
      <link>https://trid.trb.org/View/2671088</link>
      <description><![CDATA[Bridges are important links in our road infrastructure network, making it possible to carry all types of goods and creating economic growth. This is particularly the case for (some) long-span bridges that cross main rivers, and for which there exist no alternative. These long-span bridges are often composed of high concrete piers, and steel orthotropic decks, which permit big crossings. These types of bridges are sensible to specific parameters of the traffic, possibly reducing their residual lifetime when introducing new traffic management procedures. This is the case for platooning, where vehicles are closer to each other than regular traffic and whose lateral positions are aligned in the lane. After presenting the context, this paper will show the assumptions and boundary conditions for our work. Without big emphasis on the calculations themselves, we will give some results in terms of modification of the residual lifetime of these bridges.]]></description>
      <pubDate>Tue, 30 Jun 2026 09:45:10 GMT</pubDate>
      <guid>https://trid.trb.org/View/2671088</guid>
    </item>
    <item>
      <title>Reliability Evaluation of Bridge Network System Based on Graph Neural Network (GNN)</title>
      <link>https://trid.trb.org/View/2711499</link>
      <description><![CDATA[The increase in the scale and complexity of bridge structures has limited the existing reliability assessment methods based on probability statistics and finite element analysis in multi-dimensional data fusion and structural topology dependency modeling, making it difficult to effectively capture the high-dimensional correlation characteristics between bridge components. This article is based on Graph Neural Network (GNN) to construct a reliability evaluation model for bridge systems. The bridge structure is simplified into a node edge form, and the component state is represented by node attributes, while the connection relationship is described by edge attributes. Firstly, this article establishes a bridge structure diagram using historical monitoring data and sensor measurement point information; Secondly, feature extraction and normalization are used to input the stress, strain, fatigue life and other parameters of the component into the GNN model; Then, structural information fusion is achieved through Graph Convolutional Network (GCN), and key node feature weights are strengthened using Graph Attention Network (GAT) mechanism; Finally, a sample set is generated through Monte Carlo simulation to validate the system reliability indicators output by the model. Taking a highway arch bridge as an example, when the load level is increased to 1.3, the reliability of the model in this paper still reaches 0.804, and the highest accuracy is 0.948, which verifies the accuracy and stability of the method.]]></description>
      <pubDate>Tue, 30 Jun 2026 09:45:06 GMT</pubDate>
      <guid>https://trid.trb.org/View/2711499</guid>
    </item>
    <item>
      <title>Reducing Test Campaigns through Advanced Yield Surface Modeling for New Aircraft Metal Grades</title>
      <link>https://trid.trb.org/View/2712141</link>
      <description><![CDATA[Qualification of new aerospace alloys requires extensive mechanical testing to capture anisotropy and ensure reliable performance under complex loading conditions. This process is costly and time-consuming, particularly with emerging manufacturing routes such as additive manufacturing. Advanced yield surface prediction offers a route to reduce test campaigns by linking microstructural features to macroscopic constitutive models. In this work, Digimat is employed as a multi-scale material modeling platform to generate yield surfaces of polycrystalline metals using computational homogenization. Representative volume elements (RVEs) are constructed from experimental texture and grain morphology data, and their response under multiaxial loading is simulated using a crystal plasticity framework. The computed yield loci are then fitted with phenomenological functions (e.g. Yld2000-2D), enabling calibration of anisotropic yield models from virtual testing. As a case study, an AA6016-T4 sheet with strong cube texture is modeled and validated against experimental data, including yield stresses and Lankford coefficients in multiple directions. The predictive capability of the approach is further assessed through a cup drawing simulation in Simufact, where earing behavior is accurately reproduced. These results demonstrate that digital yield surface prediction can capture anisotropic plasticity and provide reliable input to forming simulations while significantly reducing experimental requirements. This capability lays the foundation for more efficient alloy qualification, with direct impact on fatigue and damage tolerance modeling in aerospace applications.]]></description>
      <pubDate>Tue, 23 Jun 2026 13:36:31 GMT</pubDate>
      <guid>https://trid.trb.org/View/2712141</guid>
    </item>
    <item>
      <title>Non-Intrusive Fatigue Detection for Pilots</title>
      <link>https://trid.trb.org/View/2712086</link>
      <description><![CDATA[Pilot fatigue represents a critical concern in aviation safety, as it can significantly impair cognitive functions, decision-making abilities, and reaction times. In addition to decreasing performance, in-flight chronic fatigue has negative long-term health effects. Possible causes of fatigue include sleep loss, extended time awake, circadian phase irregularities and workload. Conventionally, the risk due to fatigue in aerospace is reduced by flight time limits and controlled rest requirements. Despite regulations limiting flight time and enabling optimal rostering, fatigue cannot be prevented completely. Hence, there is need to detect pilot fatigue in real time.There is ongoing research to detect pilot fatigue using devices that can capture Electroencephalogram (EEG) and Electrocardiogram (ECG). Though these devices have high fidelity, they are intrusive and can limit pilot activity. This limitation could potentially be overcome by non-intrusive devices such as a smart watch/wrist band/goggles which can measure physiological parameters that provide insights into pilot’s mental health. Heart rate variability (HRV) is one such physiological marker of interest for detecting pilot fatigue in real time. HRV can be effectively derived by processing raw Photoplethysmography (PPG) signals to gain insights into the autonomic nervous system, enabling the assessment of physiological state. Wearable devices such as a wristwatch are used in the current study to measure PPG data. Time and frequency domain analysis were performed to evaluate the potential of HRV indices. The analysis of R-R intervals and the Low Frequency / High Frequency (LF/HF) ratio plots, derived from HRV signals, revealed distinct characteristics that differentiate between an alert and a fatigued pilot. This study demonstrates a reliable non-intrusive method for detecting pilot fatigue and enhancing flight safety.]]></description>
      <pubDate>Wed, 10 Jun 2026 13:18:08 GMT</pubDate>
      <guid>https://trid.trb.org/View/2712086</guid>
    </item>
    <item>
      <title>Augmented Reality and Multimodal Interfaces for Astronaut Training and In-Orbit Operations</title>
      <link>https://trid.trb.org/View/2712079</link>
      <description><![CDATA[Augmented Reality (AR) and multimodal human–machine interfaces (MMI)— combining visual overlays, voice, gesture, eye- tracking, and biometric sensing—are maturing into flight-relevant technologies capable of transforming astronaut training and in-orbit operations. These interfaces can reduce task time, lower procedural errors, and mitigate cognitive workload, thereby strengthening crew autonomy and mission safety.Global operational experiences from International Space Station (ISS) augmented- reality trials and related international programs are synthesized to inform the proposed system architecture and validation framework: (i) an overview of India’s current AR/MMI-related ecosystem relevant to human spaceflight, including astronaut training pipelines and research collaborations; (ii) a mission-grade AR/MMI system architecture and multimodal fusion/decision logic suitable for human-rated operations; (iii) algorithms and programming examples for AR-driven finite-state-machine (FSM) procedures and workload-sensitive adaptation; and (iv) simulation-backed datasets across representative procedures indicating approximately 20 to 30 percent task-time reduction and approximately 40 to 50 percent error- rate reduction under controlled conditions (based on ten procedures and twenty-four simulated sessions for workload analysis).The findings reinforce that AR/MMI deployment can improve training throughput, reduce crew fatigue, and increase safety margins when designed with evidence gating, conservative confidence thresholds, and robust fallback modes. Recommendations include establishing a Human Space Flight Centre (HSFC) AR/MMI laboratory, conducting structured A/B validation trials, and committing resources for progressive demonstrations aligned with future in-orbit operations.]]></description>
      <pubDate>Wed, 10 Jun 2026 13:18:08 GMT</pubDate>
      <guid>https://trid.trb.org/View/2712079</guid>
    </item>
    <item>
      <title>Evaluation of Healing Potential of Asphalt Mixtures Modified with Nanoparticle-Enhanced Binders</title>
      <link>https://trid.trb.org/View/2712014</link>
      <description><![CDATA[This study investigates the fatigue and healing performance of asphalt mixtures incorporating nanoparticle-modified binders, using nanoclay and nanosilica. Two binders (PG 58-28 and PG 76-22) were selected to assess their effect on healing behavior. Asphalt mixtures were prepared using granite aggregates and UFGS Gradation 3, then subjected to mechanical and simulation-based evaluations. Cyclic fatigue testing was conducted using the asphalt mixture performance tester at 25°C, with a strain amplitude of 800 microstrains and a loading frequency of 10 Hz. To simulate in-service conditions, 10- and 20-minute rest periods were introduced after 25% of the specimen’s estimated fatigue life, representing early stage fatigue damage accumulation. Healing was quantified by comparing the number of cycles to failure (Nf) before and after rest period. Dynamic modulus testing and FlexPAVE™ simulations were also performed to assess viscoelastic behavior and long-term pavement performance. Results showed that both nanoclay and nanosilica modified mixtures exhibited notable improvements in fatigue life relative to the control, with nanoclay modified mixtures achieving the highest fatigue life improvement, while nanosilica modified mixtures demonstrated consistent intermediate gains across both binder types. FlexPAVE™ simulations indicated a 37% reduction in total fatigue damage over 20 years for nanoclay-modified mixtures with rest periods. Rutting and cracking resistance also improved significantly, as observed from indirect tensile asphalt cracking test and asphalt pavement analyzer tests. The findings confirm that nanomodification, especially with nanoclay, enhances the intrinsic healing capacity, fatigue resistance, and durability of asphalt mixtures. Incorporating rest periods in design further optimizes long-term performance, offering a sustainable strategy for modern pavement systems.]]></description>
      <pubDate>Tue, 09 Jun 2026 10:54:11 GMT</pubDate>
      <guid>https://trid.trb.org/View/2712014</guid>
    </item>
    <item>
      <title>Fatigue Resistance of High-Pressure Hydrogen Storage Vessels with Aluminum Liner</title>
      <link>https://trid.trb.org/View/2706236</link>
      <description><![CDATA[Although carbon fiber-reinforced aluminum-lined hydrogen storage vessels (Type III) exhibit outstanding specific strength and specific stiffness, the constraints imposed by their design parameters on fatigue performance and ultimate load-bearing capacity remain incompletely elucidated. We propose a fatigue life prediction method for high-pressure vessels that couples progressive damage in the fiber composite with cumulative damage in the metallic liner, aimed at forecasting the fatigue performance of Type III pressure vessels under cyclic loading. Furthermore, a finite element analysis systematically investigates the influence of key design parameters, for nominal pressure, liner diameter and liner thickness, on fatigue performance and ultimate load-bearing capacity. Results indicate that fatigue life significantly decreases with increasing nominal pressure and liner diameter, with nominal pressure exerting a more pronounced effect. Notably, altering the autoclave pressure alone cannot achieve a synergistic design that balances high load-bearing capacity and high fatigue life when the burst safety factor equals 2.25. More interestingly, we discover that appropriately increasing the pressure vessel's safety factor or liner thickness enables synergistic optimization of the overall structure. These findings provide reliable design approach for the structural design and life assessment of composite hydrogen storage pressure vessels.]]></description>
      <pubDate>Tue, 02 Jun 2026 11:12:49 GMT</pubDate>
      <guid>https://trid.trb.org/View/2706236</guid>
    </item>
    <item>
      <title>Propagation trends of cracks situated in bogie frames based on a rigid-flexible coupled vehicle dynamic model</title>
      <link>https://trid.trb.org/View/2673003</link>
      <description><![CDATA[The bogie frame of high-speed electric multiple units in China is subjected to more complex fatigue loads, significantly increasing the risk of fracture. However, current research lacks a comprehensive dynamic model that accounts for the presence of cracks in the flexible bogie frame within a rigid-flexible coupled vehicle system. To address this gap, this study develops a novel rigid-flexible coupled vehicle dynamic model to investigate the fracture behaviour of bogie frames with cracks. The motion equations of the vehicle model, incorporating crack effects, are firstly derived and validated for accuracy and effectiveness. Numerical results reveal that Mode-II fracture mode primarily governs crack propagation at all critical positions studied. It is observed that during vehicle operation, the maximum dynamic energy release rate generally increases with crack size, with the peak value shifting to different points along the crack front. Among the three critical positions examined, when a single crack is present, the crack at the curved area of the top cover plate exhibits a higher likelihood of propagation. In scenarios where two cracks are present, the crack located at the gearbox suspender seat is more prone to propagation.]]></description>
      <pubDate>Fri, 29 May 2026 14:09:16 GMT</pubDate>
      <guid>https://trid.trb.org/View/2673003</guid>
    </item>
    <item>
      <title>Behavior of Asphalt Overlays with Geogrids and Geocomposite Interlayer Systems</title>
      <link>https://trid.trb.org/View/2113159</link>
      <description><![CDATA[Geosynthetics in the form of geotextiles, geogrids, and geocomposites have been incorporated into pavement systems to enhance the service life of asphalt overlays by retarding reflective cracking. In this study, the performance of asphalt overlays reinforced with geogrids and geocomposite interlayer systems placed on pre-existing asphalt layer was evaluated. Specifically, both unreinforced and geosynthetic-reinforced, two-layered asphalt beam specimens prepared with a pre-existing 25 mm-deep notch (crack) in the bottom layer were tested under repeated four-point bending load conditions. The two-layered asphalt specimen consisted of a 45 mm-thick, old pavement layer collected from an existing highway as a bottom layer, a binder tack coat, the tested interlayer, and a 45 mm-thick hot mix asphalt (HMA) overlay. A glass-geogrid composite (GGC) involving a geotextile backing interlayer and two different types of geogrid interlayers, namely, a polyester geogrid (PET) and a polypropylene geogrid (PP) were used in this study. Repeated loading was applied to all specimens using a four-point bending configuration in a load-controlled mode at a frequency of 1 Hz. The performance of the different geosynthetic-reinforced specimens was compared against that of the control specimen (CS) and the improvement in fatigue life was estimated. Considering the specific products in this study, results indicate that all the geosynthetic-reinforced specimens resulted in extended fatigue life of overlays in relation to the CS, and among them, the best performance was obtained using the GGC.]]></description>
      <pubDate>Thu, 28 May 2026 17:09:38 GMT</pubDate>
      <guid>https://trid.trb.org/View/2113159</guid>
    </item>
    <item>
      <title>Effects of variable amplitude loading and random loading sequence on fatigue of welded joints made of high-strength steel in ship structural details</title>
      <link>https://trid.trb.org/View/2667021</link>
      <description><![CDATA[The aim of this study was to validate a local stress-based fatigue assessment approach, the 4R method, for assessing the fatigue strength of common welded ship structural details subjected to variable amplitude (VA) loads. The objective was to provide increased accuracy in fatigue strength estimations through the consideration of local elastic-plastic material behaviour, possible residual stress relaxation, and sequential effects of loading conditions. The VA load effects on fatigue strength of welded joints made of high-strength steel (690QT) were investigated by means of analytical calculations and experimental testing. Random VA load spectra were created for the fatigue tests based on different mean stress levels according to a two-parameter Weibull distribution. Additionally, a 3D scan-based solid finite element model of the longitudinal double-sided gusset joint was employed within the notch-based local fatigue assessments. The use of the scanned geometry reduced the scatter of the fatigue test results among specimens in the local approaches highlighting the importance of accurate consideration of real weld geometry in the determination of fatigue notch factors. Furthermore, the 4R method provided additional accuracy by considering the loading sequence and mean stress via mean-stress correction.]]></description>
      <pubDate>Tue, 26 May 2026 11:56:22 GMT</pubDate>
      <guid>https://trid.trb.org/View/2667021</guid>
    </item>
    <item>
      <title>Data-driven estimation of hull girder structural integrity using wave-induced motions</title>
      <link>https://trid.trb.org/View/2669911</link>
      <description><![CDATA[As modern ships grow larger, monitoring structural integrity becomes increasingly critical, particularly high-frequency hull-girder vibrations that accelerate structural fatigue. Since hull monitoring systems are expensive and difficult to maintain, this research explores using machine learning models based on ship motion sensors for prediction of the vertical bending moment (VBM), analysing in-service data of a 2800 TEU container ship. The tested models include LightGBM, Random Forest, XGBoost, Extra Trees, with LightGBM emerging as the best-performing and fastest framework, reinforcing its status as a state-of-the-art choice for tabular data regression tasks. Two spectral methodologies are developed: a frequency energy densities approach and a statistical-feature approach. Both consistently demonstrated that the optimal input for predicting VBM is a combination of heave, roll, and pitch motions, limited to cutoff frequency of 0.3 Hz for global wave-frequency loads, and bow acceleration, extended to 2.0 Hz to capture high-frequency hull vibrations. The report provides ship operators with a practical, cost-effective alternative to conventional strain measurement systems, predicting key VBM parameters such as the standard deviation and zero-crossing frequency, enabling fatigue analysis, critical for ensuring the structural integrity and operational safety of modern vessels.]]></description>
      <pubDate>Tue, 26 May 2026 11:56:22 GMT</pubDate>
      <guid>https://trid.trb.org/View/2669911</guid>
    </item>
    <item>
      <title>Probabilistic Assessment of the Fatigue Performance of Crumb Rubber Concrete Pavement: Fatigue Model Development and Reliability Assessment</title>
      <link>https://trid.trb.org/View/2666661</link>
      <description><![CDATA[Recycled postconsumer tyres show potential to be used as an alternative to natural aggregate in pavement concrete where fatigue performance is crucial. This paper presents four fatigue models for crumb rubber concrete, considering probabilistic concepts and statistical analysis. The probability of survival was incorporated in the form of McCall’s model, enabling it to serve as a user input for pavement design. A two-parameter Weibull distribution function is used to statistically analyze the fatigue life. Reliability assessment is carried out to predict the fatigue failure of pavement using the developed models. An illustrative example is presented, accompanied by a sensitivity analysis identifying the most influential variables for pavement fatigue failure. The reliability index and corresponding failure probability of pavement under different axle groups were determined. This study shows that the fatigue models developed based on plain concrete underestimate the fatigue life of crumb rubber concrete, emphasizing the necessity of updating the models for innovative materials. Additionally, it is found that the concrete flexural strength and axle group loads are the dominant variables influencing fatigue failure. It also highlights that the probability distributions of input variables can affect the sensitivity analysis results.]]></description>
      <pubDate>Tue, 26 May 2026 11:56:22 GMT</pubDate>
      <guid>https://trid.trb.org/View/2666661</guid>
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