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    <title>Transport Research International Documentation (TRID)</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>Transport Research International Documentation (TRID)</title>
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      <link>https://trid.trb.org/</link>
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    <item>
      <title>Impact of Tamping on Repeated Ballast Settlement</title>
      <link>https://trid.trb.org/View/2675872</link>
      <description><![CDATA[It has been confirmed that track irregularity gradually returns to its original shape after ballast tamping on ballasted track even under the same track and support structure conditions. However, the details of this mechanism are not yet clear. Therefore, we surveyed an actual situation using track inspection data for this phenomenon. In addition, we performed tests with small-model, discontinuum analysis for ballast density after ballast tamping and cyclic loading tests, to reveal the mechanism of reversion in settlement after and before ballast tamping.]]></description>
      <pubDate>Fri, 05 Jun 2026 16:41:22 GMT</pubDate>
      <guid>https://trid.trb.org/View/2675872</guid>
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    <item>
      <title>Monitoring Rail Bed Infrastructure Using Wireless Passive Sensing</title>
      <link>https://trid.trb.org/View/2663117</link>
      <description><![CDATA[Railroad ballast plays a critical role in distributing train loads, maintaining track stability, and ensuring proper drainage. Over time, intrusion of fines and moisture leads to ballast fouling, which can compromise track structural integrity and service life. Early detection of ballast fouling is therefore essential for effective maintenance and extending the operational life of rail infrastructure. This study investigated the use of passive harmonic transponders as a low-cost, sustainable, and long-lasting sensing solution for monitoring ballast conditions. Laboratory experiments were conducted using clean and fouled ballast under varying moisture levels and transponder embedment depths. The harmonic transponder is unique in that it returns a signal at twice the frequency of that which interrogates it, allowing clear separation of the transponder response from background reflections and noise. Two types of harmonic transponders, one is commercially available sensing device (RECCO, operated at fundamental frequency 890 MHz) and another university developed sensing device (developed by Czech Technical University, operated at 1.17 GHz), were tested to evaluate signal responses under different conditions. The results indicate that moisture content and embedment depth are the primary factors influencing signal attenuation, while fouling level plays a secondary role. The transponders operated at lower frequency demonstrated more stable performance, whereas the device operated at higher frequency was more sensitive to ballast heterogeneity and moisture fluctuations. The findings suggest that embedding passive harmonic transponders during new rail-bed construction can provide a built-in monitoring system capable of early fouling detection. Interrogation of the embedded transponders can be performed using portable units, moving trains, or drones, offering a flexible and non-destructive approach. Their low-cost and passive design eliminates the need for batteries or continuous power supply, reducing both maintenance requirements and environmental impact. This energy-efficient operation enables long-term field deployment with minimal resource consumption. As a result, harmonic transponders represent a practical and sustainable solution for continuous monitoring of fouling in railway ballast.]]></description>
      <pubDate>Thu, 04 Jun 2026 10:58:21 GMT</pubDate>
      <guid>https://trid.trb.org/View/2663117</guid>
    </item>
    <item>
      <title>An Alternative Approach to Track Settlement Prediction</title>
      <link>https://trid.trb.org/View/2113174</link>
      <description><![CDATA[Many empirical equations have been formulated in an attempt to model the settlement of ballasted railway track at an individual sleeper. Some equations have been used with vehicle track interaction models (VTI) to predict the development of differential settlement along the track iteratively and hence maintenance requirements, over potentially millions of cycles of load. For settlement equations to be suitable in such simulations, they require as an input a VTI model output that varies along the track, such as the force on the sleeper or the current resilient deflection range. For computational economy, these VTI simulations are usually run in large steps with the settlement predicted forward over many cycles. There is, however, no one generally applicable settlement equation, and it remains unclear whether the loss of accuracy that ensues from stepping the VTI analyses is acceptable. A realistic settlement equation needs to incorporate both stress- and load history-dependent behaviour. This paper proposes a new settlement model that allows for stress history and has the potential to be applied at every cycle within an iterative VTI simulation. The ballast layer is modelled by combining a nonlinear visco-elastic element to simulate the resilient response with a plastic-hardening element for permanent settlement. This leads to the calculation of permanent settlement without recourse to an explicit empirical equation. The parameters used in the model are determined using data from cyclic laboratory tests on a single sleeper. The effect of different loading histories on the model is considered.]]></description>
      <pubDate>Thu, 28 May 2026 17:09:38 GMT</pubDate>
      <guid>https://trid.trb.org/View/2113174</guid>
    </item>
    <item>
      <title>The Use of Microporous Membranes to Address Mud Pumping—UK Experience</title>
      <link>https://trid.trb.org/View/2113169</link>
      <description><![CDATA[The UK has one of the most complex, most heavily trafficked rail networks in the world. In most areas, there is little time available for maintenance or track renewal; as a result, the installation of a completely new roadbed is seldom justifiable. Over the last 30 years, the need for time-consuming treatments requiring large volumes of excavation and imported sub-ballast has been gradually eliminated by maximising the use of geosynthetics to enhance the mechanical performance of the existing sub-ballast and subgrade. However, prior to the introduction of microporous membranes there was no treatment for a severe subgrade erosion problem that did not require installation of a new granular layer before placing new ballast. The paper summarises early roadbed treatments, subsequent research work on geosynthetics and the development of a suite of geosynthetic treatments. These would mitigate problems with existing sub-ballast or subgrade before placing new ballast. The development of microporous membranes is then described. Finally, the paper summarises the current methodology used by network rail (NR) to select an appropriate treatment for any given site.]]></description>
      <pubDate>Thu, 28 May 2026 17:09:38 GMT</pubDate>
      <guid>https://trid.trb.org/View/2113169</guid>
    </item>
    <item>
      <title>A Deep Investigation into the Mechanisms and Factors Producing Mud Pumping</title>
      <link>https://trid.trb.org/View/2113156</link>
      <description><![CDATA[Mud pumping is a common issue facing all railroads in which wet fines within the ballast pump up around the ties and onto the surface of the track during train loading. This often corresponds to poor drainage, loss in track geometry, reduced ballast strength and stiffness, and in the worst case leading to ballast failure. Despite the prevalence of this problem, the mechanisms behind mud pumping and the factors influencing it are not fully understood, although past investigations have determined that fines and water in ballast, as well as repeated dynamic wheel loads, need to act together to produce mud pumping. An improved understanding is to how it will allow for better prediction of mud pumping and the negative effects associated with it, along with improved maintenance planning and techniques. Transportation Technology Center, Inc. (TTCI) has further reviewed and investigated mud pumping situations from both its “rainy section” test zone at the Facility for Accelerated Service Testing (FAST) near Pueblo, Colorado, and multiple revenue service locations in attempt to understand the underlying mechanisms behind mud pumping. The rainy section at FAST and some revenue service sites suggested that some (and possibly most) mud pumping situations are purely surficial, originating from the wet regions just around the ties. However, other mud pumping situations have shown a seasonal or perched water table below the bottom of the ties that appears to cause moisture and fines to pump up to the surface from the subgrade or lower ballast layer. TTCI plans to continue to investigate different mud pumping situations with the end goals of developing ballast maintenance guidelines and improving track substructure-induced track geometry degradation forecasting models.]]></description>
      <pubDate>Thu, 28 May 2026 17:09:38 GMT</pubDate>
      <guid>https://trid.trb.org/View/2113156</guid>
    </item>
    <item>
      <title>Dynamic Behavior Modeling of Full-Scale High-Speed Ballasted Track Using Discrete Element Method</title>
      <link>https://trid.trb.org/View/2113137</link>
      <description><![CDATA[Ballast layer dynamic behavior is critically important for railway track design and maintenance optimization. This paper presents findings on crosstie and ballast particle dynamic responses obtained from: (i) laboratory tests conducted at Zhejiang University innovative high-speed rail tester (ZJU-iHSRT) and (ii) discrete element method (DEM) simulations using algorithms with newly featured parallel computing capability developed at UIUC. Overall, more than 170,000 ballast particles and eight crossties were assembled in the DEM model. A proportional integral derivative (PID) controller was utilized to ensure realistic dynamic loads applied on crossties at three train speeds: (1) 108 km/h; (2) 252 km/h; (3) 300 km/h. Crosstie vibration velocities predicted using the DEM model matched closely with measurements from laboratory tests both in trends and in magnitudes. With Fourier transformation and Butterworth filter techniques implemented on ballast particle vibration velocities captured in the DEM model, inherent signal noise could be reduced, and as a result, the predicted ballast particle vibration trends matched closely with laboratory sensor measurements. However, individual ballast particle vibration magnitudes predicted by the DEM simulations revealed certain discrepancies with the measurements since velocity sensors used in the experiment only recorded vibration responses of an assembly of ballast particles. Further studies are necessary to reveal more detailed findings.]]></description>
      <pubDate>Thu, 28 May 2026 17:09:38 GMT</pubDate>
      <guid>https://trid.trb.org/View/2113137</guid>
    </item>
    <item>
      <title>Effect of Degraded Subgrade Stiffness on Rail Geometry and Train Vibration in High-Speed Railways</title>
      <link>https://trid.trb.org/View/2113129</link>
      <description><![CDATA[The degradation of railway subgrade stiffness will cause the acceleration of track degradation and deteriorate ride comfort and safety. It is hard to foresee these problems because both track substructure and subgrade are not visible to inspect. To study the influence of subgrade stiffness degradation on high-speed train and track system, 3D FEM analyses of the Fuxing trains and ballastless tracks were conducted to simulate different degradation conditions of roadbed stiffness. Firstly, the numerical model was verified by the field measurement of vibration velocity at the concrete base in the Beijing–Tianjin high-speed railway. Then, the influences of the degradation of roadbed stiffness on the rail displacements and train dynamics were analyzed. It is found that since the ballastless track had quite high rigidity, the uneven degradation of roadbed stiffness did not result in obvious additional increase of rail displacement. However, the uneven degradation of roadbed stiffness resulted in larger vibrations of train wheels. Particularly, the wheel accelerations were more sensitive to the uneven degradation of roadbed modulus amplitudes than the wavelengths.]]></description>
      <pubDate>Thu, 28 May 2026 17:09:38 GMT</pubDate>
      <guid>https://trid.trb.org/View/2113129</guid>
    </item>
    <item>
      <title>Mud Pumping in Ballastless Slab Track of High-Speed Railway and Its Remediation</title>
      <link>https://trid.trb.org/View/2113128</link>
      <description><![CDATA[Mud pumping is an increasing subgrade distress in ballastless slab track of high-speed railway, which heavily affects the driving comfort and even threatens the driving safety. However, the existing researches mostly focused on the water-induced distress in ballasted railway, and little attention was paid on the mud pumping in ballastless slab track. In this paper, both in-situ investigation and discussion of the conventional grouting remediation method were carried out. It can be observed that mud pumping mainly occurred at the expansion joint located at the ends of concrete base and then expanded to both sides of the expansion joint from 1–2 m. The defects in railway structure and the standing water stored in the roadbed layer were the two main factors contributing to mud pumping. In addition, based on the traditional chemical glue injection (CGI) remediation method (the shallow grouting repair method), a modified polyurethane grouting remediation method (the deep grouting repair method) was put forward to treat mud pumping in ballastless slab track.]]></description>
      <pubDate>Thu, 28 May 2026 17:09:38 GMT</pubDate>
      <guid>https://trid.trb.org/View/2113128</guid>
    </item>
    <item>
      <title>Ballasted Track Maintenance Modelling Using DEM</title>
      <link>https://trid.trb.org/View/2113123</link>
      <description><![CDATA[The ballast layer is a crucial component of railway tracks and it is hence essential to maintain it using adequate processes like tamping and stabilization. These will ensure that the density of the ballast layer is high enough to avoid shearing and settlement of the track under traffic. Ballasted tracks settle unevenly under the passage of trains. These geometrical defects are corrected by tamping which consists of lifting individually the sleepers and compacting the ballast underneath using vibrating tines. After tamping, the ballast layer is not homogeneous in terms of density along the track and requires stabilization before being commercially operational. This stabilization is performed either by regular trains at lower speeds for a given period hindering commercial operations, dynamic stabilization, or crib compaction. All these processes rely on vibrating the ballast layer using different approaches and have mainly been based on empirical observations. This paper describes an analysis of these ballasted track maintenance processes and their optimization using the discrete element numerical approach. This approach considers a granular material as an assembly of objects interacting through a specific contact law. In the present study, the code called LMGC90 has been used. The study includes a comparison of the processes in terms of ability to compact the ballast layer and lateral mechanical resistance of the track and their optimization. The final purpose of the project is to be able to specify optimal functioning parameters for all these processes.]]></description>
      <pubDate>Thu, 28 May 2026 17:09:38 GMT</pubDate>
      <guid>https://trid.trb.org/View/2113123</guid>
    </item>
    <item>
      <title>YOLOv8DTL: A Deep Transfer Learning Model for Few-Shot Rail Abrasion Detection</title>
      <link>https://trid.trb.org/View/2658869</link>
      <description><![CDATA[Rapid movement of the wheels on defective tracks and high-frequency friction collisions cause vibration between train and rail track, which greatly damages the lifespan of train components and is a significant cause of train derailments. Timely and accurate detection of rail abrasions is of great significance for ensuring the safety of railway operations. Deep learning based-automatic rail abrasion detection methods face the challenge of having fewer samples. Thus, a deep transfer learning framework based on improved YOLOv8 models is developed to detect rail abrasions to address the issue of few-shot learning. Then, deformable convolution networks (DCNs), convolutional block attention module (CBAM) and a new intersection over union (IoU) loss are introduced to improve the detection performance. The ablation experiments show that it effectively reduces the rate of missed and false abrasion detections. By comparing with the existing detection methods, the developed YOLOv8DTL method has higher precision, recall, and average precision under different abrasion size thresholds, indicating that it is more adaptable to detection tasks with different abrasion sizes. It also has the best robustness, maintaining a high level of detection efficiency.]]></description>
      <pubDate>Thu, 28 May 2026 17:09:35 GMT</pubDate>
      <guid>https://trid.trb.org/View/2658869</guid>
    </item>
    <item>
      <title>Accurate and Fast Quantitative Detection of Rail Corrugation Driven by Deep Learning Algorithms Using Adaptive Targeted Loss and Directed Metric Functions</title>
      <link>https://trid.trb.org/View/2658841</link>
      <description><![CDATA[Rail corrugation is a common rail surface defect in wheel–rail system and can significantly reduce the running quality of vehicles. Accurate and fast detection of corrugation is of great importance for uncovering its mechanism, predicting the evolution and planning maintenance. Traditional physical model-driven detection approaches rely on high-fidelity simulations and are poorly automated. The popular data-driven methods have good availability. However, they ignore the power of expert experience, resulting in limited accuracy. This paper presents a quantitative model for detecting rail corrugation based on measurement data and expert knowledge using deep learning (DL) algorithms. First, a data quality improvement framework is proposed to overcome the problem that conventional DL models may produce incorrect results when trained on low-quality datasets. Then, the characteristics of rail corrugation are deeply investigated and adaptive targeted loss and directed metric functions are developed according to the individual samples to build a detection model with better global feature regression and local detail convergence. Finally, the effectiveness of the proposed method is verified using on-site measurement data from a commercial metro line. Comparative analyses and ablation studies demonstrate the superiority of targeted loss and directed metric in terms of more accurate detection and scientific evaluation of rail corrugation roughness, respectively.]]></description>
      <pubDate>Thu, 28 May 2026 17:09:34 GMT</pubDate>
      <guid>https://trid.trb.org/View/2658841</guid>
    </item>
    <item>
      <title>A Semi-Supervised Diffusion-Based Paradigm for Vehicle-Track System Health Monitoring With Distributed Acoustic Sensing</title>
      <link>https://trid.trb.org/View/2658813</link>
      <description><![CDATA[Monitoring the health of vehicle-track system using deep learning and distributed fiber optic sensing presents a significant challenge due to the vast volume of real-time data and the difficulty of directly assessing the system’s condition. This often results in a severe imbalance in the distribution of extreme samples within the dataset, as large-scale signal collection typically lacks manual labeling. Consequently, supervised deep learning models face limitations due to insufficient labeled training data, while unsupervised deep learning models struggle with contamination from ambiguous samples whose health status remains unclear, hindering the development of robust and accurate models. To address this challenge, we propose SemAnoDiffusion, a semi-supervised model based on blur diffusion and an enhanced contrastive loss training approach. SemAnoDiffusion leverages a small set of labeled data alongside a large amount of unlabeled samples to accurately differentiate between anomalous data, normal data, and ambiguous samples that fall between these categories. In a case study of a metro system in Singapore, Distributed Acoustic Sensing and accelerometer arrays were used to collect track vibration responses as trains passed, with wheel flats occurring in a small subset of the trains. SemAnoDiffusion achieved 100% accuracy in classifying manually labeled normal and anomalous samples and effectively identified semi-damaged samples with unclear damage levels from the labeled data, successfully detecting all trains with wheel flats.]]></description>
      <pubDate>Thu, 28 May 2026 17:09:34 GMT</pubDate>
      <guid>https://trid.trb.org/View/2658813</guid>
    </item>
    <item>
      <title>Defect Detection and Classification of Railway Track System for In-Service MRT in Tropical Regions Using a Contactless TBMS and Adaptive-DBSCAN</title>
      <link>https://trid.trb.org/View/2658762</link>
      <description><![CDATA[Railway track systems serve as vital parts of urban mobility and intelligent transportation. Detecting defects in rail track systems not only avoids unexpected downtime but also safeguards passenger lives. Moreover, defect classification holds great economic value, which optimizes both traffic operations and management strategies. Compared to lab tests and track recording vehicle-based field tests, defect detection and classification using in-service trains is an emerging area of study. This enables continuous monitoring, increases carrying capacity, and reduces maintenance costs, but it also requires robust performance and compatibility with various weather conditions. Considering the precipitation characteristics in tropical regions, this paper proposes a novel online defect detection and classification method for mass rapid transit (MRT) railway track systems by integrating a non-contact train-borne monitoring system (TBMS) and an adaptive density-based spatial clustering of applications with noise (Adaptive-DBSCAN) algorithm. The TBMS is developed based on the inductive coupling theory, affirming real-time, contactless, and effective defect detection in tropical regions with high annual and intense short-duration rainfall. By assessing the voltage health ratio (VHR) of the train-rail electrical path, the TBMS can simultaneously monitor defects from rail, ballast, and sleepers/ fasteners. To classify the group of each defect for maintenance decisions, Adaptive-DBSCAN is applied using VHR as inputs and calibrates the algorithm parameters adaptively. Therefore, it avoids the exhaustive traversal typically needed for parameter selection in DBSCAN while preserving accuracy. Experiments conducted on an in-service MRT train (operating at 80 km/h) verified the effectiveness of the proposed method.]]></description>
      <pubDate>Thu, 28 May 2026 17:09:33 GMT</pubDate>
      <guid>https://trid.trb.org/View/2658762</guid>
    </item>
    <item>
      <title>A Large-Model-Enhanced Method for Rail Surface Defect Detection in Heavy-Haul Railway</title>
      <link>https://trid.trb.org/View/2659083</link>
      <description><![CDATA[The rail surface defects directly impact the safety and efficiency of heavy-haul train operations. Timely assessment of these defects is crucial for informed maintenance decisions, with precise defect detection at its core. In recent years, the accumulation of extensive rail inspection images has led to the application of numerous computer vision-based methods for pixel-level detection of rail surface defects. However, given the constraint of a limited number of labeled defect samples, ensuring the generalization and robustness of existing methods remains challenging, particularly across varying track conditions and complex heavy-haul scenarios. Thus, this paper introduces a Segment-Anything-Model (SAM)-enhanced method for the detection of rail surface defects. First, a shadow-detection-based algorithm is developed to extract the rail regions and mitigate background interference. Then a student-teacher-Simi-network (S-T-Simi)-based unsupervised method is designed to generate prompt information for SAM. Utilizing this prompt information, we develop a task-specified SAM for precise rail defect detection. Finally, comprehensive validation is performed using inspection data collected from diverse heavy-haul tracks. Experimental results indicate that the proposed method achieves highly accurate segmentation of rail defects.]]></description>
      <pubDate>Thu, 28 May 2026 17:09:32 GMT</pubDate>
      <guid>https://trid.trb.org/View/2659083</guid>
    </item>
    <item>
      <title>DiffRSD: Diffusion-Based and Integrity-Aware RGB-D Rail Surface Defect Inspection</title>
      <link>https://trid.trb.org/View/2659039</link>
      <description><![CDATA[Rail quality evaluation ensures the safety of railway transportation, where rail surface defect inspection is one of important tasks. Traditional methods adopt encoder-decoder framework, which is difficult to extract discriminative defect features to achieve the integrity of defect inspection. In contrast, we propose DiffRSD, a diffusion-based method which restores the defect mask from a noise conditional on the RGB-D defect image, showing integrity-aware defect inspection ability by the following strategies: (a) superpixel-aware corruption; (b) coarse-to-fine dilation supervision. The first strategy can struggle the model to restore a defect mask from a corrupted mask and make the model rectify the error prediction in the inference stage. The second strategy can locate the defect region in the initial decoding stage and further depict the clear boundary in the later decoding ones. Both strategies improve the performance of defect inspection by experimental verification on NEU RSDDS-AUG RGB-D defect dataset, thus advancing the proposed DiffRSD beyond state-of-the-art methods. The generalization of DiffRSD is further verified by the experiments in RGB rail surface defect inspection dataset and multi-modal pavement crack segmentation dataset. The proposed DiffRSD consistently shows the integrity-aware ability https://github.com/liuzywen/DiffRSD.]]></description>
      <pubDate>Thu, 28 May 2026 17:09:32 GMT</pubDate>
      <guid>https://trid.trb.org/View/2659039</guid>
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