<rss version="2.0" xmlns:atom="https://www.w3.org/2005/Atom">
  <channel>
    <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" />
    <description></description>
    <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>
    </image>
    <item>
      <title>Vehicle loads identification on beam bridges via a genetic algorithm</title>
      <link>https://trid.trb.org/View/2685130</link>
      <description><![CDATA[Due to the increasing volume of freight traffic and the growing practice of structural monitoring for existing bridges, it has become crucial to exploit modal analysis to identify the magnitudes of axle loads and the frequency content of the structural response induced by vehicle crossings. In this paper, starting from the comparison between a simplified analytical model of the bridge and the relevant experimental responses, a multi-parameter identification method is proposed to identify the magnitude, the number, the axle distance, and the eccentricity of moving loads crossing the bridge. A bending-torsional beam model subjected to travelling loads characterized by non-uniform spacing and magnitudes, has been adopted to describe the bridge dynamics. The identification procedure is based on the Differential Evolution genetic algorithm. The proposed method is tested and validated using numerically simulated dynamic responses including the presence of noise. The main contribution of this work concerns the combined use of a beam model and the DE algorithm to identify heavy vehicle load distributions for skew road bridges.]]></description>
      <pubDate>Thu, 09 Jul 2026 13:29:25 GMT</pubDate>
      <guid>https://trid.trb.org/View/2685130</guid>
    </item>
    <item>
      <title>Vision-based modal identification and weight estimation of vehicles</title>
      <link>https://trid.trb.org/View/2684460</link>
      <description><![CDATA[Vehicle weight estimation plays a crucial role in evaluating the fatigue life of bridges and pavements. Traditional Weigh-in-Motion (WIM) systems, which require sensors on bridges or pavements, are costly and have limited deployment flexibility. Recently, computer vision-based approaches have been explored to estimate vehicle weight from vibration characteristics of vehicles without the need for such sensors. However, most existing methods employ simplified vehicle models that ignore damping effects, resulting in limited estimation accuracy due to the inherently high damping ratio of vehicles. Moreover, these methods often rely on prior knowledge of the vehicle’s center of gravity (CoG), further restricting their general applicability.This study proposes an enhanced vision-based framework that overcomes these limitations. Vehicle vibrations are captured through object detection, filtering, and subpixel displacement estimation. The System Realization through Information Matrix (SRIM) method is then used to identify damped bouncing and pitching modes with complex mode shapes. Based on the state-space equations, a complex matrix formulation incorporating these damped modes enables simultaneous estimation of vehicle and axle weights, achieving over 84% accuracy in field tests.]]></description>
      <pubDate>Tue, 30 Jun 2026 10:21:01 GMT</pubDate>
      <guid>https://trid.trb.org/View/2684460</guid>
    </item>
    <item>
      <title>Dynamic performance reliability analysis of mixed passenger and freight railway turnouts based on neural networks</title>
      <link>https://trid.trb.org/View/2683114</link>
      <description><![CDATA[Mixed passenger and freight railway turnouts must meet competing demands: high-speed stability for passenger trains and tolerance of heavy axle loads for freight traffic. However, their coupled dynamic behavior and reliability remain insufficiently studied. This work introduces an efficient reliability analysis framework that combines back-propagation (BP) neural networks with Monte Carlo simulation (MCS), enhanced through latinized partially stratified sampling (LPSS). The framework enables accurate assessment of low-probability failure events while reducing the computational cost of high-dimensional implicit limit state functions. The findings reveal distinct passenger and freight response patterns within turnout zones and show that the proposed BP–MCS–LPSS method significantly outperforms conventional techniques.]]></description>
      <pubDate>Mon, 29 Jun 2026 09:12:10 GMT</pubDate>
      <guid>https://trid.trb.org/View/2683114</guid>
    </item>
    <item>
      <title>Enhancing roll and lateral stability of a 3-axle heavy vehicle through ARC and AFS control</title>
      <link>https://trid.trb.org/View/2706057</link>
      <description><![CDATA[Ensuring the stability of heavy-duty trucks is crucial to prevent accidents, as their high center of gravity makes them prone to rollovers. Alongside lateral stability, roll stability is a critical control parameter, and this paper proposes a strategy that uses two subsystems to simultaneously control the vehicle's roll angle and yaw rate. The active roll subsystem employs a Fuzzy-PD controller to generate the desired torque, while the active front steering (AFS) subsystem uses a sliding mode controller to track the desired yaw rate. To test the effectiveness of the proposed strategy, simulations are carried out using a full TruckSim model as a simulation model. The controller model consists of a 2-DOF 3-axle generalized bicycle model and a 1-DOF roll model. For instance, the simulation results in the J-Turn and Sine maneuver illustrate that the proposed strategy enhances the vehicle's stable speed range by up to 32.8%, particularly when the two subsystems operate simultaneously.]]></description>
      <pubDate>Tue, 23 Jun 2026 16:59:44 GMT</pubDate>
      <guid>https://trid.trb.org/View/2706057</guid>
    </item>
    <item>
      <title>Enhanced axle weight identification via multi-scale feature embedding assisted multi-task neural network</title>
      <link>https://trid.trb.org/View/2674488</link>
      <description><![CDATA[The growing prevalence of heavy and overloaded vehicles poses significant threats to bridge safety, highlighting the critical need for accurate axle weight identification. To address this challenge, this study proposes an enhanced axle weight identification method based on multi-scale feature embedding assisted multi-task neural network (MSMTNN). This approach only utilizes a short bridge response recorded after the vehicle enters the bridge, eliminating the need of full-time structural response. In the feature embedding stage, MSMTNN integrates multi-scale feature extraction through parallel global and local embedding modules. The global module extracts feature directly from measured accelerations, while the local module reformulates the response into a two-dimensional representation using the overlapping segmentation strategy, thereby enhancing spatiotemporal feature learning from limited data. The axle weight identification task is reformulated as a multi-task regression problem with each axle load predicted by an independent regressor. To ensure balanced convergence across tasks, a dynamic average weighting strategy based on loss variation rates is introduced. Numerical simulations on a simply-supported beam structure demonstrate that the identification accuracy of the MSMTNN achieves 95.61 % for front axle and 96.59 % for rear axle under noise-free conditions and maintains 91.46 % and 94.06 % under 15 % Gaussian noise. Laboratory experiments further confirm the practicality and application potential of the proposed method for real-world bridge monitoring.]]></description>
      <pubDate>Wed, 17 Jun 2026 12:23:20 GMT</pubDate>
      <guid>https://trid.trb.org/View/2674488</guid>
    </item>
    <item>
      <title>Load distribution and fatigue damage in railway open deck girder bridges through synergistic field testing and FE modelling</title>
      <link>https://trid.trb.org/View/2708276</link>
      <description><![CDATA[This paper summarises observations and results obtained from diagnostic testing and a complementary finite element model of a 140-year old short-span open-deck metallic girder bridge. A purpose-built frame was constructed around the bridge in order to apply the load in a form that represents passenger train axle loading. Testing was carried out under different axle load positions on the bridge with and without the timber deck superstructure to quantify load distribution effects on the supporting bridge members. Minor asymmetries, due to the location of offset stiffeners on main girder webs, and imperfections in the timber deck, in the form of unintended camber in the solid beams, played a significant role in the recorded bridge response, manifested through U-frame action deformation profiles and cross-girder bending stresses. Prompted by these test observations and using appropriate finite element models, a novel numerical sensitivity study was undertaken to quantify, for the first time, the effect of these factors on fatigue damage for a typical train passage. It is demonstrated that the synergistic application of load testing and numerical modelling can add value to our understanding of bridge response and inform issues related to static and fatigue assessments leading to more reliable service life predictions.]]></description>
      <pubDate>Wed, 03 Jun 2026 09:07:20 GMT</pubDate>
      <guid>https://trid.trb.org/View/2708276</guid>
    </item>
    <item>
      <title>Investigating Flexible Pavement Responses and Performance under Trunnion Axle Loading Using Three-Dimensional Finite Element Modeling and Field Validation</title>
      <link>https://trid.trb.org/View/2701381</link>
      <description><![CDATA[The growing demand for high-capacity freight vehicles has heightened the need to evaluate the impact of heavy truck axle configurations on pavement behavior. This study employed a validated three-dimensional (3D) finite element (FE) model to evaluate the structural response of flexible pavements under trunnion and tandem axle configurations at two vehicle speeds (35 and 55 mph), using legal load levels of 60 and 34 kip, respectively. Then, key pavement responses (i.e., tensile strain, stress, and vertical displacement) were assessed. Also, pavement performance (i.e., fatigue cracking and subgrade rutting) was evaluated. The results showed that the trunnion axle generated higher tensile strain, von Mises stress, maximum principal stress, vertical stress, and vertical displacement than the tandem axle. This increase in pavement responses is primarily attributed to the trunnion’s shorter axle spacing, which causes overlapping stress zones and amplifies strain concentrations within the asphalt and subgrade layers. Concerning pavement performance, the trunnion axle exhibited lower fatigue life and lower resistance to subgrade rutting. Vehicle speed was also found to influence pavement response, with lower speeds producing higher stress, strain, and displacement levels for both axle types. Model predictions were validated using field measurements from the MnRoad test site in Minnesota, USA, confirming consistent trends in pavement responses under dynamic axle loading. These findings offer insight into the roles of axle configuration, maximum load, and truck speed in determining flexible pavement performance, thereby supporting better-informed design and evaluation practices.]]></description>
      <pubDate>Thu, 14 May 2026 17:01:53 GMT</pubDate>
      <guid>https://trid.trb.org/View/2701381</guid>
    </item>
    <item>
      <title>Simulating the effects of structural discontinuities on the long-term behavior of the ballasted railway track</title>
      <link>https://trid.trb.org/View/2669741</link>
      <description><![CDATA[Structural discontinuities in railway tracks have proven challenging from a maintenance perspective. These discontinuities can lead to uneven settlements, reducing serviceability of the railway network and increasing the track’s dynamic loading. To optimize the long-term performance of railway structures, it is essential to evaluate different design solutions under varying loading conditions to identify potential risk factors early. Consequently, this study proposes a novel computational model for simulating the dynamic long-term behavior of ballasted railway tracks. The proposed model enables computationally efficient simulation and provides an innovative mathematical framework for analyzing the mechanical behavior of structural discontinuities, allowing detailed consideration of substructure and subsoil properties, including their variations along the longitudinal direction of the track. Simulations were conducted to investigate the effects of bridge transition zones and rail defects on the short- and long-term behavior of the track for two vehicle types. In addition, extensive field measurement data were utilized for model verification. Based on simulations, the axle load appears to be the primary factor influencing the long-term performance of railway transition zones. However, for more localized defect types, the significance of driving speed and the unsprung mass of rolling stock becomes more pronounced. Overall, the findings highlight the nonlinear relationship between vehicle loading and structural deterioration, emphasizing its strong dependence on track properties.]]></description>
      <pubDate>Thu, 07 May 2026 09:20:59 GMT</pubDate>
      <guid>https://trid.trb.org/View/2669741</guid>
    </item>
    <item>
      <title>Dynamics Modeling for Modular Distributed-Drive Vehicles With Unknown Parameters</title>
      <link>https://trid.trb.org/View/2659132</link>
      <description><![CDATA[Modular distributed-drive (MDD) vehicles are an optimal solution for transporting oversized and overweight cargo. This study develops an extensible three-degree-of-freedom dynamics modeling approach for MDD vehicles. To calibrate unknown dynamics parameters, a novel Markov-based adaptive particle swarm optimization with hierarchical learning (MAPSO-HL) is designed. A six-axle MDD vehicle serves as a case study. The MDD dynamics model is calibrated across two driving scenarios, two adhesion conditions, and three load states using eight optimizers, resulting in 96 calibrations. The results indicate that the MAPSO-HL performs the best, with average calibration improvements of 54.636% and 40.78% over standard particle swarm optimization (PSO) and adaptive particle swarm optimization (APSO), respectively. Meanwhile, the calibrated dynamics model accurately characterizes the kinematic acceleration of the MDD vehicle, highlighting the effectiveness of the extensible dynamics model and its parameter calibrator. This research contributes a unified dynamics modeling approach for the MDD vehicles, which is beneficial for engineering applications.]]></description>
      <pubDate>Wed, 29 Apr 2026 09:10:07 GMT</pubDate>
      <guid>https://trid.trb.org/View/2659132</guid>
    </item>
    <item>
      <title>Line tests of wheel-rail adhesion recovery under braking using an electric multiple unit train</title>
      <link>https://trid.trb.org/View/2657051</link>
      <description><![CDATA[Rail surface contaminations causing wheel-rail low adhesion problems may gradually be cleaned by passing wheelsets, leading to the phenomenon of adhesion recovery. This suggests an enhancement in available adhesion under low adhesion conditions for a train composed of multiple coaches. Using a four-coach Electric Multiple Unit (EMU) metro train equipped with a wheel slide protection system, field tests are conducted on a test line to measure the adhesion recovery rate of braking wheelsets under soapy water contamination. Initial braking speeds range from 100 km/h to 140 km/h, and tests under dry conditions are also conducted for reference and braking force calibration. Instantaneous creepages for different wheelsets are determined by recorded train speeds and rotational speeds of wheelsets, and the corresponding creep forces are calculated by measured vehicle speed, angular speeds of wheelsets, and brake cylinder pressures. Creep curves for different wheelsets are then obtained in consideration of the calculated axle load transfer. Adhesion coefficients corresponding to friction saturation are further derived for sliding wheelsets, typically located in the leading part of the train, with a maximum measured creepage of up to 11%. The number of passing wheelsets required to achieve adhesion recovery meeting braking requirements is also identified. The results indicate that the adhesion coefficient is as low as approximately 0.075 of the 1st axle at speeds between 93 and 132 km/h, with an adhesion recovery rate of 0.0040/axle among the first seven wheelsets. Such measurement technology holds potential application in operational trains, as it requires no significant modifications.]]></description>
      <pubDate>Wed, 29 Apr 2026 09:10:06 GMT</pubDate>
      <guid>https://trid.trb.org/View/2657051</guid>
    </item>
    <item>
      <title>A nonlinear optimisation considering axle load transfer for enhancing adhesion utilisation of a locomotive with an oblique traction rod</title>
      <link>https://trid.trb.org/View/2659288</link>
      <description><![CDATA[Locomotives are susceptible to wheel slip when passing through the low adhesion sections. Wheel slip is more likely to occur on wheelsets with reduced axle load due to axle load transfer (ALT). To address this problem, we propose a nonlinear optimisation method for traction forces to improve the locomotive adhesion utilisation under the rail adhesion limitation. Firstly, we established an improved ALT model considering the rotation of the traction rod and verified its effectiveness through a field test. Then, we applied a sequential quadratic programming algorithm to optimise the traction forces with the constraints of the adhesion and ALT. Based on the proposed method, we analysed the impact of static axle load, traction force limitations, and coupler pitch angle on the optimisation results. The results indicate that the optimisation method can effectively utilize the axle load and adhesion of all wheelsets under different factors. A train dynamics model incorporating the optimisation module is further developed to investigate the method's effectiveness in improving the adhesion utilisation of locomotives. Simulation results demonstrate that the proposed optimisation method can enhance the maximum traction force of the locomotive, effectively preventing the wheelset with reduced axle load from slipping.]]></description>
      <pubDate>Thu, 16 Apr 2026 13:54:14 GMT</pubDate>
      <guid>https://trid.trb.org/View/2659288</guid>
    </item>
    <item>
      <title>Axle-information-free identification of vehicle moving forces from bridge displacement using U-Net</title>
      <link>https://trid.trb.org/View/2683178</link>
      <description><![CDATA[Traffic loads are the main source of live loads on bridge structures, and accurately identifying the dynamic axle load of vehicles is of great importance for Bridge Structural Health Monitoring (BSHM). Most of the past research established the relationship between moving forces and structures through analytical or finite element methods to estimate axle loads, which was proved to be ill-conditioned and inefficient. In this paper, a Moving Force Identification (MFI) method based on U-shaped Network (U-Net) is proposed, which does not require axle position information as a prior knowledge, but is completely based on monitoring data. Firstly, the dynamic response of the finite element model under traffic load is used to establish a data set. In this process, a large number of random traffic flows with different axle loads, axle spacings and vehicle speeds are generated to simulate the operation process of real bridges. Then, the deep learning model is trained with dynamic displacement data as input and dynamic axial load as output. The accuracy of the model is verified by numerical simulation and the robustness of the model is tested. Finally, the practicality of the proposed method is tested by the actual bridge experiment. The results of the actual bridge test show that the mean error of the axle force identification of the proposed method is 6.28 %, indicating that the proposed method has certain application prospects in practical engineering.]]></description>
      <pubDate>Mon, 13 Apr 2026 16:48:10 GMT</pubDate>
      <guid>https://trid.trb.org/View/2683178</guid>
    </item>
    <item>
      <title>Axle load violations model for sustainable financing of road pavement maintenance in Nigeria</title>
      <link>https://trid.trb.org/View/2684430</link>
      <description><![CDATA[The sustainability of Nigeria’s federal highway network is increasingly undermined by persistent axle load violations among heavy goods vehicles (HGVs). Overloaded axles accelerate pavement deterioration, escalate lifecycle maintenance costs, and compromise freight system reliability. This study integrates engineering-based deterioration modelling with operations management principles to estimate pavement damage costs and develop a penalty-based financing framework. Using weigh-in-motion (WIM) systems, traffic and axle load data were collected across three freight-intensive corridors, namely Lokoja-Abuja, Ilorin-Jebba, and Abakaliki-Ogoja. Equivalent Single Axle Load (ESAL) analysis and econometric modelling were employed to quantify incremental damage and calibrate penalty functions. Findings reveal systemic overloading, with corridor-specific damage costs ranging from ₦0.74 to ₦5.70 per ESAL and violation rates exceeding 70% on high-intensity routes. A log-linear penalty model was developed, explaining over 80% of the variability in cost recovery estimates. The study demonstrates that monetizing axle load violations through calibrated penalties can transform enforcement into a sustainable financing mechanism. The contribution lies in extending operations management theory by embedding asset management, externality internalization, and game-theoretic principles into road infrastructure governance. The proposed model offers a scalable framework for enhancing infrastructure resilience, optimizing maintenance funding, and improving regulatory compliance in Nigeria and other Sub-Saharan African economies.]]></description>
      <pubDate>Wed, 08 Apr 2026 13:41:51 GMT</pubDate>
      <guid>https://trid.trb.org/View/2684430</guid>
    </item>
    <item>
      <title>Analysis of High-Speed Vehicle-Bridge Interactions</title>
      <link>https://trid.trb.org/View/2159531</link>
      <description><![CDATA[Since railroads are constructed mostly as double tracks, the eccentricity exists between the vehicle axles and the neutral axis of cross-section of the bridge. Therefore, this eccentricity needs to be taken into account in the accurate numerical analysis of the dynamic behavior of the bridge. In this study, a model for the simplified 3-dimensional analysis of high-speed vehicle(KTX)-bridge interactions is presented by considering the eccentricity of axle loads. Through results obtained by the analyses of an existing bridge, the investigations into the influence of vehicle speed on vehicle-bridge interactions are carried out.]]></description>
      <pubDate>Sat, 07 Mar 2026 16:05:42 GMT</pubDate>
      <guid>https://trid.trb.org/View/2159531</guid>
    </item>
    <item>
      <title>Design methodology for heavy-haul railway track foundations under 40-tonne axle loads</title>
      <link>https://trid.trb.org/View/2627383</link>
      <description><![CDATA[Heavy haul railway, recognized for high capacity and efficiency, is gaining traction worldwide. However, heavier axle loads can accelerate track and foundation deterioration without robust structural design. This study proposes a practical design methodology for heavy-haul track foundations under 40-tonne axle loads. Short axle spacing was considered in analyzing train load distribution, revealing significant stress superposition on the subgrade. Based on general strength, deformation, and long-term stability, a standardized multi-axle load pattern (4Z1800/2400) is introduced to capture these effects. The recommended design ensures cumulative deformation remains within the subgrade “working zone” by integrating induced stress levels and fill materials, surpassing single-factor approaches. Long-term stability, indicated by cumulative deformation approaching slow convergence, serves as the control requirement. Proposed design solutions include: 1) roadbed thickness of 3.5 m, with subgrade reaction modulus (K30) ≥110 MPa/m below the roadbed; 2) lower roadbed of 2.3 m, K30 ≥130 MPa/m, with thickness adjustments as needed; 3) upper roadbed of 0.7 m using high-quality graded gravel. These measures effectively control deformation, thus ensuring the performance and longevity of heavy-haul track foundations under extreme axle loads. The outcomes support global heavy-haul development.]]></description>
      <pubDate>Mon, 02 Feb 2026 09:33:22 GMT</pubDate>
      <guid>https://trid.trb.org/View/2627383</guid>
    </item>
  </channel>
</rss>