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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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    <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>Parametric Road and Bridge Design - New Technology for Combined Automated Bridge Generation</title>
      <link>https://trid.trb.org/View/2671531</link>
      <description><![CDATA[Parametric Road and Bridge design - new Technology for combined automated Bridge Generation. Life cycle of road bridges starting from design to construction and finally to asset management is a crucial aspect of digitization of infrastructure planning and maintenance. Germany is a pathfinder for a completely new approach, the generic parametric generation of digital description of digital twins of roads and bridges. In this study, we present a novel method that integrates two state-of-the-art platforms, KorFin for Infrastructure Modelling and Allplan Bridge for Building and Bridge Modelling. By leveraging this integration, we establish a connection between modeling in any life cycle stage in KorFin and the parametric modelling capabilities of Allplan Bridge. This integration is an important outcome of the biggest German AI research projects involving German federal road authorities. This generation had been developed and is being already used in practice. Our research addresses the need for cost-effective infrastructure planning and enhanced construction and maintenance practices. By utilizing the integrated capabilities of KorFin and Allplan Bridge, stakeholders can optimize their decision- making processes, using accurate infrastructure models, and prepare them for infrastructure and especially structure health monitoring. This approach is expected to become a best practice in the infrastructure life cycle management.]]></description>
      <pubDate>Tue, 30 Jun 2026 16:05:36 GMT</pubDate>
      <guid>https://trid.trb.org/View/2671531</guid>
    </item>
    <item>
      <title>Computer Vision Technologies for Cost-Effective Asset Condition Inspections</title>
      <link>https://trid.trb.org/View/2671483</link>
      <description><![CDATA[Minimum Maintenance Standards (MMS) are being implemented in response to government requests for relief from onerous court decisions. To use this statutory defense in court, a municipality must be able to show through documentation that it met the minimum standards, as defined in North American regulatory regimes such as Regulation 239/02 (Canada). Documentation of MMS patrols and record-keeping are critical. IRIS R&D Group Inc. is developing AI and Software technologies to fully automate MMS patrolling activities by relying on AI and computer vision to capture, identify and assess Asset Condition for Roadway Asset Management. IRIS uses computer vision and AI for the detection of deficiencies to support the maintenance of roads, pavement, and roadway assets, such as traffic signs. IRIS offers Automated Road Patrol, Pavement Condition Index (PCI) and Roadway Asset Inventory solutions that were implemented in municipalities such as the City of Brantford, Vaughan and Hamilton in Ontario and other cities in the United States of America. Instantly, IRIS’ data and analytics improved their road safety and legislative compliance results, operational efficiency, and budget allocations. We were awarded the 2022 Smart Cities Connect Smart 50 Award, 2022 IRF Start-up Label award, 2021 John Niedra Better Practices Competition – Innovative Management Practices, Asset Management, Maintenance Management Award, and 2021 GI infraChallenge winner—also recognized by the G20 Summit.]]></description>
      <pubDate>Tue, 30 Jun 2026 16:05:36 GMT</pubDate>
      <guid>https://trid.trb.org/View/2671483</guid>
    </item>
    <item>
      <title>Efficient Pavement Crack Monitoring for Road Life Cycle Management</title>
      <link>https://trid.trb.org/View/2671016</link>
      <description><![CDATA[Road pavements are vital for transportation infrastructure, yet they deteriorate over time due to traffic loads and environmental factors, resulting in cracks and damage. This paper introduces an innovative method for crack detection on road pavements using digital imagery. Our approach incorporates geo-localization, annotates, characterizes, and quantifies crack severity. This empowers experts to monitor crack progression, a critical element in pavement management. The methodology allows for seamless result comparison and augments existing techniques, aiding in condition assessment and conservation strategy determination. Timely detection of cracks enables proactive maintenance, preventing structural degradation, and ensuring user safety and comfort. Leveraging deep learning and open-source frameworks like TensorFlow and QGIS, our approach automates road pavement image analysis and crack identification, providing a cost-effective, accessible solution for crack detection. This research offers significant advantages in resource efficiency and accessibility, especially in areas without regular manual inspections or dedicated vehicles, thereby enhancing road pavement monitoring and maintenance.]]></description>
      <pubDate>Tue, 30 Jun 2026 16:05:36 GMT</pubDate>
      <guid>https://trid.trb.org/View/2671016</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>Rail Defect Detection Using Distributed Acoustic Sensing Technology</title>
      <link>https://trid.trb.org/View/2671792</link>
      <description><![CDATA[In recent years, advances in Distributed Acoustic Sensing (DAS) technology have resulted in significant progress in the detection of vibration sources. However, its use in railway monitoring is still relatively new, even though thousands of kilometers of optical fiber cables are already set up for telecommunication purposes, thus potentially exploitable. In this paper, we explore the possibility of using a DAS system and machine learning tools to detect rail defects along the track. Rail defects are defined as anything other than a smooth rail, and we focus on the detection of rail joints, which are common elements along the track. In this study, measurements were carried out on a short railway section of a few kilometers between two train stations in Paris. The results show that nearly all rail joints along the track are correctly detected, demonstrating the ability of the system to detect these elements with a spatial accuracy of a few meters. Lastly, some future perspectives for the study are proposed, such as a more in-depth analysis of the detected locations or the integration of field information to enhance the reliability of detections.]]></description>
      <pubDate>Tue, 30 Jun 2026 16:05:35 GMT</pubDate>
      <guid>https://trid.trb.org/View/2671792</guid>
    </item>
    <item>
      <title>Using a Motorcycle Probe Vehicle to Provide Infrastructure Information for Powered Two Wheelers</title>
      <link>https://trid.trb.org/View/2580124</link>
      <description><![CDATA[Powered two wheelers are a consistently popular mode of transportation and riding them is a widely practiced recreational activity. However, with regards to safety powered two wheelers are clearly vulnerable road users, for whom accidents of all types yield more severe outcomes on average than for larger vehicles. One way to ensure the safety of powered two-wheeler riders is to identify challenging infrastructure properties, such as transversal evenness qualities or potholes, through the use of probe vehicles. We present the analysis of infrastructure properties through data collected with a motorcycle probe vehicle, previously employed to study human driving dynamics and assess the risk thereof. We present the first steps towards a new standardized evaluation of motorcycle driving dynamics data for this purpose and show that our outcomes can be achieved based on in-vehicle driving dynamics data. This holds the potential to enable the provision of safety relevant data from everyday driven vehicles, which represents the needs of powered two wheelers as much as those of passenger cars or larger vehicle-types and could serve as a template for similar analyses employing smaller probe vehicles like bicycles or (e-)scooters.]]></description>
      <pubDate>Tue, 30 Jun 2026 09:45:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/2580124</guid>
    </item>
    <item>
      <title>Spectral Characterization of the Rail Surface in Urban Environments Using in-Service Vehicles</title>
      <link>https://trid.trb.org/View/2580115</link>
      <description><![CDATA[Rail monitoring using in-service vehicles enables the fast detection of surface defects, which are often responsible for high noise emission. In this paper a processing sequence is presented that converts axle box accelerations into rail condition indicators based on spectral characteristics of the rail surface. The methodology is exemplified with data acquired with a shunter locomotive operating at an inland harbour in the city of Braunschweig, Germany.]]></description>
      <pubDate>Tue, 30 Jun 2026 09:45:14 GMT</pubDate>
      <guid>https://trid.trb.org/View/2580115</guid>
    </item>
    <item>
      <title>D`A Modified Unscaled S-Transform for Seismic Time-Frequency Analysis of Road Detection in Intelligent Transportation Systems</title>
      <link>https://trid.trb.org/View/2617927</link>
      <description><![CDATA[Seismic exploration is an important tool for the detection of road diseases. However, since engineering seismic exploration usually deals with near-surface problems, its detection is complex and difficult. Time-frequency analysis is an important seismic attribute extraction method, which can provide hidden information that is difficult to obtain from seismic profiles, which can effectively help to identify subsurface structures and various types of disease. The S-transform is an important linear time-frequency analysis method, but the window function is fixed during its time-frequency feature extraction, resulting in a shift of the spectrum to higher frequencies, which reduces the accuracy of the time-frequency analysis. The unscaled S-transform, which removes the linear frequency term in the window function, overcomes the above problem to some extent, but affects the temporal resolution of the spectrum in the low-frequency region. To this end, we propose a modified frequency-domain unscaled S-transform method (MFUST) to perform the time-frequency decomposition of seismic signals, and the proposed method adds additional parameters to its window function, which ensures the time-frequency accuracy while realizing the improvement of the spectrum in terms of temporal resolution through the adjustment of the parameters. The effectiveness of the proposed method is verified using synthetic numerical experiments and a real data test.]]></description>
      <pubDate>Tue, 30 Jun 2026 09:45:14 GMT</pubDate>
      <guid>https://trid.trb.org/View/2617927</guid>
    </item>
    <item>
      <title>A Vehicle-Based Sensing Approach for Network-Level Bridge Condition Monitoring</title>
      <link>https://trid.trb.org/View/2671834</link>
      <description><![CDATA[Transport networks rely on well-maintained physical infrastructure to allow them to operate efficiently. Bridges are particularly important, and the failure of a bridge can cause major disruption to the network. Current practices for inspection and monitoring of bridges are slow and expensive, meaning that it is not feasible to constantly monitor the vast quantities of bridges on a transport network. This paper proposes an approach which leverages data measured from in-vehicle sensors to monitor the condition of bridges, without requiring any sensors to be installed on the bridges. The proposed approach uses a machine learning algorithm to account for the influence of varying vehicle speed, and experimental tests show that changes in the structural behavior of a bridge can be detected from measurements taken on the passing vehicle. This approach represents a scalable solution for network-level bridge condition monitoring, which could be extended to account for the effects of various environmental or operational factors which may influence the measured vibrations in a full-scale scenario.]]></description>
      <pubDate>Tue, 30 Jun 2026 09:45:11 GMT</pubDate>
      <guid>https://trid.trb.org/View/2671834</guid>
    </item>
    <item>
      <title>Indirect Monitoring of Frequencies of a Multiple Span Bridge Using Acceleration Responses Collected From a Passenger Train</title>
      <link>https://trid.trb.org/View/2671829</link>
      <description><![CDATA[In this paper, a field study is carried out to monitor the natural frequencies of Malahide viaduct bridge which is located in the north of Dublin. An indirect bridge monitoring approach is employed is this paper, in which the acceleration responses from an instrumented train are used to estimate the natural frequencies of each span of the viaduct. An Ensemble Empirical Mode Decomposition (EEMD)-based Hilbert Huang Transform (HHT) technique is employed to identify the natural frequency of each span from the signals indirectly measured on the train. This is carried out by calculating the average of the Instantaneous Frequencies (IFs) using 41 runs of the instrumented train. To assess the feasibility of the indirect approach, direct monitoring approaches were also implemented using accelerometers attached to the spans of the viaduct. The measurements were carried out in twelve stages. In each stage, a different span was instrumented and monitored using five accelerometers placed on that span. The free and forced vibrations from each span are used to estimate the first natural frequencies. The frequencies obtained from drive-by measurements are compared to those from direct measurements which confirms the effectiveness of indirect approaches and shows the locations of the two replaced spans with higher stiffness that have higher natural frequencies compared to other spans. This full-scale approach expands the potential for applications of bridge–vehicle dynamic interaction responses, along with their ability to be demonstrators of successful implementations of decisions on public infrastructure.]]></description>
      <pubDate>Tue, 30 Jun 2026 09:45:11 GMT</pubDate>
      <guid>https://trid.trb.org/View/2671829</guid>
    </item>
    <item>
      <title>Innovative Condition Monitoring of the Extended Track by Means of LiDAR-Scanner</title>
      <link>https://trid.trb.org/View/2671118</link>
      <description><![CDATA[Track condition monitoring and component assessment is of high importance for the railway system. It forms the basis for maintenance planning and thus enables for guaranteeing a high track quality. A wide variety of technologies are used to describe track condition being continuously improved. The LiDAR (Light Detection And Ranging) technology is not yet a key part of the applied measurement methods but provides great potential. This paper analyses the boundary conditions and system properties of the LiDAR technology. Based on the findings, a potential analysis of the LiDAR technology for evaluating the condition of the extended track and in particular of ditches is carried out. For this purpose, a methodology is developed describing different factors of the extended track. It is further verified with GPR (ground-penetrating radar) data and in-situ observations. The correlation analysis between the GPR data and the LiDAR assessments shows slight correlations only. The observation of condition development over time for specific track sections shows that the LiDAR assessment method provides reliable and plausible results.]]></description>
      <pubDate>Tue, 30 Jun 2026 09:45:10 GMT</pubDate>
      <guid>https://trid.trb.org/View/2671118</guid>
    </item>
    <item>
      <title>SCITSD: A New Structural Curvature Index for Strain Analysis of Large Road Networks</title>
      <link>https://trid.trb.org/View/2671111</link>
      <description><![CDATA[Efficient maintenance of road networks requires data surface, subsurface and structural conditions. One device for providing structural data on network level is the Traffic Speed Deflectometer (TSD). The TSD measures pavement response at traffic speed, using Doppler lasers installed in a semi-trailer. While providing the user with a large amount of information, indices to evaluate the roads in a simpler manner are needed in practice, e.g., for selecting the worst conditioned roads in a network. In this article, a new index named the SCITSD is formally derived from plate bending theory which is proportional to strain in the bottom of the top layer. As opposed to indices such as SCI300 which is developed for stationary instruments, like the Falling Weight Deflectometer, SCITSD also works on asymmetric deflection bowls, which are common due to visco-elastic properties of asphalt. Results from comparisons of back-calculated top layer strain on real-life data with SCITSD are shown to have high agreement. SCITSD is calculated directly on TSD measurements and can therefore be calculated fast. It is therefore highly suitable for helping road authorities select sections of interest and can thereby increase the efficiency of road maintenance.]]></description>
      <pubDate>Tue, 30 Jun 2026 09:45:10 GMT</pubDate>
      <guid>https://trid.trb.org/View/2671111</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>Detection of Rail Surface Defects Based on Axle Box Acceleration Measurements: A Measurement Campaign in Sweden</title>
      <link>https://trid.trb.org/View/2671108</link>
      <description><![CDATA[This work presents the results of a measurement campaign to demonstrate the effectiveness of the axle box acceleration (ABA) technology for detecting rail defects. The measurements were conducted along the Iron Ore line between Sweden and Norway for the IN2TRACK3 project. This line is mostly single-track with passenger-freight mixed traffic and heavy axle load. Historical data and track information data were not considered in this study. By analyzing data acquired from the accelerometers in vertical and longitudinal directions, rail defects were detected in near real-time using big-data analytics. For our validated sections, 100% of rail defects (including squats) were detected using time-frequency analysis and an outlier detection approach. The methodology also allows for identifying priority locations, e.g., defective welds, joints, transition zones, etc., and its use for prescriptive maintenance recommendations is being explored in the framework of the IAM4RAIL project.]]></description>
      <pubDate>Tue, 30 Jun 2026 09:45:10 GMT</pubDate>
      <guid>https://trid.trb.org/View/2671108</guid>
    </item>
    <item>
      <title>ML-Based Model for the Estimation of the Pavement Elastic Modulus via Deflection Velocity Measurements</title>
      <link>https://trid.trb.org/View/2671097</link>
      <description><![CDATA[Nowadays, the urgency to appraise the structural deteriorating condition of road pavements and ascertain their remaining operational lifespan is more paramount than ever. The limitations of conventional computational methods and measurement techniques such as the Falling Weight Deflectometer (FWD) have paved the way for the emergence of the Traffic Speed Deflectometer (TSD), enabling continuous bearing capacity evaluation without traffic disruption. Given the large amount of data generated by TSD, this paper introduces an innovative Machine Learning (ML) based model for the estimation of the pavement elastic modulus (E1\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$E_1$$\end{document}) using vertical deflection velocity (Dv\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$D_v$$\end{document}) measurement. The research formulates a robust estimation model by employing Support Vector Machine (SVM) techniques, that subsequently validated through rigorous performance metrics. This research significantly advances pavement assessment by offering promising data-driven approaches and ML prospects for monitoring road durability and safety.]]></description>
      <pubDate>Tue, 30 Jun 2026 09:45:10 GMT</pubDate>
      <guid>https://trid.trb.org/View/2671097</guid>
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