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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>
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      <title>Transport Research International Documentation (TRID)</title>
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      <link>https://trid.trb.org/</link>
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    <item>
      <title>Convenience of Running-Gear Health Monitoring Systems to Reduce Unavailability in Metropolitan Railways</title>
      <link>https://trid.trb.org/View/1762381</link>
      <description><![CDATA[Condition monitoring of the running gear of metro trainsets has been receiving increasing interest in recent years due to its potential for cost reduction and safety assurance. In fact, condition-monitoring systems (CMS) may be used (a) for the early identification of component faults (health monitoring), and (b) for in-service load monitoring. This paper describes the part of the research work performed in the SHIFT2RAIL-funded RUN2Rail project addressed to use (a). The focus is on the exploitation of the CMS to reduce the unavailability of trainsets and/or of service disruptions. This fits into the current general trend for railways towards condition-based maintenance (CBM) of assets, which has become a sector-wide need. Various technologies are available or under development for this purpose. It is assumed here that monitoring of health indicators is performed on a number of wheelsets, gearboxes and suspension elements of a case study trainset type of Metro de Madrid’s fleet. The convenience of installing, or not, the systems is assessed based on the observed component failure rates, as input to a bespoke fault-tree analysis (FTA) approach. The probability of occurrence of events that may lead to trainset or service unavailability is taken as top event of the fault tree; it is calculated with different assumptions regarding the probability of detection (POD) of the monitoring systems and the probability that a given component fault will generate unavailability. The corresponding variation of unavailability costs (with/without CMS) is quantified through appropriate penalty costs. The main results are the methodology per se, with indications of which indicators should be monitored as an input to this type of convenience assessment. The method and the results are valuable for metropolitan railway operators in assessing the convenience of adopting condition monitoring systems in their new or existing rolling stock.]]></description>
      <pubDate>Wed, 03 Feb 2021 15:00:45 GMT</pubDate>
      <guid>https://trid.trb.org/View/1762381</guid>
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    <item>
      <title>Using Pavements to Generate Electricity</title>
      <link>https://trid.trb.org/View/1626540</link>
      <description><![CDATA[As part of developing a safe, efficient, and sustainable transportation infrastructure, health monitoring of pavements and bridges, as well as the monitoring of real-time roadway conditions for safety, have become increasingly important. The sensors, data acquisition, and data transmission equipment needed for such monitoring require electricity that is often unavailable, in short supply, unreliable, or a combination of the three. The development of a more robust and capable health and safety monitoring equipment will be greatly facilitated by a more ample, reliable source of electricity than is currently available. This investigation explores the concept of using pavements as thermoelectric generators to produce and supply electricity, using the temperature differential that exists between a pavement’s surface and its underlying layers. A thermoelectric module was placed within a hot-mix asphalt pavement system and a Portland cement concrete pavement system in the laboratory and subjected to full-spectrum lights. The amount of electricity generated was measured, and the results indicated that electricity can be generated. Initial power generation suggests that one square meter pavement surface area may be capable of providing about 62000 microwatts, enough power for 620 wireless transmitters and the accompanying sensor. It is estimated that it may be possible to produce up to 300 W/lane-km.]]></description>
      <pubDate>Tue, 16 Jul 2019 18:04:24 GMT</pubDate>
      <guid>https://trid.trb.org/View/1626540</guid>
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    <item>
      <title>Use of a 3D model to improve the performance of laser-based railway track inspection</title>
      <link>https://trid.trb.org/View/1584326</link>
      <description><![CDATA[In recent decades, 3D reconstruction techniques have been applied in an increasing number of areas such as virtual reality, robot navigation, medical imaging and architectural restoration of cultural relics. Most of the inspection techniques used in railway systems are, however, still implemented on a 2D basis. This is particularly true of track inspection due to its linear nature. Benefiting from the development of sensor technology and constantly improving processors, higher quality 3D model reconstructions are becoming possible which push the technology into more challenging areas. One such advancement is the use of 3D perceptual techniques in railway systems. This paper presents a novel 3D perceptual system, based on a low-cost 2D laser sensor, which has been developed for the detection and characterisation of physical surface defects in railway tracks. An innovative prototype system has been developed to capture and correlate the laser scan data; dedicated 3D data processing procedures have then been developed in the form of three specific defect-detection algorithms (depth gradient, face normal and face-normal gradient) which are applied to the 3D model. The system has been tested with rail samples in the laboratory and at the Long Marston Railway Test Track. The 3D models developed represent the external surface of the samples both laterally (2D slices) and longitudinally (3D model), and common surface defects can be detected and represented in 3D. The results demonstrate the feasibility of applying 3D reconstruction-based inspection techniques to railway systems.]]></description>
      <pubDate>Fri, 22 Feb 2019 17:06:55 GMT</pubDate>
      <guid>https://trid.trb.org/View/1584326</guid>
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    <item>
      <title>Reconstruction of an informative railway wheel defect signal from wheel–rail contact signals measured by multiple wayside sensors</title>
      <link>https://trid.trb.org/View/1576700</link>
      <description><![CDATA[Wheel impact load detectors are widespread railway systems used for measuring the wheel–rail contact force. They usually measure the rail strain and convert it to force in order to detect high impact forces and corresponding detrimental wheels. The measured strain signal can also be used to identify the defect type and its severity. The strain sensors have a limited effective zone that leads to partial observation from the wheels. Therefore, wheel impact load detectors exploit multiple sensors to collect samples from different portions of the wheels. The discrete measurement by multiple sensors provides the magnitude of the force; however, it does not provide the much richer variation pattern of the contact force signal. Therefore, this paper proposes a fusion method to associate the collected samples to their positions over the wheel circumferential coordinate. This process reconstructs an informative signal from the discrete samples collected by multiple sensors. To validate the proposed method, the multiple sensors have been simulated by an ad hoc multibody dynamic software (VI-Rail), and the outputs have been fed to the fusion model. The reconstructed signal represents the contact force and consequently the wheel defect. The obtained results demonstrate considerable similarity between the contact force and the reconstructed defect signal that can be used for further defect identification.]]></description>
      <pubDate>Fri, 25 Jan 2019 10:34:17 GMT</pubDate>
      <guid>https://trid.trb.org/View/1576700</guid>
    </item>
    <item>
      <title>Modeling a Hybrid Pavement Conditions Performance Framework for Botswana District Road Transportation Networks</title>
      <link>https://trid.trb.org/View/1558647</link>
      <description><![CDATA[Road conditions performance modeling is required in order to predict the future conditions and provide information that can be applied to transportation planning, decision making processes and identification of future maintenance interventions. As extension of knowledge in existing gravel road condition models, improved artificial intelligent gravel road performance models which best capture the effects of gravel loss condition influencing factors were developed using feed forward neural network (FFNN) hybrid with a district geographic information system (GIS)-based map using linear referencing approach to display gravel loss conditions as a threshold to trigger optimal maintenance interventions. The developed FFNN gravel loss condition (GVL) prediction model yielded R² = 0.95 > 0.9 benchmark based on minimum MSE = 0.055 < 0.1. Threshold value = 3 (fair condition) was specified on the GIS map for triggering maintenance interventions when gravel road subgrade exposure due to gravel loss is between 10 and 25% as condition monitoring innovative tools.]]></description>
      <pubDate>Mon, 12 Nov 2018 11:17:21 GMT</pubDate>
      <guid>https://trid.trb.org/View/1558647</guid>
    </item>
    <item>
      <title>Fiber Optic Availability and Opportunity Analysis for North American Railroads</title>
      <link>https://trid.trb.org/View/1526421</link>
      <description><![CDATA[The Federal Railroad Administration (FRA) sponsored an evaluation conducted by Transportation Technology Center, Inc. regarding the opportunity and availability to use Fiber Optic Acoustic Detection (FOAD) in the North American railroad industry. FOAD is an emerging technology with the potential to enhance safety in the railroad industry by continuously monitoring the condition of rail, track and rolling stock. A FOAD system pulses laser light down a fiber optic cable buried near a railroad track and using Rayleigh backscatter, can detect acoustic and seismic signals produced by such events as train movement, rail breaks, wheel impacts, dragging equipment, etc. The objective of this project was to determine the viability and applicability of implementing FOAD technology in the North American railroad environment. The FOAD task force, organized by the Association of American Railroads’ (AAR) Railway Electronic Standards Committee (RESC), identified the priority applications for use of FOAD technology to be broken rail detection, train tracking, and monitoring equipment health and track integrity, as well as security and detection of environmental hazards. Additionally, this project identified that there is the potential for cost savings to the railroads, especially if FOAD is used for multiple applications.]]></description>
      <pubDate>Sat, 28 Jul 2018 17:02:21 GMT</pubDate>
      <guid>https://trid.trb.org/View/1526421</guid>
    </item>
    <item>
      <title>Using artificial neural network-self-organising map for data clustering of marine engine condition monitoring applications</title>
      <link>https://trid.trb.org/View/1516362</link>
      <description><![CDATA[Condition monitoring is the process of monitoring parameters expressing machinery condition, interpreting them for the identification of change which could indicate developing faults. Data processing is important in a ship condition monitoring software tool, as misinterpretation of data can significantly affect the accuracy and performance of the predictions made. Data for key performance parameters for a PANAMAX container ship main engine cylinder are clustered using a two-stage approach. Initially, the data is clustered using the artificial neural network (ANN)-self-organising map (SOM) and then the clusters are interclustered using the Euclidean distance metric into groups. The case study results demonstrate the capability of the SOM to monitor the main engine condition by identifying clusters containing data which are diverse compared to data representing normal engine operating conditions. The results obtained can be further expanded for application in diagnostic purposes, identifying faults, their causes and effects to the ship main engine.]]></description>
      <pubDate>Thu, 26 Jul 2018 14:40:39 GMT</pubDate>
      <guid>https://trid.trb.org/View/1516362</guid>
    </item>
    <item>
      <title>Wireless condition monitoring of a marine gearbox</title>
      <link>https://trid.trb.org/View/1499561</link>
      <description><![CDATA[The condition-based maintenance of a marine gearbox poses special challenges because of the inaccessibility to the hull of the ship and harsh environment in the form of higher temperatures, continuous vibrations and salty sea air, which can lead to corrosion. In this article, the integration of a wireless sensor network with a marine gearbox is shown. The integration consists of sensor nodes that record characteristic measurement data, send them actively to a receiving unit and harvest energy from the environment for electrical supply after a one-time installation expenditure. The developed sensor node has a thermoelectric power supply that allows measurement intervals of less than 20 minutes. The recorded vibration data from the gearbox surface are sent via ZigBee wireless technology. By evaluating the envelope spectrum of the measured vibration data, the current rotational speed of the input drive shaft could be identified.]]></description>
      <pubDate>Thu, 31 May 2018 09:37:01 GMT</pubDate>
      <guid>https://trid.trb.org/View/1499561</guid>
    </item>
    <item>
      <title>Optical fibre networks facilitate shift to predictive maintenance</title>
      <link>https://trid.trb.org/View/1485961</link>
      <description><![CDATA[In order to switch from scheduled to condition-based and predictive maintenance, railways are using optical fiber sensing networks (OFSN), as explained in this article. Various sensors are utilized by the OFSN to indicate the health of track-based and train-borne assets. Real-time train and track condition information allows operators to act promptly in case of a fault, thus improving train availability and service quality.]]></description>
      <pubDate>Mon, 23 Oct 2017 13:42:04 GMT</pubDate>
      <guid>https://trid.trb.org/View/1485961</guid>
    </item>
    <item>
      <title>Road Weather Management Benefit Cost Analysis Compendium</title>
      <link>https://trid.trb.org/View/1481006</link>
      <description><![CDATA[The Road Weather Management (RWM) Benefit Cost Analysis (BCA) Compendium provides information about benefit cost analyses conducted around the country for specific RWM technologies or operational strategies. The actual project evaluations involve the use of custom spreadsheets developed by the agency or its contractors, or the application of available software tools to the BCA. The Compendium also includes hypothetical cases designed to demonstrate how BCA can be used for a specific RWM technology or operational strategy. The Federal Highway Administration (FHWA) has developed a sketch planning BCA tool—the Tool for Operations Benefit/Cost (TOPS-BC)—for application to transportation systems management and operations (TSMO) projects, including RWM projects. For the hypothetical cases, TOPS-BC is used to assist in the measurement of benefits and costs and in the calculation of the benefit cost ratio. Each case demonstrates how planners conducted, or could conduct, a BCA on one or more RWM technologies or strategies. There are 27 cases studies presented in the RWM Compendium, and each addresses one or more specific BCA concepts or procedures. Note that the hypothetical BCA case studies and scenarios included in this compendium for several road weather management strategies, including Connected Vehicle applications, utilize assumptions on costs and benefits that do not reflect actual costs and benefits associated with those strategies and are presented only for demonstration purposes.]]></description>
      <pubDate>Tue, 29 Aug 2017 16:39:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/1481006</guid>
    </item>
    <item>
      <title>Fault Detection and Isolation for Electro-Mechanical Actuators Using a Data-Driven Bayesian Classification</title>
      <link>https://trid.trb.org/View/1432753</link>
      <description><![CDATA[This research investigates a novel data-driven approach to condition monitoring of Electrical-Mechanical Actuators (EMAs) consisting of feature extraction and fault classification. The approach is designed to accommodate varying loads and speeds since EMAs typically operate under non-steady conditions. Since many common faults in rotating machinery produce unique frequency components, the approach is based on signal analysis in the frequency domain of both inherent EMA signals and accelerometers.         The feature extraction process exposes fault frequencies in the signal data that are synchronous with motor position through a series of signal processing techniques consisting of digital re-sampling to the position domain, Power Spectral Density (PSD) computation to the frequency domain, and feature reduction. The reduced dimension feature is then used to determine the condition of the EMA with a trained Bayesian Classifier. Signal data collected from EMAs in known health configurations is used to train the algorithms so that the condition of EMAs with unknown health may be predicted.         A passive, linear load test fixture is used to provide a known load (2,400-lbf) on a MOOG industrial MaxForce EMA used for the testing. A seeded fault testing methodology is used to induce known faults in the ball screw and then used as training and validation data for the proposed work. Various desired driving commands are utilized to simulate “real-world” conditions. Laboratory results show that EMA condition can be determined over multiple operating conditions. Although the process was developed for EMAs, it can be used generically on other rotating machine applications as a Health and Usage Management System (HUMS) tool.       ]]></description>
      <pubDate>Thu, 05 Jan 2017 16:25:27 GMT</pubDate>
      <guid>https://trid.trb.org/View/1432753</guid>
    </item>
    <item>
      <title>Improving Road Asset Condition Monitoring</title>
      <link>https://trid.trb.org/View/1414168</link>
      <description><![CDATA[Road networks often carry more than 80% of a country's total passenger-km and over 50% of its freight ton-km according to the World Bank. Efficient maintenance of road networks is highly important. Road asset management, which is essential for maintenance programs, consists of monitoring, assessing and decision making necessary for maintenance, repair and/or replacement. This process is highly dependent on adequate and timely pavement condition data. Current practice for collecting and analysing such data is 99% manual. To optimize this process, research has been performed towards automation. Several methods to automatically detect road assets and pavement conditions are proposed. In this paper, the authors present an analysis of the current state of practice of road asset monitoring, a discussion of the limitations, and a qualitative evaluation of the proposed automation methods found in the literature. The objective of this paper is to understand the issues associated with current processes, and assess the available tools to address these problems. The current state of practice is categorized into: 1) type of data collected; 2) type of asset covered; and 3) amount of information provided. The categories are evaluated in terms of a) accuracy; b) applicability (efficiency); c) cost; and d) overall improvement to current practice. Despite the methods available, the outcome of the study indicates that current condition monitoring lacks efficiency, and none of the methods provide a holistic solution to the problem of road asset condition monitoring.]]></description>
      <pubDate>Wed, 24 Aug 2016 16:53:05 GMT</pubDate>
      <guid>https://trid.trb.org/View/1414168</guid>
    </item>
    <item>
      <title>Capture and Quantification of Deterioration Progression in Concrete Bridge Decks through Periodical NDE Surveys</title>
      <link>https://trid.trb.org/View/1412090</link>
      <description><![CDATA[Monitoring the condition of concrete bridge decks is essential because bridge decks are deteriorating faster than other bridge components. This study concentrated on bridge deck condition assessment using complementary nondestructive evaluation (NDE) techniques. The assessment had three main components: evaluation of the corrosive environment and corrosion processes, concrete degradation evaluation, and assessment with respect to deck delamination. Five NDE techniques were used: impact echo (IE) to detect and characterize delamination, ground-penetrating radar (GPR) to describe the corrosive environment, measurement of the concrete cover and description of its overall condition, half-cell potential (HCP) to assess corrosion activity, ultrasonic surface waves (USW) to describe concrete quality, and electrical resistivity (ER) to estimate corrosion rate. The ability of NDE methods to objectively characterize deterioration progression is illustrated by the results from four NDE surveys of a bridge in Virginia during a period of five and a half years. The results, which include condition maps and condition indices, demonstrate the ability of NDE technologies to accurately and objectively detect and quantify deterioration progression. Results from periodical NDE surveys show a high potential for development of more realistic deterioration and lifecycle cost models for bridge decks.]]></description>
      <pubDate>Wed, 27 Jul 2016 09:49:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/1412090</guid>
    </item>
    <item>
      <title>Measuring and monitoring operational availability of rail infrastructure</title>
      <link>https://trid.trb.org/View/1410762</link>
      <description><![CDATA[In reliability and maintenance engineering, availability can be described as the ability of an item to be in a state to perform a required function at a given time. Availability is commonly given as a measure between zero and one, where one means the probability of an item to be available for use at a given time is 100%. Availability is measured in many areas, such as electronics, information technologies, military equipment, electrical grids and the industry. Various indicators related to availability of railways have been examined by academia and industry. However, there is some ambiguity about how to define and measure the availability of rail infrastructure, given railways' semi-continuous operation, besides data quality issues. This article considers the application of common definitions of availability to rail infrastructure. It includes a case study comparing various approaches for measuring availability. The case study ends with a section on how availability as a function of train frequency and maintenance time can be simulated. The results show rail infrastructure availability correlates well with train delay, but this depends on how infrastructure failure data and outliers are treated.]]></description>
      <pubDate>Thu, 30 Jun 2016 09:17:39 GMT</pubDate>
      <guid>https://trid.trb.org/View/1410762</guid>
    </item>
    <item>
      <title>Processing of collector acceleration data for condition-based monitoring of overhead lines</title>
      <link>https://trid.trb.org/View/1406603</link>
      <description><![CDATA[This paper describes a diagnostic system for overhead line monitoring; it is based on the measurement of collector accelerations, and aimed at improving current scheduled methods for catenary and pantograph maintenance, making condition-based maintenance possible. The proposed setup is inexpensive and easy to install on in-service commercial trains. Meaningful indicators of pantograph/catenary interactions are obtained by the processing and analysis of data on collector accelerations; summarized values are computed in real-time during the train run and compared with alarm limits.]]></description>
      <pubDate>Tue, 24 May 2016 17:11:02 GMT</pubDate>
      <guid>https://trid.trb.org/View/1406603</guid>
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