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    <title>Transport Research International Documentation (TRID)</title>
    <link>https://trid.trb.org/</link>
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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>AR- und KI-gestützte Analyse von Schadensentwicklungen in der Bauwerksprüfung</title>
      <link>https://trid.trb.org/View/2603392</link>
      <description><![CDATA[Methoden bildbasierter Verfahren zur Zustandserfassung und -bewertung in Verbindung mit Künstlicher Intelligenz (KI) haben Entwicklungspotenzial, um Bauwerksprüfungsprozesse zu digitalisieren und effizienter zu gestalten. Untersucht werden soll, wie Bildfolgen von Bauwerksschäden, zum Beispiel von Rissen insbesondere an Brückenbauwerken, mittels AR-/MR-fähiger Geräte (beispielsweise Tablet, Smartphone, Brille) anwendergerecht analysiert und visualisiert werden können. Schadensverläufe sollen nach automatisierter Erfassung vor Ort und Lokalisierung im Digitalen Zwilling beispielsweise KI-gestützt chronologisch geordnet, zueinander in Relation gesetzt und unter Einbezug des Faktors Zeit objektiv bewertet werden. Anschließend sollen exemplarische Prognosen zur weiteren Schadensentwicklung gegeben werden können. Durch die automatisierte Auswertung von Schadensentwicklungen können Prüfende messbar unterstützt und zeitlich entlastet werden. Mit der Bildung neuer Datenrelationen und ihrer Implementierung im Digitalen Zwilling erhalten Prüfende einen verbesserten Überblick über relevante Bauwerksinformationen. Bauwerkseigentümern wird ermöglicht, kostenintensive Schadensausweitungen und Folgeschäden frühzeitig zu erkennen, und die Lebenserwartung von Brückenbauwerken genauer einzuschätzen. Auch verkehrsträgerübergreifend ist ein hoher Mehrwert durch das Projekt zu erwarten. ABSTRACT IN ENGLISH: Image-based methods for condition assessment and evaluation in conjunction with artificial intelligence (AI) have potential for digitising building inspection processes and making them more efficient. The aim is to investigate how image sequences of structural damage, for example cracks, particularly in bridge structures, can be analysed and visualised in a user-friendly manner using AR/MR-enabled devices (e.g. tablets, smartphones, glasses). After automated recording on site and localisation in the digital twin, damage progression is to be chronologically ordered, correlated and objectively evaluated, for example with the aid of AI, taking the time factor into account. Subsequently, it should be possible to provide exemplary forecasts for further damage development.]]></description>
      <pubDate>Wed, 24 Sep 2025 10:15:36 GMT</pubDate>
      <guid>https://trid.trb.org/View/2603392</guid>
    </item>
    <item>
      <title>Evaluation zentraler Datenzugangspunkte und -schnittstellen für das Lebenszyklusmanagement von Brücken</title>
      <link>https://trid.trb.org/View/2592044</link>
      <description><![CDATA[Für fundierte und schnellstmögliche Maßnahmenentscheidungen im Lebenszyklus von Brücken müssen Informationen gemeinsam und kombiniert aufbereitet und vorgehalten werden: Die Basis dafür ist ein entsprechendes Datenmanagement. Relevante Grundlage für das Datenmanagement sind zentrale Datenzugangspunkte und möglichst allgemeingültige und offene Schnittstellen für den gesamten Lebenszyklus, die von den Bauwerksverantwortlichen genutzt werden können. Ziel des Forschungsprojekts ist die Zusammenstellung der Anforderungen an die geregelte Verfügbarkeit und den Zugriff auf die Datensätze zu einem Bauwerk/Netzabschnitt. Vorhandene Möglichkeiten und Standards sollen zur Ableitung der Anforderungen zusammengestellt und bewertet werden. Hierdurch sollen die Grundlagen zu einem Datenmanagement hinsichtlich der Anforderungen an Zugangspunkte und Schnittstellen für das Lebenszyklusmanagement für Ingenieurbauwerke geklärt werden. ABSTRACT IN ENGLISH: An appropriate data management is the basis for well-founded and quick decisions on measures to be taken during the life cycle of bridges. For this, information must be prepared and stored jointly and combined. For the entire life cycle, central data access points and interfaces that are as universal and open as possible are required which can be used by those responsible for the structures. The aim of the research project is to compile the requirements for regulated availability and access to data sets for a structure/network section. For this purpose, existing possibilities and standards should be compiled and evaluated. This should clarify the fundamentals of data management with regard to the requirements for data access points and interfaces for the life cycle management of civil engineering structures.]]></description>
      <pubDate>Thu, 30 Oct 2025 08:11:03 GMT</pubDate>
      <guid>https://trid.trb.org/View/2592044</guid>
    </item>
    <item>
      <title>Monitoring und BIM</title>
      <link>https://trid.trb.org/View/2446985</link>
      <description><![CDATA[Zusätzlich zur regelmäßigen und wiederkehrenden Bauwerksprüfung werden bereits viele Bestandsbauwerke mit Sensorik für die Dauerüberwachung ausgestattet. Bislang sind diese Daten selten miteinander vernetzt und die Auswertung der Messergebnisse ist nur eingeschränkt mit der Bauwerksprüfung vor Ort kombinierbar, da es an geeigneter Visualisierung der Daten mangelt. Ziel des Projekts ist es, vorhandene Messdaten der Dauerüberwachung mittels Monitoring-Systemen effektiver zu nutzen und diese mit zugehörigen BIM-Modellen zu verknüpfen. Es soll untersucht werden, inwiefern das digitale Bauwerksmodell bei der Ableitung möglicher Korrelationen zwischen den verschiedenen Monitoring-Daten unterstützen kann. Ein weiteres Ziel ist die visuelle Aufarbeitung der Messergebnisse, welche in Verbindung mit dem zugehörigen BIM-Modell eine intuitive Bewertung der Daten ermöglichen soll. Des Weiteren sollen die aufbereiteten Daten den Prüfenden vor Ort mittels des digitalen Werkzeuges „Augmented Reality“ (AR) zur Verfügung gestellt werden, um bei der Bauwerksdiagnose und Begutachtung vor Ort zu unterstützen. ABSTRACT IN ENGLISH: In addition to structural inspections, many existing structures are already equipped with sensors for long-term monitoring. To date, this data has rarely been networked with each other and the evaluation of the measurement results can only be combined with on-site structural inspections to a limited extent, as there is a lack of suitable visualisation of the data. The aim of the project is to utilise existing measurement data from monitoring systems more effectively and to link these with the associated BIM models. The extent to which the digital model can support the derivation of possible correlations between the various monitoring data is to be investigated. A further aim is the visual processing of the measurement results, which, in conjunction with the associated BIM model, should enable an intuitive evaluation of the data. Furthermore, the processed data is to be made available to inspectors on site using the digital tool ‘Augmented Reality’ (AR) in order to support on-site building diagnosis and assessment.]]></description>
      <pubDate>Mon, 04 Nov 2024 04:38:58 GMT</pubDate>
      <guid>https://trid.trb.org/View/2446985</guid>
    </item>
    <item>
      <title>Umsetzung des Konzepts zum Brückenkompetenzzentrum</title>
      <link>https://trid.trb.org/View/2425020</link>
      <description><![CDATA[Das Brückenkompetenzzentrum soll Fachwissen zu Brücken bündeln, aufbereiten und verfügbar machen; es sollen zukunftsfähige Verfahren aus den Bereichen Brückendiagnostik, Nachrechnung und Bautechnik identifiziert sowie Forschungsthemen und -konzepte erarbeitet und Stakeholder vernetzt werden. Das Brückenkompetenzzentrum wurde im Januar 2023 in die Zielvereinbarung zwischen BMDV und BASt aufgenommen und soll bis zum Ende des 1. Quartals 2025 konzeptionell umgesetzt und etabliert sein. ABSTRACT IN ENGLISH: The Bridge Competence Centre will pool, process and make available specialist knowledge on bridges. It will identify future-proof processes in the fields of bridge diagnostics, recalculation and construction technology, develop research topics and concepts and network stakeholders. The Bridge Competence Centre was included in the target agreement between BMDV and BASt in January 2023 and is to be conceptually implemented and established by the beginning of 2025. ]]></description>
      <pubDate>Mon, 02 Sep 2024 10:46:16 GMT</pubDate>
      <guid>https://trid.trb.org/View/2425020</guid>
    </item>
    <item>
      <title>Konzeption eines GIS-basierten Tools zur Resilienzbewertung von Infrastrukturbauwerken</title>
      <link>https://trid.trb.org/View/2401715</link>
      <description><![CDATA[Im Rahmen des Forschungsprojekts 69.005/2020 „Optimierung und Weiterentwicklung von Handlungshilfen zur Resilienzbewertung der Verkehrsinfrastruktur“ (Bericht siehe 01888227) wurde ein Tool entwickelt, um Infrastrukturbetreiber in die Lage zu versetzen, die Resilienz ihrer Bauwerke untersuchen und bewerten zu können. Des Weiteren liefert das Tool Vorschläge für Maßnahmen, die die Resilienz der untersuchten Bauwerke verbessern. Bislang sind die Informationen zu den Bauwerken und deren Lage im Netz händisch einzutragen, ein automatisiertes Einbinden von Daten aus externen Quellen findet nicht statt. Ziel des aktuellen Projekts ist die Erstellung eines Konzepts für die Entwicklung eines Geographischen Informationssystems (GIS) zur Resilienzbewertung von Infrastrukturbauwerken für die Verkehrsträger Schiene, Straße und Wasserstraße sowie der Entwurf eines Lastenheftes für die IT-technische Umsetzung. Hierfür soll das bestehende Konzept zur Resilienzbewertung durch Funktionen und Module erweitert und für die Überführung in ein GIS vorbereitet werden. Das erweiterte Tool soll gemäß den Standards und Richtlinien des Arbeitskreises Straßenbauwerke (ASB) konzeptioniert sein. Die Umsetzung des Konzepts für die Straße soll in Form eines Demonstrators exemplarisch dargestellt werden. Der entwickelte Demonstrator soll kompatibel zum Bundesinformationssystem Straße (BISStra) sein. ABSTRACT IN ENGLISH: As part of the research project 69.005/2020 "Optimization and further development of guidelines for the resilience assessment of transport infrastructure" (report see 01888227), a tool was developed to enable infrastructure operators to examine and assess the resilience of their structures. The tool also provides suggestions for measures to improve the resilience of the analysed structures. Up to now, the information on the structures and their location in the network has had to be entered manually; there is no automated integration of data from external sources. The aim of the current project is to create a concept for the development of a geographic information system (GIS) for the resilience assessment of infrastructure structures for rail, road and waterway transport and to draft a specification for the IT implementation.To this end, the existing concept for resilience assessment is to be expanded with functions and modules and prepared for transfer to a GIS.The extended tool is to be designed in accordance with the standards and guidelines of the Road Structures Working Group (ASB). The implementation of the concept for the road is to be exemplified in the form of a demonstrator. The developed demonstrator should be compatible with Bundesinformationssystem Strasse (BISStra).
]]></description>
      <pubDate>Tue, 09 Jul 2024 10:36:49 GMT</pubDate>
      <guid>https://trid.trb.org/View/2401715</guid>
    </item>
    <item>
      <title>Zukünftige Entwicklung eines Tools für ein indikatorgestütztes, verkehrsträgerübergreifendes Lebenszyklusmanagement von Infrastrukturbauwerken</title>
      <link>https://trid.trb.org/View/2104604</link>
      <description><![CDATA[Die Investitionen in die Leistungsfähigkeit der bestehenden Verkehrsinfrastruktur gewinnen immer stärker an Bedeutung. Ein effizientes Lebenszyklusmanagement (LZM) der Infrastrukturbauwerke soll dabei helfen, die Lebensdauer der Bauwerke zu erhöhen und die notwendigen Eingriffe in den Verkehr zu minimieren. Wesentliche Kernaufgaben der Zukunft bestehen in der systematischen Modernisierung des Bauwerksbestandes, auch unter Betrieb. Hierbei ist die Abkehr vom aktuellen reaktiven Erhaltungsmanagement hin zu einem integralen, vorrausschauenden und systematischen Vorgehen notwendig. Ziel war es, die aktuelle Datenlage zu eruieren und auf dieser Basis einen IT-Prototypen für eine indikatorengestützte Hilfe zur Maßnahmenentscheidung für das Erhaltungsmanagement von Infrastrukturbauwerken zu entwickeln. Hierfür wurde zunächst der Status Quo bezüglich LZM und Erhaltungsmanagement bei den Verkehrsträgern Schiene, Straße und Wasserstraße mittels Experteninterviews und Literaturrecherche in Erfahrung gebracht. Parallel wurde eine Liste von Indikatoren erstellt. Diese Indikatorenliste diente zur Entwicklung eines Kennzahlensystems mit hierarchischer Übersicht aller Kriterien und zugehörigen Indikatoren. Weiterhin erfolgte eine Überprüfung der vorhandenen Datengrundlagen der drei Verkehrsträger. Hierbei galt es, den Umfang, die Aktualität, die Qualität, die verwendeten Formate sowie die Form der Speicherung und Wartung der Daten zu dokumentieren. In Bezug auf die Ergebnisse der Experteninterviews wurde zudem überprüft, ob und in welchem Umfang die Daten der unterschiedlichen Verkehrsträger kompatibel sind. Die Ergebnisse wurden im Rahmen eines Workshops interessierten Anwendern vorgestellt und mit diesen diskutiert. Auf dieser Basis wurde der IT-Prototyp entwickelt. Dieser Prototyp dient als Basis für die Umsetzung eines indikatorengestützten, verkehrsträgerübergreifenden Lebenszyklusmanagementtools. Dies wiederum ermöglicht den Schritt, weg von reaktivem Bauwerksmanagement, hin zu einem prädiktiven und kognitiven Vorgehen. (A) ABSTRACT IN ENGLISH: Investments in the performance of existing transport infrastructure are becoming increasingly important. An efficient life cycle management (LCM) of transport infrastructure should help to increase the service life of the structures and to minimise the necessary interventions in traffic. Essential core tasks for the future consist in the systematic modernisation of the existing structures, also during operation. In this context, it is necessary to shift away from the current reactive maintenance management towards an integral, forward-looking and systematic approach. The aim was to determine the current data situation and, on this basis, to develop an IT prototype for an indicator-supported aid for deciding on measures for the maintenance management of the transport infrastructure. The status quo with regard to LCM and maintenance management in the transport modes of rail, road and waterway was first ascertained by means of expert interviews and literature research. At the same time, a list of indicators was compiled. This list of indicators was used to develop a system of indicators with a hierarchical overview of all criteria and associated indicators. Furthermore, a review of the existing data basis of the three transport modes was carried out. This involved documenting the scope, timeliness, quality, formats used and the form of storage and maintenance of the data. In relation to the results of the expert interviews, it was also checked whether and to what extent the data of the different modes of transport are compatible. The results were presented to and discussed with interested users in a workshop. The IT prototype was developed on this basis. This prototype serves as the basis for the implementation of an indicator-based, cross-modal life cycle management tool. This enables the shift away from reactive construction management towards a predictive and cognitive approach. (A)]]></description>
      <pubDate>Mon, 30 Jan 2023 08:25:17 GMT</pubDate>
      <guid>https://trid.trb.org/View/2104604</guid>
    </item>
    <item>
      <title>Advanced Auto Planner</title>
      <link>https://trid.trb.org/View/1748942</link>
      <description><![CDATA[Together with our external supplier we will collaboratively design, build and deploy a Minimum Viable Product (MVP) solution that will prove the hypothesis that it will be possible to successfully navigate simulated aircraft across a single airspace sector in Sweden automatically. Every aircraft should be identified by the system at the relevant entry point and safely delivered to their intended exit point according to their flight plan and in the process the system should handle any conflicts correctly and safely without creating additional conflicts or to navigate aircraft into unauthorized airspace. The system should be able to navigate aircraft in a flexible way in order to adjust to the real world environment in a natural manner. The system should also provide the least (common) delay and/or cost impact to the airline and manage the airspace with a manageable (e.g. from a voice radio link perspective) amount of actions.]]></description>
      <pubDate>Mon, 02 Nov 2020 14:10:14 GMT</pubDate>
      <guid>https://trid.trb.org/View/1748942</guid>
    </item>
    <item>
      <title>Machine Learning Powered Roadside Asset Extraction using LiDAR</title>
      <link>https://trid.trb.org/View/1563648</link>
      <description><![CDATA[Traditionally, road assets are monitored and inventory controlled getting direct access to each asset, which can be very time-consuming and requires numerous field recordings and trained personnel. With recent advancements in data collection technologies, vehicle-based data collection platforms can collect millions of data points from all spatial directions at highway speed per second. The big data incorporates LiDAR point clouds, 360o degree imagery, and Laser Crack Measurement Sensor data. This paper presents the development of an innovative advanced machine learning algorithm capable of extracting roadside assets including traffic signs, guardrails, line painting, and rumble strips from the big database. The machine learning process starts with training steps in which hundreds of thousands of training datasets are used and then tested against the testing dataset. Once the testing database has passed at 99% or more, the trained program is ready to detect that asset. The techniques used to train machine learning algorithms to extract signs from the LiDAR database are developed using unsupervised clustering algorithm followed by auto-classification using machine learning classifiers with imagery. A similar approach has been taken to identify other assets such as guardrails, rumble strips, and line paintings. This process has been able to successfully identify and classify traffic signs from highways as well as urban and rural roads. The developed machine learning algorithm is programmed in parallel and performed at typical highway operation speed. Additional information about the geometry and retro-reflectivity properties are other important features that are also calculated and reported by this algorithm. The developed algorithm has been in production phase in the British Columbia Ministry of Transportations Asset Management project, in more than 13000 km of highways and has been able to pass all quality assurance mechanisms. This paper outlines the steps followed to develop a roadside asset extraction machine learning algorithm from the LiDAR and imagery database, as well as present a sample of the resultant roadway traffic sign asset database.]]></description>
      <pubDate>Tue, 16 Oct 2018 14:05:42 GMT</pubDate>
      <guid>https://trid.trb.org/View/1563648</guid>
    </item>
    <item>
      <title>OSIMAB </title>
      <link>https://trid.trb.org/View/1508202</link>
      <description><![CDATA[Im Rahmen einer zunehmenden Digitalisierung stehen bereits heute umfangreiche Daten zur Verfuegung, die im Rahmen neuer und innovativer Ansaetze fuer ein praediktives Lebenszyklusmanagement von Strassenverkehrsinfrastrukturen Verwendung finden koennen. Fuer Brueckenbauwerke koennen zusaetzliche Informationen mittels dauerhafter Strukturueberwachungssysteme gewonnen werden. Insgesamt ermoeglicht dies einen innovativen Ansatz, um Strukturdefizite und Gefahren sowie deren zeitliche Entwicklung fruehzeitig zu erkennen und – wenn noetig – Gegenmassnahmen rechtzeitig zu treffen. Mit dem innovativen Ansatz von OSIMAB wird ein ganzheitliches Konzept fuer die Ueberwachung und Zustandsbewertung von Strassenbruecken verfolgt. Neben der Erkennung und Bewertung des aktuellen Zustands sollen auch Prognosen fuer die weitere Entwicklung aus den am Bauwerk gewonnenen Daten und ihrer zeitlichen Veraenderung in Verbindung mit Vorhersagemodellen erstellt werden. Durch die direkt anschliessende Bewertung potenzieller Risiken und moeglicher Massnahmen zur Aufrechterhaltung und Wiederherstellung der vorgesehenen Zielzuverlaessigkeit, wird mit OSIMAB eine Plattform geschaffen, die ein fruehzeitiges Einleiten von Massnahmen zur Sicherstellung der Verfuegbarkeit von Bauwerken erstmals durch einen ganzheitlichen Ansatz ermoeglicht. So koennen Einschraenkungen des Verkehrsflusses minimiert und erforderliche verkehrliche oder bauliche Massnahmen unmittelbar oder mit ausreichender Vorlaufzeit geplant und umgesetzt werden. ABSTRACT IN ENGLISH: As a consequence of increasing digitalisation, extensive data are available even now which may be used for the predictive life cycle management of road traffic infrastructures using new and innovative approaches. Additional information can be obtained for bridge structures through durable structure monitoring systems. All in all, this permits an innovative approach to recognise structural shortcomings and risks and their temporal development at an early stage and to take any counter-measures which may be necessary in good time. With the innovative approach of OSIMAB, a holistic concept is pursued to monitor and assess the condition of road bridges. In addition to recognising and evaluating the current condition, predictions are to be made for future developments from the data obtained at the structure and their temporal change in connection with prediction models. Through the directly ensuing assessment of potential risks and possible measures to maintain and restore the planned target reliability, a platform is created with OSIMAB which permits an early initiation of measures to safeguard the availability of structures using a holistic approach for the first time. In this way, restrictions to the flow of traffic can be minimised and the requisite traffic and structural measures planned and implemented directly or with an adequate run-up time. ]]></description>
      <pubDate>Tue, 22 May 2018 03:54:42 GMT</pubDate>
      <guid>https://trid.trb.org/View/1508202</guid>
    </item>
    <item>
      <title>Developing the European Road Safety Decision Support System</title>
      <link>https://trid.trb.org/View/1482048</link>
      <description><![CDATA[The main objective of the European co-funded research project SafetyCube (“Safety Causation, Benefits and Efficiency - www.safetycube-project.eu) is the development of an innovative road safety Decision Support System (DSS) to better support evidence-based policy making. The DSS will present the following information for each suggested road safety measure: details of risk factor tackled, measure, best estimate of casualty reduction effectiveness, cost-benefit evaluation and analytic background. The development of the DSS presents a great potential to further support decision making at local, regional, national and international level, aiming to fill in the current gap of comparable measures effectiveness evaluation. In order to provide policy-makers and industry with comprehensive and well-structured information about measures, it is essential that a systems approach is used to ensure the links between risk factors and all relevant safety measures are made fully visible. The DSS is intended to become a major source of information for industry, policy-makers and the wider road safety community.  It will be developed in a form that can readily be incorporated within the existing European Road Safety Observatory of the European Commission DG-MOVE.]]></description>
      <pubDate>Tue, 26 Sep 2017 10:43:49 GMT</pubDate>
      <guid>https://trid.trb.org/View/1482048</guid>
    </item>
    <item>
      <title>Winter Management Systems…: eller finns det andra versioner av Vintermodellen? en översikt över befintliga system</title>
      <link>https://trid.trb.org/View/1347568</link>
      <description><![CDATA[VTI has developed a model called the Winter Model, a strategic tool to make calculations for the winter road maintenance during the whole winter season and how this affects the societies costs. This report contains a summary of other models used in this area. The report mostly contains brief explanations of some of the existing tactical decision support systems. No strategic models which is used as tools for follow-up on consequences depending on actions taken on the road, level of winter service, and on how a different winter seasons climate affects the economics, could be found. Most countries have developed their own tactical decision support system. These systems are in many cases very similar, and they are generally used for the decision of the best maintenance activity to perform. The model that reminds the most of the Swedish Winter Model is the American MDSS-model, which has approximately the same incoming data. The largest difference is that it is more used in the daily activities and not as a tool to make calculations for the winter road maintenance during the whole winter season and how this in turn affects the societies costs.]]></description>
      <pubDate>Fri, 27 Mar 2015 12:05:28 GMT</pubDate>
      <guid>https://trid.trb.org/View/1347568</guid>
    </item>
    <item>
      <title>Bridge Integrated Analysis and Decision Support: Case Histories Phase II</title>
      <link>https://trid.trb.org/View/1239124</link>
      <description><![CDATA[In 2004, the Wisconsin Highway Research Program (WHRP) initiated project # 0092-04-15, entitled, “Bridge Integrated Analysis and Decision Support – Case Histories (Phase I)” at the University of Wisconsin-Milwaukee (UWM) to document the existing knowledge related to Wisconsin bridge incidents in the form of a database of case histories. That study was completed in 2007 where for the database for each case study included detailed information on the bridge, past repair and maintenance, description of the events, reporting, and initial and subsequent responses by the responsible parties, and any resulting remediation. The database entitled “Bridge Incident Response Database” (BIRD) is web-based and searchable with “keywords” and it is detailed in the final report (SPR # 0092-04-15) that was published in January 2008. In January 2008, the WHRP approved this study, “Bridge Integrated Analysis and Decision Support – Phase II,” to enhance the database from the Phase I study (BIRD) and to develop a decision support system (DSS) that could aid the Wisconsin Department of Transportation (WisDOT) personnel in making appropriate decisions in cases of bridge emergencies. The DSS developed under this study, “Bridge Emergency Expert System” (BEES), utilizes rules and procedures in the form of a decision tree that is built based on information from visual inspection at the time of the incident and existing records to recommend appropriate initial actions at the site of the incident. BEES interacts with the user through a “user interface” and makes recommendations on following safety procedures appropriate with the level of damage present in the structure. It should be noted that the recommended actions from the expert system under this study are not based on performing structural analysis or rating of the bridge at the time of the incident. As such, these recommendations should be considered and implemented by experienced staff or in consultation with experienced bridge engineers.]]></description>
      <pubDate>Tue, 19 Feb 2013 15:03:31 GMT</pubDate>
      <guid>https://trid.trb.org/View/1239124</guid>
    </item>
    <item>
      <title>Bridge Integrated Analysis and Decision Support – Phase II</title>
      <link>https://trid.trb.org/View/1127130</link>
      <description><![CDATA[In 2004, the Wisconsin Highway Research Program (WHRP) initiated project # 0092-04-15, entitled, “Bridge Integrated Analysis and Decision Support – Case Histories (Phase I)” at the University of Wisconsin-Milwaukee (UWM) to document the existing knowledge related to Wisconsin bridge incidents in the form of a database of case histories. That study was completed in 2007 where the database for each case study included detailed information on the bridge, past repair and maintenance, description of the events, reporting, and initial and subsequent responses by the responsible parties, and any resulting remediation. The database entitled “Bridge Incident Response Database” (BIRD) is web-based and searchable with “keywords” and it is detailed in the final report (SPR # 0092-04-15) that was published in January 2008. In January 2008, the WHRP approved this study, “Bridge Integrated Analysis and Decision Support – Phase II,” to enhance the database from the Phase I study (BIRD) and to develop a decision support system (DSS) that could aid Wisconsin Department of Transportation (WisDOT) personnel in making appropriate decisions in cases of bridge emergencies. The DSS developed under this study, “Bridge Emergency Expert System” (BEES), utilizes rules and procedures in the form of a decision tree that is built based on information from visual inspection at the time of the incident and existing records to recommend appropriate initial actions at the site of the incident. BEES interacts with the user through a “user interface” and makes recommendations on following safety procedures appropriate with the level of damage present in the structure. It should be noted that the recommended actions from the expert system under this study are not based on performing structural analysis or rating of the bridge at the time of the incident. As such, these recommendations should be considered and implemented by experienced staff or in consultation with experienced bridge engineers.]]></description>
      <pubDate>Wed, 25 Jan 2012 14:22:56 GMT</pubDate>
      <guid>https://trid.trb.org/View/1127130</guid>
    </item>
    <item>
      <title>Pavement management system on the strategic and operative level</title>
      <link>https://trid.trb.org/View/1084723</link>
      <description><![CDATA[Experience gained with the use of pavement management systems (PMS) in Germany as well as observations on their use abroad have shown that road administration often does not identify with results of PMS. Obviously, there is a significant decrease in acceptance of the PMS as decision tool. For more than two years, a new PMS approach has been applied intensely and with great success in Germany. The approach implies the differentiation between two decision levels: strategic and operative. On the strategic level, means of maintenance for achieving certain quality targets are determined in the long planning perspective (the so called "quality scenario"). On the operative level, maintenance programs are concretized for the relevant sub-networks, taking budget restrictions into consideration that have been determined on the strategic level (the so called "budget scenario"). Most of the PM systems used worldwide can be used first of all on the strategic level. However, up to now, there were only few programs available on the operative level. The approach presented in this article has already been successfully applied in several federal states and has thus contributed to a significant increase in acceptance of PMS. For the covering abstract see ITRD E144473.]]></description>
      <pubDate>Wed, 22 Dec 2010 08:46:01 GMT</pubDate>
      <guid>https://trid.trb.org/View/1084723</guid>
    </item>
    <item>
      <title>Heads and tails</title>
      <link>https://trid.trb.org/View/1084628</link>
      <description><![CDATA[In 1999, the US Department of Transportation's Federal Highway Administration (FHWA) initiated a programme to develop a winter road decision support tool called the Maintenance Decision Support System (MDSS). MDSS integrates relevant road weather forecasts, maintenance rules of practice and maintenance resource data to provide road maintenance managers with atmospheric and road weather forecasts, as well as recommending snow ploughing and treatment strategies. The aim is to make the road safe at minimum cost and to reduce the harmful environmental effects of salt and other harsh road chemicals. Currently 41 USA States participate in the program. Version 6.0 of the MDSS software is available from the NCAR MDSS website and is published in open source format and made available to public, private and academic organisations. Four other commercial products derived form the standard NCAR / Federal version are available. A cost benefit ratio study of MDSS was undertaken in the city and county of Denver, Colorado  during two winters covering 56 snow events. MDSS provided a significant benefit for 10 of the 56 snow events, giving a US$100,000 cost saving in labour per winter season. MDSS incorporates tailored weather and pavement models which approximate small-scale weather effects on road systems that usually experience highly variable environmental conditions but the effectiveness of this depends on the availability of road weather conditions across the jurisdiction. The development of the Clarus system, designed to ingest data from road weather sensors from many separate transport networks across North America, is described. Expansions of the existing software to include assessment of the seasonal bearing capacity of roads, multi-state coordination and improved information quality is ongoing.]]></description>
      <pubDate>Wed, 22 Dec 2010 08:42:25 GMT</pubDate>
      <guid>https://trid.trb.org/View/1084628</guid>
    </item>
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