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Titlebook: Digital Railway Infrastructure; Diogo Ribeiro,Pedro Aires Montenegro,Maria D. Mart Book 2024 The Editor(s) (if applicable) and The Author(

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书目名称Digital Railway Infrastructure
编辑Diogo Ribeiro,Pedro Aires Montenegro,Maria D. Mart
视频video
概述Presents advanced simulations for studying the behaviour of railway infrastructure under extreme conditions.Explores the use of AI and augmented reality in monitoring and maintenance of railway infras
丛书名称Digital Innovations in Architecture, Engineering and Construction
图书封面Titlebook: Digital Railway Infrastructure;  Diogo Ribeiro,Pedro Aires Montenegro,Maria D. Mart Book 2024 The Editor(s) (if applicable) and The Author(
描述. ..This book describes the most recent strategies for the digitalization of railway sector that bring new challenges for the construction, operation, and maintenance of railway infrastructures. These strategies involve the development and application of new technologies and methodologies to enhance the sustainability and resilience of railway infrastructures particularly under extreme climate and operational events. In particular, the digital transformation involves the use of digital twins, augmented and virtual reality, forecast capabilities, risk and life-cycle analysis, cybersecurity, unmanned technologies for monitoring and inspection, computer vision, AI, risk and disaster management, among others. Presently, the literature is lacking a contribution that provides general and foundational knowledge across these and other topics. Therefore, this book provides valuable information for researchers, students, and professionals in railway engineering that want to developtheir knowledge and skills in this field..
出版日期Book 2024
关键词Virtual Reality; digital twins; UAVs; railways; BIM; digital technologies; condition monitoring; remote ins
版次1
doihttps://doi.org/10.1007/978-3-031-49589-2
isbn_softcover978-3-031-49591-5
isbn_ebook978-3-031-49589-2Series ISSN 2731-7269 Series E-ISSN 2731-7277
issn_series 2731-7269
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
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Railway Bridges Health Monitoring Supported by Artificial Intelligence,systems. To achieve this goal, an innovative data-driven Artificial Intelligence (AI)-based methodology is proposed, consisting of a combination of time series analysis and advanced multivariate statistical techniques for an unsupervised learning approach. Different combinations of techniques are im
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Condition Monitoring of Rolling Stock Supported by Artificial Intelligence Technique,echnologies. The main goal of this research is to provide an unsupervised method for identifying railway wheel flats. The two-step procedure based on the automatic damage identification algorithm evaluates the acceleration on rails during the passage of traffic loads. The first step consists of asse
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Drive-By Methodologies for Smart Condition Monitoring of Railway Infrastructure,ransportation infrastructure in the last four decades. Sensor-based condition monitoring identifies damage sensitive features from measurements. While the vast majority of condition monitoring research relies on dedicated and stationary sensor positioning, there is an emerging paradigm with so calle
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Digital Twins for Condition Assessment of Railway Infrastructures,se structures are subjected to deterioration processes; therefore, their structural performance tends to decay over time, contributing to risk events occurrence, such as component failure. Thus, the integrity management and condition assessment of railway infrastructure is crucial, requiring prevent
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Augmented Asset Management in Railways,ment in resources, technology, and expertise. Augmented asset management is an emerging concept that leverages advanced technologies such as AI and digitalisation to optimise asset utilisation, reduce costs, improve operational efficiency, enhance and safety. Asset management in railways is increasi
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