Data Analytics for Smart Infrastructure
Asset Management and Network Performance
1. Auflage
Erschienen am 31. Januar 2025
Buch
Softcover
184 Seiten
978-1-032-75415-4 (ISBN)
Beschreibung
This book presents, for the first time, data analytics for smart infrastructures. The authors draw on over a decade's experience working with industry and demonstrating the capabilities of data analytics for infrastructure and asset management.
The volume gives data-driven solutions to cover critical capabilities for infrastructure and asset management across three domains: 1) situation awareness 2) predictive analytics and 3) decision support. The reader will gain from various data analytic techniques including anomaly detection, performance evaluation, failure prediction, trend analysis, asset prioritization, smart sensing and real-time/online systems. These data analytic techniques are vital to solving problems in infrastructure and asset management. The reader will benefit from case studies drawn from critical infrastructures such as water management, structural health monitoring and rail networks.
This groundbreaking work will be essential reading for those studying and practicing analytics in the context of smart infrastructure.
The volume gives data-driven solutions to cover critical capabilities for infrastructure and asset management across three domains: 1) situation awareness 2) predictive analytics and 3) decision support. The reader will gain from various data analytic techniques including anomaly detection, performance evaluation, failure prediction, trend analysis, asset prioritization, smart sensing and real-time/online systems. These data analytic techniques are vital to solving problems in infrastructure and asset management. The reader will benefit from case studies drawn from critical infrastructures such as water management, structural health monitoring and rail networks.
This groundbreaking work will be essential reading for those studying and practicing analytics in the context of smart infrastructure.
Rezensionen / Stimmen
" "Data Analytics for Smart Infrastructure" is an indispensable resource for researchers, engineers, and even asset managers. The book's focus on data analytics for smart infrastructure will be of significant benefit to professionals seeking to expand their knowledge and skills in the use of analytics techniques. "-Professor Changsheng Xu, Chinese Academy of Sciences
" "Data Science for Smart Infrastructure" is an indispensable resource that empowers professionals to harness the power of data analytics in infrastructure and asset management practices. With insightful case studies, this book showcases the transformative potential of data-driven solutions in optimizing infrastructure operations and management."
-Professor Dikai Liu, University of Technology Sydney
This book is essential for navigating the complexities of modern infrastructure management. With a focus on real-world applications and advanced techniques, it equips readers with the knowledge to optimise infrastructure and asset management through data analytics. A must-read for anyone seeking to harness the power of data for critical infrastructure systems.
-Professor Lei Wang, University of Wollongong
The book is a comprehensive exploration of data analytics for smart infrastructure, offering invaluable insights into enhancing situation awareness, predictive analytics, and decision support. Covering a range of data-driven solutions with practical applications, from service gap identification to real-time system implementation, it's an indispensable resource for professionals and researchers.
-Professor Zhiyong Wang, University of Sydney
Weitere Details
Sprache
Englisch
Verlagsort
London
Großbritannien
Verlagsgruppe
Taylor & Francis Ltd
Zielgruppe
Für höhere Schule und Studium
Für Beruf und Forschung
Professional Practice & Development, Professional Reference, and Undergraduate Advanced
Illustrationen
81 s/w Abbildungen, 13 s/w Photographien bzw. Rasterbilder, 68 s/w Zeichnungen, 23 s/w Tabellen
23 Tables, black and white; 68 Line drawings, black and white; 13 Halftones, black and white; 81 Illustrations, black and white
Maße
Höhe: 234 mm
Breite: 156 mm
Dicke: 11 mm
Gewicht
319 gr
ISBN-13
978-1-032-75415-4 (9781032754154)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Klassifikation
Weitere Ausgaben
Andere Ausgaben
Yang Wang | Zhidong Li | Bin Liang
Data Analytics for Smart Infrastructure
Asset Management and Network Performance
E-Book
01/2025
1. Auflage
CRC Press
68,49 €
Als Download verfügbar
Yang Wang | Zhidong Li | Bin Liang
Data Analytics for Smart Infrastructure
Asset Management and Network Performance
E-Book
01/2025
1. Auflage
CRC Press
68,49 €
Als Download verfügbar
Yang Wang | Zhidong Li | Bin Liang
Data Analytics for Smart Infrastructure
Asset Management and Network Performance
Buch
01/2025
1. Auflage
CRC Press
96,70 €
Versand in 10-20 Tagen
Personen
Yang Wang is a professor at UTS Data Science Institute, leading advanced data analytics for smart infrastructure. Yang keeps actively engaged with industry partners and delivers innovative data-driven solutions for critical infrastructures including supply water and transport network, structural health monitoring, etc. Yang has received various research and innovation awards including Eureka Prize, iAwards, and AWA water awards.
Associate Professor Zhidong Li at UTS is an award-winning expert in data science and machine learning, with a notable tenure at Data61, CSIRO, and a history of significant contributions to translate machine learning into industrial fields, including infrastructure, finance, environment, and agriculture.
Ting Guo is a senior research fellow in the Data Science Institute at UTS. He has years of experience in collaborative research with industry partners in infrastructure failure prediction and proactive maintenance. His research interests include deep learning, graph learning and data mining.
Bin Liang, a senior lecturer at UTS, is an accomplished data scientist with extensive industry and research experience. With publications in top venues and successful industry project deliverables, his expertise in data analytics, AI, and computer vision has driven significant academic, social, and economic advancements.
Hongda Tian is a research and innovation focused Senior Lecturer at the UTS Data Science Institute. By leveraging the power of artificial intelligence, he has been focusing on research translation through working with government and industry partners and providing data-driven solutions to real-world problems.
Professor Fang Chen is the Executive Director at the UTS Data Science Institute. She is an award-winning, internationally recognised leader in AI and data science, having won the Australian Museum Eureka Prize 2018 for Excellence in Data Science, NSW Premier's Prize of Science and Engineering, and the Australia and New Zealand "Women in AI" Award in Infrastructure in 2021. Her extensive expertise is centered around developing data-driven innovations that address complex challenges across large-scale networks in different industry sectors.
Associate Professor Zhidong Li at UTS is an award-winning expert in data science and machine learning, with a notable tenure at Data61, CSIRO, and a history of significant contributions to translate machine learning into industrial fields, including infrastructure, finance, environment, and agriculture.
Ting Guo is a senior research fellow in the Data Science Institute at UTS. He has years of experience in collaborative research with industry partners in infrastructure failure prediction and proactive maintenance. His research interests include deep learning, graph learning and data mining.
Bin Liang, a senior lecturer at UTS, is an accomplished data scientist with extensive industry and research experience. With publications in top venues and successful industry project deliverables, his expertise in data analytics, AI, and computer vision has driven significant academic, social, and economic advancements.
Hongda Tian is a research and innovation focused Senior Lecturer at the UTS Data Science Institute. By leveraging the power of artificial intelligence, he has been focusing on research translation through working with government and industry partners and providing data-driven solutions to real-world problems.
Professor Fang Chen is the Executive Director at the UTS Data Science Institute. She is an award-winning, internationally recognised leader in AI and data science, having won the Australian Museum Eureka Prize 2018 for Excellence in Data Science, NSW Premier's Prize of Science and Engineering, and the Australia and New Zealand "Women in AI" Award in Infrastructure in 2021. Her extensive expertise is centered around developing data-driven innovations that address complex challenges across large-scale networks in different industry sectors.
Inhalt
1. AI Empowering Infrastructure: the Road to Smartness 2. Asset anomaly identification - damage detection in structural health monitoring 3. Network performance evaluation - Delay Propagation on Large Scale Railway Systems 4. Network Status Monitoring - Signal Aspect Detection for Railway Networks 5. Underground Vessel: Water Pipe Failure Prediction 6. Long-Term Prediction of Water Supply Networks Condition 7. Service Demand Prediction - passenger flow 8. Prioritising Risk Assets for Infrastructure Maintenance 9. Adapting dynamic behavior evolution in structural health monitoring 10. Smart Sensing and Preventative Maintenance