Data Analytics for Smart Infrastructure
CRC Press (Verlag)
978-1-032-75416-1 (ISBN)
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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.
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.
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
| Erscheint lt. Verlag | 31.1.2025 |
|---|---|
| Zusatzinfo | 23 Tables, black and white; 68 Line drawings, black and white; 13 Halftones, black and white; 81 Illustrations, black and white |
| Verlagsort | London |
| Sprache | englisch |
| Maße | 156 x 234 mm |
| Themenwelt | Informatik ► Datenbanken ► Data Warehouse / Data Mining |
| Mathematik / Informatik ► Mathematik ► Angewandte Mathematik | |
| ISBN-10 | 1-032-75416-8 / 1032754168 |
| ISBN-13 | 978-1-032-75416-1 / 9781032754161 |
| Zustand | Neuware |
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