Data Analytics for Smart Infrastructure: Asset Management and Network Performance

Wang, Yang/ Li, Zhidong/ Liang, Bin/ Tian, Hongda/ Guo, Ting

ISBN 10: 1032754168 / ISBN 13: 9781032754161

Edité par CRC Pr I Llc, 2025

A propos de cet article

184 pages. 9.19x6.13 inches. In Stock. This item is printed on demand. N° de réf. du vendeur __1032754168

Détails bibliographiques

Titre : Data Analytics for Smart Infrastructure: ...

Éditeur : CRC Pr I Llc

Date d'édition : 2025

Reliure : Hardcover

Etat : Brand New

A propos de ce titre

Synopsis :

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.

À propos de l?auteur:

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.