Название: Data Analytics for Smart Infrastructure: Asset Management and Network PerformanceАвтор: Yang Wang, Zhidong Li, Bin Liang, Hongda Tian, Ting Guo
Издательство: CRC Press
Год: 2025
Страниц: 203
Язык: английский
Формат: pdf (true), epub
Размер: 31.8 MB
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.
Machine Learning and statistics are not polar opposites. Mathematics is fundamental to statistics, making it a powerful tool for bridging the gap between data and real-world problems. Similarly, Machine Learning acts as another layer, connecting fundamental theory to practical applications. In this way, machine learning also bridges the gap between applications and data. Moreover, Machine Learning often serves as a link between statistics and applications, while statistical techniques remain crucial for data processing. Machine Learning techniques in this process strive for increased agility, efficiency, replicability, capacity, and applicability. These properties of Machine Learning can fill different needs, and they are usually not isolated but overlapped. That is, some Machine Learning techniques will fulfil more than one need with multiple properties displayed.
This groundbreaking work will be essential reading for those studying and practicing analytics in the context of smart infrastructure.
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